JP2026019798A - system - Google Patents

Abstract

A system is provided.
[Solution]
means for receiving any issues or problems input by a user;
A means of analyzing received issues and problems;
A means of generating solutions from different cultural backgrounds and disciplines based on the analysis results;
means for presenting the generated solution to a user;
A system including:
[Selected Figure] Figure 1

Description

The technology disclosed herein relates to a system.

Patent document 1 discloses a persona chatbot control method executed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Japanese Patent Application Laid-Open No. 2022-180282

In modern society, many challenges and problems require different solutions depending on the field of expertise and cultural background. However, existing systems often only provide approaches limited to a single perspective, making it difficult to obtain multifaceted solutions. This makes it difficult for users to find new perspectives and ideas. The problem that this invention aims to solve is to provide a system that allows users to obtain optimal solutions from a variety of perspectives.

The present invention provides a system that includes the following means: means for receiving any task or problem input by a user; means for analyzing the received task or problem; means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results; and means for presenting the generated solutions to the user. It is also possible to include means for generating solutions using multiple AI models based on the analysis results, and means for integrating the generated solutions and formatting them before presenting them to the user. This allows the user to obtain optimal solutions from a variety of perspectives.

"User" refers to a person or organization that inputs issues or problems into the system.

"Any issue or problem" refers to any type of question or problem that a user enters into the system for a solution.

"Means for receiving" refers to the interface or function that allows the system to receive issues or problems entered by the user.

"Means of analysis" refers to the function of analyzing received issues and problems using natural language processing or other technologies and extracting relevant information and elements.

"Different cultural backgrounds" refers to knowledge and experiences in cultural spheres and communities that are geographically, historically, and socially different.

"Field of expertise" refers to specialized knowledge and skills in a particular academic field, technology, or industry.

"Means for generating solutions" refers to the function of using AI models and other technologies to create answers and proposals for issues and problems based on the analysis results.

"Generated solutions" refers to solutions or suggestions created by the system and provided to the user.

"Presentation means" refers to the interface or method used to display and inform the user of the solution generated by the system.

An "AI model" refers to an algorithm or program that uses artificial intelligence technology to generate solutions to specific challenges or problems.

"Integration means" refers to the functions and methods for organizing multiple solutions into a single, coherent format.

"Formatting methods" refers to the functions and methods used to format the integrated solution in a way that is easy for users to understand.

1 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a first embodiment. 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a second embodiment. FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a third embodiment. FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. 1 shows an emotion map onto which multiple emotions are mapped. 1 shows an emotion map onto which multiple emotions are mapped. FIG. 2 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in application example 1. FIG. 10 is a sequence diagram showing the flow of processing of the data processing system in the second embodiment when an emotion engine is combined. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined.

Below, an example of an embodiment of a system relating to the technology disclosed herein will be described with reference to the accompanying drawings.

First, let me explain the terminology used in the following explanation.

In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, a processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose Computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

In the following embodiments, coded random access memory (RAM) is memory in which information is temporarily stored and is used by the processor as work memory.

In the following embodiments, the coded storage refers to one or more non-volatile storage devices that store various programs, parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

In the following embodiments, a communication I/F (Interface) with a symbol is an interface that includes a communication processor, an antenna, etc. The communication I/F controls communication between multiple computers. Examples of communication standards that can be applied to the communication I/F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

In the following embodiments, "A and/or B" is synonymous with "at least one of A and B." In other words, "A and/or B" means that it may be just A, just B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and/or B" also applies when three or more things are expressed connected by "and/or."

[First embodiment]

Figure 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

The reception device 38 is equipped with a touch panel 38A, a microphone 38B, etc., and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or finger) to receive user input via the indicator. The microphone 38B detects the user's voice to receive user input via voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

The output device 40 is equipped with a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible by the user 20 (e.g., audio and/or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54.

Figure 2 shows an example of the main functions of the data processing device 12 and smart device 14.

As shown in FIG. 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

The present invention is a system that provides multifaceted solutions to issues and problems entered by users. This system works through a process of receiving issues, analyzing them, generating solutions from different cultural backgrounds and areas of expertise, and presenting them to the user.

User assignment entry

Users enter the issues or problems they want to solve through an interface on their web browser. The information entered by the user is sent to the server as an issue.

Receive and save assignments

The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

Analysis of the problem

The server analyzes the received assignment using a natural language processing (NLP) engine. This analysis process extracts the category and related keywords of the input assignment. For example, if the input assignment is "Looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Generating solutions

The server utilizes multiple AI models based on the analysis results to generate solutions from multiple perspectives. Specifically, it generates solutions using a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Integrating and presenting solutions

The server then integrates the generated solutions and formats them before presenting them to the user. The integrated solution is displayed to the user through a web browser interface, allowing the user to view solutions from a variety of perspectives.

Specific examples

For example, here is a specific example of when the task entered is "I'm looking for new methods for sustainable agriculture."

The user enters a problem, which is received and saved by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The server generates solutions based on the analysis results, utilizing a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," and the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server integrates these solutions and presents them to the user in the optimal format. Ultimately, the user is able to obtain solutions such as "Sustainable methods inspired by African agricultural technology" and "Examples of the application of vertical farming in urban areas."

The system of the present invention allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling effective problem-solving.

The processing flow is explained below.

Step 1:

Users enter the issue or problem they want to solve through a web browser interface. By entering the issue in the input field and clicking the submit button, the issue is sent to the system.

Step 2:

The server receives assignments sent by users. The received assignments are temporarily stored in a database. This storage process is for later analysis.

Step 3:

The server invokes a natural language processing (NLP) engine to analyze the received and saved assignments. This analysis process extracts the assignment's category and related keywords. For example, for the assignment "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Step 4:

The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. As a specific example, a multicultural AI model and a multi-perspective AI model are used. The multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Step 5:

The server then combines the generated solutions into a consistent format. During this process, each solution is modified as needed and formatted for easy viewing.

Step 6:

The server presents the prepared solutions to the user through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Step 7:

Users can refer to the presented solutions and use them to solve their own problems as needed. This allows users to gain new perspectives and ideas.

The system of the present invention provides users with a variety of solutions through these steps, supporting effective problem-solving.

(Example 1)

Next, we will explain Example 1. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

In today's increasingly diverse society, the challenges and problems users face are complex, making it difficult to provide effective solutions from a single perspective or approach. Furthermore, integrating knowledge from different cultural backgrounds and fields of expertise requires collaboration and cooperation among people with expertise in each field. However, the systems and methods that enable this are not well established, making it difficult for users to quickly obtain multifaceted solutions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for receiving any tasks or problems input by the user, means for temporarily storing the received tasks or problems in a database, means for analyzing the stored tasks or problems using a natural language processing engine and extracting categories and related keywords for the input tasks, means for utilizing multiple AI models to generate solutions from different cultural backgrounds and fields of expertise based on the analysis results, means for integrating the multiple generated solutions and formatting them before presenting them to the user, and means for displaying the formatted solutions to the user. This enables users to quickly obtain multifaceted and effective solutions.

"User" refers to the entity that uses the system to input issues and problems and receive solutions.

"Server" refers to a computer system that receives input from users, analyzes it, and generates and presents solutions.

"Challenges and problems" refer to specific situations or difficult circumstances that users are seeking a solution to.

"Means of receiving" refers to the function by which the server acquires and stores information entered by the user.

A "database" refers to an information storage system for temporarily storing and managing data such as issues and problems.

A "natural language processing engine" is a software module that analyzes human language and extracts categories and keywords for input issues or problems.

"Means of analysis" refers to the function of using a natural language processing engine to analyze received issues and problems and extract relevant information.

"AI model" refers to a model trained using artificial intelligence algorithms to solve a specific problem.

A "multicultural AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different cultural backgrounds.

A "multi-perspective AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different fields of expertise.

"Means for generating solutions" refers to the function of using multicultural AI models and multi-perspective AI models to generate specific solutions to problems.

"Means of integration" refers to the function for combining and shaping multiple generated solutions into one.

"Formatting tools" refers to the ability to adjust the format and appearance of a solution before presenting it to the user.

"Means of display" refers to the function of presenting the formatted solution to the user in an easy-to-read format.

The present invention relates to a system for providing multifaceted solutions to issues and problems entered by users. This system receives issues entered by users, stores them in a database, analyzes them using a natural language processing (NLP) engine, and generates solutions using multiple AI models based on the analysis results. It also integrates the generated solutions, formats them, and presents them to the user.

Users enter the problem they want to solve through a web browser. For example, they could enter a specific prompt such as, "I'm looking for new methods for sustainable agriculture."

Once the input is complete, the server receives it and temporarily stores it in the assignment database. It is recommended that a common relational database such as MySQL or PostgreSQL be used for this database. The saved assignment data is also assigned metadata such as a timestamp and user ID.

The server then retrieves the saved task data and analyzes it using an NLP engine. The NLP engine can use natural language processing libraries such as SpaCy or NLTK. Analysis steps include tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, for the task "Looking for new methods for sustainable agriculture," results such as "Category: Agriculture" and "Keywords: Sustainability, New Methods" can be obtained.

Based on the analysis results, the server generates solutions using a multicultural AI model and a multi-perspective AI model. These AI models provide solutions from different cultural backgrounds and areas of expertise. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

The generated solutions are then integrated and formatted by the server, which includes merging and formatting the text. The formatted solutions are then presented to the user through a web browser.

This allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling more effective problem-solving. For example, users can obtain specific solutions such as "sustainable methods based on African agricultural techniques" or "examples of applying vertical farming in urban areas."

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1: User enters assignment

Users enter the issues or problems they wish to solve through a web browser interface. For example, they might enter, "Please provide us with some innovative ideas for managing green spaces in urban areas." Once the information is complete, it is sent to the server via an HTML form.

Input: The prompt text entered by the user

Output: User input data is sent to the server.

Step 2: The server receives and stores the assignment.

The server receives HTTP requests and retrieves the assignment data submitted by the user. The retrieved data is temporarily stored in a relational database such as MySQL or PostgreSQL. Metadata such as timestamps and user IDs are also added to the data for later analysis.

Input: Issue data from the user

Output: Issue data stored in the database

Step 3: The server analyzes the issue.

The server retrieves the saved assignment data and analyzes it using an NLP engine such as SpaCy or NLTK. This analysis process includes tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, if the assignment "Looking for new methods for sustainable agriculture" is input, the NLP engine will extract "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Input: Issue data retrieved from the database

Output: Category and keyword information

Step 4: The server generates a solution.

The server uses a multicultural AI model and a multi-perspective AI model to generate solutions based on the analysis results. The analysis results are input as prompts into the multicultural AI model, which generates solutions from different cultural backgrounds. For example, it generates "Examples of green space management in European urban planning." The same analysis results are input as prompts into the multi-perspective AI model, which generates solutions based on specialized fields. For example, it generates "New methods for managing green spaces in urban areas using technology."

Input: Analysis results (category and keyword information)

Output: Multiple solutions

Step 5: The server integrates and presents the solution.

The server then integrates and formats the generated solutions. Specifically, it merges the solution text and formats it into a visually easy-to-read form. The formatted solution is then saved back into the database and displayed to the user via a web browser. The user can view multiple solutions and consider approaches to solving the problem from multiple perspectives.

Input: Multiple Solutions

Output: Consolidated and formatted solution

These are the main processing steps of this system. This allows users to quickly obtain solutions from a variety of perspectives, enabling more effective problem-solving.

(Application Example 1)

Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Existing solution-providing systems rely on a single cultural background or field of expertise, making it difficult to obtain solutions from diverse perspectives. They also lack the functionality to effectively analyze users' issues, extract related keywords, and generate and present solutions from multiple cultural and perspectives based on those keywords. For this reason, there is a need for systems that can quickly provide optimal solutions to users' problems.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes: means for receiving any task or problem entered by the user; means for analyzing the received task or problem; means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results; means for presenting the generated solution to the user; means including a natural language processing engine for analyzing the task entered by the user and extracting specific keywords; means for utilizing multiple artificial intelligence models to generate solutions from multicultural and multi-perspectives; means for integrating the generated solutions and optimally formatting them; and means for displaying the integrated solution on a display device. This allows the user to gain new perspectives from a variety of points of view, enabling them to quickly obtain multifaceted solutions.

Word definition

"User" refers to a person who uses the system to input issues and problems.

"Issue" refers to a problem or question that a user enters into the system and needs to be solved.

A "natural language processing engine" refers to a software component that analyzes input text and extracts specific keywords.

"Cultural background" refers to the collection of history, customs, values, etc. that exist in different regions and countries.

A "specialty" refers to an area that requires concentrated specific knowledge or skills.

"Solution" refers to specific measures or proposals provided to address users' challenges or problems.

An "artificial intelligence model" refers to an algorithm or system designed to perform a specific task based on training data.

"Format" refers to the standards and rules for arranging information in a particular form or structure.

"Display device" refers to a device for visually presenting the generated solution to the user.

A "multicultural perspective" refers to an approach to solving problems from the perspectives of various cultures.

"Multiple perspectives" refers to an approach that looks at a single issue from multiple different angles or perspectives.

Form for implementing the invention

The present invention is a system that provides multifaceted solutions to issues and problems entered by users. This system includes a process that receives and analyzes issues entered by users via devices such as smartphones, and generates and provides solutions from multiple cultural and perspectives to the user.

System Program

The system consists of the following components:

1. User device: A device such as a smartphone or tablet. Equipped with an interface for users to input tasks.

2. Server: Installed in a cloud environment, it performs the primary processing of analyzing received challenges and generating solutions. Specifically, it includes a database, a natural language processing engine, multiple artificial intelligence models, and software components for integrating and formatting solutions.

3. Display device: Present the solution visually on the user's terminal or other device.

Program processing explanation

The server receives assignments sent from user devices and temporarily stores them in a database. It then uses a natural language processing engine (e.g., the transformers library) to analyze the received assignments and extract specific keywords. This analysis process clarifies the assignment's category and related keywords.

Based on the analysis results, the server utilizes multiple artificial intelligence models to generate solutions from a multicultural and multi-perspective perspective. For example, one model generates solutions from a "multicultural perspective" and another model generates solutions from a "multi-perspective" perspective.

The generated solutions are then integrated and optimally formatted by the server. The formatted solution is then sent to the user's terminal's display device and presented to the user.

Specific examples

For example, if the problem "Sales are sluggish" is entered, a solution will be generated using the following steps:

Step 1:

The problem "Sales are sluggish" is entered from the user's device.

Step 2:

The server receives this challenge and analyzes it using a natural language processing engine. As a result of the analysis, the keywords "renewal case studies," "promotion," "display improvement," and "online marketing" are extracted.

Step 3:

The server generates solutions using multiple artificial intelligence models. For example, it generates "success stories in Japan" and "overseas promotion strategies" from a multicultural perspective, and "ways to improve displays" and "techniques for utilizing online marketing" from multiple perspectives.

Step 4:

These solutions are integrated, formatted optimally, and presented on the user's terminal display device.

Example prompt:

A user has entered the issue of "slumping sales." The following keywords have been extracted: "renewal case study," "promotion," "display improvement," and "online marketing." Based on this, please propose a solution from a multicultural and multi-perspective perspective.

In this way, the system of the present invention allows users to gain new perspectives from a variety of viewpoints and quickly obtain multifaceted solutions. This makes it possible to provide effective solutions to the problems users face.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Program processing steps

Step 1: User inputs assignment

Input: Users enter tasks using their smartphones or tablets.

Operation: The user's device enters the assignment through a web browser interface and sends the assignment to the system.

Output: The submitted assignment arrives at the server.

Step 2: Receive and save the assignment

Input: Assignment submitted from the user's device.

How it works: The server receives assignments submitted by users and temporarily stores them in a database.

Output: Issues saved in the database.

Step 3: Analyze the issue

Input: Issues saved in the database.

How it works: The server uses a natural language processing engine (e.g., the transformers library) to parse the incoming assignment. The parsing process extracts the assignment's category and associated keywords.

Output: Extracted keywords and categories.

Step 4: Generating Solutions - From a Multicultural Perspective

Input: Extracted keywords and categories.

How it works: The server uses an artificial intelligence model to generate solutions from a multicultural perspective. Based on keywords, it generates solutions from a multicultural perspective.

Output: Solutions from a multicultural perspective.

Step 5: Generate solutions - from multiple perspectives

Input: Extracted keywords and categories.

How it works: The server uses an artificial intelligence model to generate solutions from multiple perspectives. Based on keywords, it generates solutions from multiple perspectives.

Output: A solution from multiple perspectives.

Step 6: Integrate and format the solution

Input: Multicultural and multi-perspective solutions.

How it works: The server combines the multiple solutions generated and formats them into the most optimal format.

Output: Integrated solution.

Step 7: Present an integrated solution

Input: Integrated solution.

Operation: The server sends the integrated solution to the user's device. The user's device's display device visually displays it.

Output: The solution presented to the user.

Specific examples of behavior

Step 1:

The user enters the issue of "poor sales" into a smartphone app.

Step 2:

The user's device sends this assignment data to the server, which receives it and stores it in a database.

Step 3:

The server launches a natural language processing engine, analyzes the problem text, and extracts keywords such as "renewal case study," "promotion," "display improvement," and "online marketing."

Step 4:

The server runs an artificial intelligence model using the extracted keywords to generate solutions from a multicultural perspective, generating "success stories in Japan" and "overseas promotion strategies."

Step 5:

The server runs another artificial intelligence model using the extracted keywords to generate solutions from multiple perspectives, generating "ways to improve display" and "techniques for utilizing online marketing."

Step 6:

The server aggregates these solutions and formats them into a format that is easy for the user to understand.

Step 7:

The server sends the integrated solution to the user's terminal, which then visually displays it on its display device.

In this way, users can quickly and effectively access solutions from a variety of perspectives.

Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

The present invention provides a system that provides multifaceted solutions to issues and problems entered by users. This system combines an emotion engine to recognize the user's emotions and provide more optimized solutions.

User assignment entry

Users enter the issues or problems they want to solve through an interface on their web browser. The information entered by the user is sent to the server as an issue.

Receive and save assignments

The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

Analysis of the problem

The server uses a natural language processing (NLP) engine to analyze the received and stored assignments. This analysis process extracts the assignment's category and related keywords. For example, if the assignment "Looking for new methods for sustainable agriculture" is entered, the server extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Emotion recognition

The device uses an emotion engine to recognize the user's emotions. To do this, it analyzes the user's facial expressions, voice, and keyboard typing patterns when typing. The emotion engine extracts information such as whether the user is tense or relaxed.

Generating solutions

The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. As a specific example, a multicultural AI model and a multi-perspective AI model are used. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

Emotion-based adjustment

The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. It changes the tone and content of the proposed solutions depending on the user's emotions. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is relaxed, it will suggest new and creative ideas.

Integrating and presenting solutions

The server consolidates the generated solutions and organizes them into a coherent format. During this process, each solution is modified as needed and formatted for easy viewing. The organized solutions are then presented to the user. Presentation is done through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Specific examples

For example, here is a specific example of when the task entered is "I'm looking for new methods for sustainable agriculture."

The user enters the problem, which is received and stored by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The device uses an emotion engine to recognize the user's emotions and determines that the user is feeling anxious. The server generates solutions based on the analysis results using a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server adjusts the solutions to provide detailed and specific information to alleviate the user's anxiety. Finally, the server integrates these solutions, formats them in an easy-to-read format, and presents them to the user. The user can obtain specific and detailed solutions such as "Sustainable methods inspired by African agricultural technology" and "Examples of the application of vertical farming in urban areas."

Through these steps, the system of the present invention provides users with diverse and optimal solutions, and by adjusting according to the user's emotional state, supports more effective problem-solving.

The processing flow is explained below.

Step 1:

Users enter the issue or problem they want to solve through a web browser interface by entering the issue in the input field and clicking the submit button.

Step 2:

The server receives assignments submitted by users. The received assignments are temporarily stored in a database, ready for analysis.

Step 3:

The device uses an emotion engine to analyze the user's facial expressions, voice, keyboard typing patterns, etc., and extract emotional information about the user. For example, if the user is feeling anxious, the emotion engine will label it as "anxious."

Step 4:

The server invokes a natural language processing (NLP) engine to analyze the received and stored assignments. During this analysis process, the assignment's category and related keywords are extracted. For example, if the assignment "Looking for new methods for sustainable agriculture" is entered, "Category: Agriculture" and "Keywords: Sustainability, New Methods" are extracted.

Step 5:

The server generates solutions from multiple perspectives based on the analysis results and emotional information. Specific examples include a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Step 6:

The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. If the user is feeling anxious, it will provide detailed and specific solutions, and if the user is relaxed, it will adjust to suggest new and creative ideas.

Step 7:

The server then consolidates the generated solutions and formats them into a consistent format. During this process, each solution is modified and formatted for easy viewing.

Step 8:

The server presents the prepared solutions to the user through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Step 9:

Users can refer to the presented solutions and use them to solve their own problems as needed. This allows users to gain new perspectives and ideas.

Through these steps, the system of the present invention provides users with a variety of optimal solutions and makes adjustments based on the user's emotional state, thereby supporting more effective problem-solving.

(Example 2)

Next, we will explain Example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

Previous problem-solving systems did not present solutions that took the user's emotional state into consideration, and were unable to make suggestions that were optimized for the user's emotions. Furthermore, the presentation of solutions from different cultural backgrounds or fields of expertise based on analysis results was limited. As a result, the specific and multifaceted solutions that users desired were not adequately provided.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for recognizing the user's emotional state, means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results and the user's emotional state, and means for presenting the generated solutions to the user. This makes it possible to provide optimal solutions tailored to the user's emotions and present multifaceted solutions from different perspectives.

A "user" is a person or organization that uses the system to input issues or problems and receive solutions.

"Issues and problems" refer to the issues or problems that users want to solve through the system.

"Means of receiving" refers to the function that allows the system to receive issues and problems sent by users.

"Means of analysis" refers to the technology and algorithms used to analyze received issues and problems and understand their content.

"Means for recognizing emotional states" refers to technologies and algorithms for analyzing and judging a user's emotions.

"Different cultural backgrounds" refer to the cultures, customs, and values of different regions, countries, and societies.

A "specialty" refers to knowledge or skills specialized in a particular field or occupation.

"Means for generating solutions" is a function for generating appropriate solutions based on the analysis results and the user's emotional state.

"Presentation means" is a function for showing the generated solution to the user.

A "system" is a comprehensive device or software that performs a series of processes to receive and analyze a user's problem, and then generate and present a solution.

The present invention is a system that provides multifaceted and optimal solutions to issues and problems entered by a user. This system includes technology that recognizes the user's emotional state and adjusts solutions based on that. Specific embodiments of the present invention are described below.

System Configuration

User assignment input

Users enter the issues or problems they want to solve through a web browser interface. This interface consists of a simple web page with text input fields and a submit button. The information entered by the user is sent to the server as an HTTP request.

Receive and save assignments

The server receives assignments submitted by users and temporarily stores them in a database. This reception process is carried out using a common web server and database management system. For example, an Apache or Nginx server is used as the front end, and MySQL or PostgreSQL is used as the database.

Analysis of the problem

The server analyzes the received and stored assignments using a natural language processing (NLP) engine. For natural language processing, libraries such as OpenNLP or NLTK can be used. This engine is responsible for extracting assignment categories and related keywords.

Example:

If a user types "I'm looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture, Keywords: Sustainability, New Methods."

Emotion recognition

The device uses an emotion engine to recognize the user's emotions. The emotion engine can be, for example, Microsoft's Azure Cognitive Services or IBM's Watson. This engine analyzes the user's facial expressions, voice, and keyboard typing patterns when typing to determine the user's emotional state.

Example:

Based on the video and audio data when the user enters the task, the emotion engine returns "Emotion: Anxiety."

Generating solutions

The server uses multiple AI models to generate solutions based on the analysis results and the user's emotional state. Specific prompts are input to each AI model. For example, a generative AI model such as GPT-3 can be used.

Example:

Prompt for multicultural AI model: "Examples of agricultural technology applications in Africa"

Prompt for multi-perspective AI model: "The potential of vertical farming for urban agriculture."

Emotion-based adjustment

The server adjusts the generated solution based on the emotion recognition results. For example, if the user feels anxious, the solution will be changed to be more specific and detailed.

Example:

We will add specific procedures and pricing information to the "Examples of Application of Agricultural Technology in Africa" section and adjust the content to reduce user concerns.

Integrating and presenting solutions

The server then combines the generated solutions, formats them, and presents them to the user, for example by using HTML and CSS to display the solutions in an easy-to-read format on a web browser.

Example:

The server combines "Examples of agricultural technology applications in Africa" and "The potential for urban agriculture through vertical farming" into a report, and the solution is displayed when the user reloads the browser.

In this way, the system of the present invention provides optimal solutions tailored to the user's emotions and presents multifaceted solutions from different perspectives.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1: User inputs assignment

Users enter the issue or problem they want to solve through a web browser interface. This involves describing the issue in a text input field and clicking the "Submit" button.

Input: Text data describing the issue or problem (e.g., "I'm looking for new methods for sustainable agriculture.")

Output: Issue data sent to the server as an HTTP request

Step 2: Receive and save the assignment

The server receives the submitted assignment and stores it in a database. This step may also validate the format according to the database schema before saving the assignment.

Input: Issue data sent as an HTTP request

Output: Issue data stored in the database (e.g., "Issue ID: 12345, Content: Searching for new methods for sustainable agriculture")

Step 3: Analyze the issue

The server passes the received assignments to a natural language processing (NLP) engine for analysis. This analysis extracts the assignment's category and related keywords.

Input: Project data stored in the database (e.g., "Project ID: 12345, Content: Searching for new methods for sustainable agriculture")

Data processing and calculation: The NLP engine analyzes the text and extracts important categories and keywords.

Output: Extracted categories and keywords (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods")

Step 4: Recognize emotions

The device uses an emotion engine to analyze the user's emotional state. In this step, the user's facial expressions, voice, keyboard typing patterns, etc. are used as input data.

Input: User facial expression data, voice data, keyboard keystroke data

Data processing and calculation: The emotion engine analyzes this data to identify the user's emotional state.

Output: User's emotional state (e.g., "Emotion: Anxiety")

Step 5: Generate a solution

The server uses multiple AI models to generate solutions based on the analysis results and emotional state. In this step, an appropriate prompt sentence is entered into each AI model to generate a solution.

Input: Analysis results and emotional state (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods, Emotion: Anxiety")

Data processing and data calculation: Input a prompt statement into each AI model and generate a solution based on it.

Output: Generated solutions (e.g., "Application of agricultural technology in Africa" and "Possibilities for urban agriculture through vertical farming")

Step 6: Emotional Adjustment

The server adjusts the solution based on the user's emotional information recognized by the emotion engine. If the user is feeling anxious, the solution will be made more specific and detailed.

Input: User's emotional state and generated solution

Data processing and calculation: Modify and adjust parts of the solution based on emotional information.

Output: Tailored solution (e.g., "Example of agricultural technology application in Africa - specific procedures and pricing information added")

Step 7: Integrate and present the solution

The server consolidates multiple solutions, formats them, and presents them to the user, who then sees them in an easy-to-read format displayed in their web browser.

Input: Tailored solutions (e.g., "Applications of agricultural technology in Africa," "Potential for urban agriculture through vertical farming")

Data processing and calculation: Integrate solutions and format using HTML and CSS.

Output: The integrated solution displayed in the user's browser.

This completes the entire system's processing, allowing users to see a multifaceted, emotionally optimized solution.

(Application Example 2)

Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional systems can provide multifaceted solutions to problems entered by users, but because they do not optimize with user emotions in mind, it is difficult to present more appropriate solutions to individual users. Furthermore, there is a lack of methods for tailoring appropriate solutions based on the user's emotional state and providing personalized information. Therefore, in order to increase user satisfaction, a system that recognizes the user's emotions and adjusts solutions based on them is needed.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results, means for recognizing the user's emotions, means for adjusting the generated solution based on the user's emotions, and means for presenting the generated solution to the user. This makes it possible to provide personalized solutions according to the user's emotional state.

"Means for receiving any issues or problems entered by the user" refers to an interface that receives data such as text, audio, and images entered by the user and sends it to the server.

"Means for analyzing received issues and problems" refers to analytical functions such as natural language processing engines that analyze received data and extract relevant keywords and categories.

"Means for generating solutions from different cultural backgrounds and fields of expertise" refers to a function for generating solutions from various cultures and fields of expertise using multicultural AI models and multi-perspective AI models.

"Means for recognizing user emotions" refers to an emotion engine that analyzes the user's facial expressions, voice, input patterns, etc. to recognize their emotional state.

"Means for adjusting generated solutions based on the user's emotions" is a function for appropriately adjusting the tone and content of solutions according to the user's emotional state recognized by the emotion engine.

"Means for presenting the generated solution to the user" refers to an interface for arranging the adjusted solution in an easy-to-view format and displaying it to the user.

The present invention is a system that provides multifaceted solutions to issues and problems entered by the user. In particular, it includes a function that recognizes the user's emotions and adjusts the solutions accordingly. The system's components include the following elements:

First, a user interface on a smartphone or PC is used as a means of receiving any assignments or problems entered by the user. Through this interface, the user inputs the assignments using text or voice. The received assignments and problems are then sent to the server.

Next, a server is installed as a means of analyzing the received issues and problems. This server uses a natural language processing (NLP) engine to analyze the issues and extract related keywords and categories. For example, if a user types "I want to know about recent environmental issues," the server will extract "Category: Environment" and "Keywords: Recent Environmental Issues."

Furthermore, the server uses multicultural AI models and multi-perspective AI models as a means of generating solutions from different cultural backgrounds and areas of expertise. These multiple AI models generate solutions from different perspectives and address user issues from multiple angles.

The device is also equipped with an emotion engine to recognize the user's emotions. This makes it possible to analyze the user's emotional state from their facial expressions, voice, keystroke patterns, etc. For example, it can recognize whether the user is excited or anxious.

The server also has the means to adjust the generated solutions based on the user's emotions. It adjusts the content and tone of the solutions depending on the user's recognized emotional state. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if they are relaxed, it will suggest more creative ideas.

Finally, to present the generated solutions to the user, the server formats the solutions in an easy-to-read format and presents them to the user through a user interface. This allows the user to view solutions from different perspectives in a list format.

As a concrete example, consider the case where a user types, "What environmental issues are you interested in recently? Please tell me in detail." In this case, the server analyzes the relevant data, and the emotion engine analyzes the user's emotional state. Next, using the multicultural AI model and multi-perspective AI model, it generates solutions such as "The latest research on global warming" or "The current state and future of plastic pollution." These solutions are then adjusted to appeal to the user's interest and proposed to them through the user interface.

In this way, the present invention can analyze the issues and problems entered by the user from multiple angles, provide optimal solutions from different cultures and fields of expertise, and even adjust them according to the user's emotional state.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Receives any issues or problems entered by users.

Specifically, users input tasks via text or voice through a user interface on their smartphone or computer. This input data (tasks or problems) becomes input data for the system. At this stage, the input data is sent to the server, which receives it.

Step 2:

Analyze received issues and problems.

The server uses a natural language processing (NLP) engine to analyze the input data received from the user. The data to be analyzed is the text and voice data entered by the user. Specifically, this analysis process extracts related keywords and categories. For example, if the input is "I want to know about recent environmental issues," "Category: Environment" and "Keywords: Recent Environmental Issues" will be extracted.

Step 3:

Generate solutions from different cultural backgrounds and disciplines.

The server generates solutions from different cultural backgrounds and fields of expertise based on the extracted keywords and categories. To do this, it uses multicultural AI models and multi-perspective AI models. For example, based on "Category: Environment" and "Keywords: Recent Environmental Issues," it generates solutions such as "Latest research on global warming" and "The current state and future of plastic pollution."

Step 4:

Recognize user emotions.

An emotion engine installed on the user's device analyzes the user's facial expressions, voice, and input patterns to recognize their emotional state. This data includes facial recognition data, voice data, and typing patterns. For example, it recognizes whether the user is feeling anxious or excited. This emotional information becomes input data for adjusting the solution generated in the next step.

Step 5:

Adjust the generated solutions based on the user's sentiment.

The server adjusts the content and tone of the generated solution based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is feeling relaxed, it will suggest creative ideas. In this process, emotional data and solution data are input, and adjusted solution data is output.

Step 6:

Present the generated solution to the user.

The server then formats the adjusted solutions in an easy-to-read format and presents them to the user through a user interface. In this step, the formatted solution data is input and display data for the user is output. Specifically, the solutions are presented in a list format on the web browser, allowing the user to review them. For example, "Recent research on global warming" and "The current state and future of plastic pollution" are displayed.

The specific processing unit 290 transmits the results of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the results of the specific processing. The microphone 38B acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). Examples of data generation model 58 include generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>) and Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart device 14.

[Second embodiment]

Figure 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

As shown in FIG. 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

Figure 4 shows an example of the main functions of the data processing device 12 and smart glasses 214. As shown in Figure 4, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

The present invention is a system that provides multifaceted solutions to issues and problems entered by users. This system works through a process of receiving issues, analyzing them, generating solutions from different cultural backgrounds and areas of expertise, and presenting them to the user.

User assignment entry

Users enter the issues or problems they want to solve through an interface on their web browser. The information entered by the user is sent to the server as an issue.

Receive and save assignments

The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

Analysis of the problem

The server analyzes the received assignment using a natural language processing (NLP) engine. This analysis process extracts the category and related keywords of the input assignment. For example, if the input assignment is "Looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Generating solutions

The server utilizes multiple AI models based on the analysis results to generate solutions from multiple perspectives. Specifically, it generates solutions using a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Integrating and presenting solutions

The server then integrates the generated solutions and formats them before presenting them to the user. The integrated solution is displayed to the user through a web browser interface, allowing the user to see solutions from a variety of perspectives.

Specific examples

For example, here is a specific example of when the task entered is "I'm looking for new methods for sustainable agriculture."

The user enters a problem, which is received and saved by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The server generates solutions based on the analysis results, utilizing a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," and the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server integrates these solutions and presents them to the user in the optimal format. Ultimately, the user is able to obtain solutions such as "Sustainable methods inspired by African agricultural technology" and "Examples of the application of vertical farming in urban areas."

The system of the present invention allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling effective problem-solving.

The processing flow is explained below.

Step 1:

Users enter the issue or problem they want to solve through a web browser interface. By entering the issue in the input field and clicking the submit button, the issue is sent to the system.

Step 2:

The server receives assignments sent by users. The received assignments are temporarily stored in a database. This storage process is for later analysis.

Step 3:

The server invokes a natural language processing (NLP) engine to analyze the received and saved assignments. This analysis process extracts the assignment's category and related keywords. For example, for the assignment "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Step 4:

The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. As a specific example, a multicultural AI model and a multi-perspective AI model are used. The multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Step 5:

The server then combines the generated solutions into a consistent format. During this process, each solution is modified as needed and formatted for easy viewing.

Step 6:

The server presents the prepared solutions to the user through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Step 7:

Users can refer to the presented solutions and use them to solve their own problems as needed. This allows users to gain new perspectives and ideas.

The system of the present invention provides users with a variety of solutions through these steps, supporting effective problem-solving.

(Example 1)

Next, we will explain Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

In today's increasingly diverse society, the challenges and problems users face are complex, making it difficult to provide effective solutions from a single perspective or approach. Furthermore, integrating knowledge from different cultural backgrounds and fields of expertise requires collaboration and cooperation among people with expertise in each field. However, the systems and methods that enable this are not well established, making it difficult for users to quickly obtain multifaceted solutions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for receiving any tasks or problems input by the user, means for temporarily storing the received tasks or problems in a database, means for analyzing the stored tasks or problems using a natural language processing engine and extracting categories and related keywords for the input tasks, means for utilizing multiple AI models to generate solutions from different cultural backgrounds and fields of expertise based on the analysis results, means for integrating the multiple generated solutions and formatting them before presenting them to the user, and means for displaying the formatted solutions to the user. This enables users to quickly obtain multifaceted and effective solutions.

"User" refers to the entity that uses the system to input issues and problems and receive solutions.

"Server" refers to a computer system that receives input from users, analyzes it, and generates and presents solutions.

"Challenges and problems" refer to specific situations or difficult circumstances that users are seeking a solution to.

"Means of receiving" refers to the function by which the server acquires and stores information entered by the user.

A "database" refers to an information storage system for temporarily storing and managing data such as issues and problems.

A "natural language processing engine" is a software module that analyzes human language and extracts categories and keywords for input issues or problems.

"Means of analysis" refers to the function of using a natural language processing engine to analyze received issues and problems and extract relevant information.

"AI model" refers to a model trained using artificial intelligence algorithms to solve a specific problem.

A "multicultural AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different cultural backgrounds.

A "multi-perspective AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different fields of expertise.

"Means for generating solutions" refers to the function of using multicultural AI models and multi-perspective AI models to generate specific solutions to problems.

"Means of integration" refers to the function for combining and shaping multiple generated solutions into one.

"Formatting tools" refers to the ability to adjust the format and appearance of a solution before presenting it to the user.

"Means of display" refers to the function of presenting the formatted solution to the user in an easy-to-read format.

The present invention relates to a system for providing multifaceted solutions to issues and problems entered by users. This system receives issues entered by users, stores them in a database, analyzes them using a natural language processing (NLP) engine, and generates solutions using multiple AI models based on the analysis results. It also integrates the generated solutions, formats them, and presents them to the user.

Users enter the problem they want to solve through a web browser. For example, they could enter a specific prompt such as, "I'm looking for new methods for sustainable agriculture."

Once the input is complete, the server receives it and temporarily stores it in the assignment database. It is recommended that a common relational database such as MySQL or PostgreSQL be used for this database. The saved assignment data is also assigned metadata such as a timestamp and user ID.

The server then retrieves the saved task data and analyzes it using an NLP engine. The NLP engine can use natural language processing libraries such as SpaCy or NLTK. Analysis steps include tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, for the task "Looking for new methods for sustainable agriculture," results such as "Category: Agriculture" and "Keywords: Sustainability, New Methods" can be obtained.

Based on the analysis results, the server generates solutions using a multicultural AI model and a multi-perspective AI model. These AI models provide solutions from different cultural backgrounds and areas of expertise. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

The generated solutions are then integrated and formatted by the server, which includes merging and formatting the text. The formatted solutions are then presented to the user through a web browser.

This allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling more effective problem-solving. For example, users can obtain specific solutions such as "sustainable methods based on African agricultural techniques" or "examples of applying vertical farming in urban areas."

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1: User enters assignment

Users enter the issues or problems they wish to solve through a web browser interface. For example, they might enter, "Please provide us with some innovative ideas for managing green spaces in urban areas." Once the information is complete, it is sent to the server via an HTML form.

Input: The prompt text entered by the user

Output: User input data is sent to the server.

Step 2: The server receives and stores the assignment.

The server receives HTTP requests and retrieves the assignment data submitted by the user. The retrieved data is temporarily stored in a relational database such as MySQL or PostgreSQL. Metadata such as timestamps and user IDs are also added to the data for later analysis.

Input: Issue data from the user

Output: Issue data stored in the database

Step 3: The server analyzes the issue.

The server retrieves the saved assignment data and analyzes it using an NLP engine such as SpaCy or NLTK. This analysis process includes tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, if the assignment "Looking for new methods for sustainable agriculture" is input, the NLP engine will extract "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Input: Issue data retrieved from the database

Output: Category and keyword information

Step 4: The server generates a solution.

The server uses a multicultural AI model and a multi-perspective AI model to generate solutions based on the analysis results. The analysis results are input as prompts into the multicultural AI model, which generates solutions from different cultural backgrounds. For example, it generates "Examples of green space management in European urban planning." The same analysis results are input as prompts into the multi-perspective AI model, which generates solutions based on specialized fields. For example, it generates "New methods for managing green spaces in urban areas using technology."

Input: Analysis results (category and keyword information)

Output: Multiple solutions

Step 5: The server integrates and presents the solution.

The server then integrates and formats the generated solutions. Specifically, it merges the solution text and formats it into a visually easy-to-read form. The formatted solution is then saved back into the database and displayed to the user via a web browser. The user can view multiple solutions and consider approaches to solving the problem from multiple perspectives.

Input: Multiple Solutions

Output: Consolidated and formatted solution

These are the main processing steps of this system. This allows users to quickly obtain solutions from a variety of perspectives, enabling more effective problem-solving.

(Application Example 1)

Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Existing solution-providing systems rely on a single cultural background or field of expertise, making it difficult to obtain solutions from diverse perspectives. They also lack the functionality to effectively analyze users' issues, extract related keywords, and generate and present solutions from multiple cultural and perspectives based on those keywords. For this reason, there is a need for systems that can quickly provide optimal solutions to users' problems.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes: means for receiving any task or problem entered by the user; means for analyzing the received task or problem; means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results; means for presenting the generated solution to the user; means including a natural language processing engine for analyzing the task entered by the user and extracting specific keywords; means for utilizing multiple artificial intelligence models to generate solutions from multicultural and multi-perspectives; means for integrating the generated solutions and optimally formatting them; and means for displaying the integrated solution on a display device. This allows the user to gain new perspectives from a variety of points of view, enabling them to quickly obtain multifaceted solutions.

Word definition

"User" refers to a person who uses the system to input issues and problems.

"Issue" refers to a problem or question that a user enters into the system and needs to be solved.

A "natural language processing engine" refers to a software component that analyzes input text and extracts specific keywords.

"Cultural background" refers to the collection of history, customs, values, etc. that exist in different regions and countries.

A "specialty" refers to an area that requires concentrated specific knowledge or skills.

"Solution" refers to specific measures or proposals provided to address users' challenges or problems.

An "artificial intelligence model" refers to an algorithm or system designed to perform a specific task based on training data.

"Format" refers to the standards and rules for arranging information in a particular form or structure.

"Display device" refers to a device for visually presenting the generated solution to the user.

A "multicultural perspective" refers to an approach to solving problems from the perspectives of various cultures.

"Multiple perspectives" refers to an approach that looks at a single issue from multiple different angles or perspectives.

Form for implementing the invention

The present invention is a system that provides multifaceted solutions to issues and problems entered by users. This system includes a process that receives and analyzes issues entered by users via devices such as smartphones, and generates and provides solutions from multiple cultural and perspectives to the user.

System Program

The system consists of the following components:

1. User device: A device such as a smartphone or tablet. Equipped with an interface for users to input tasks.

2. Server: Installed in a cloud environment, it performs the primary processing of analyzing received challenges and generating solutions. Specifically, it includes a database, a natural language processing engine, multiple artificial intelligence models, and software components for integrating and formatting solutions.

3. Display device: Present the solution visually on the user's terminal or other device.

Program processing explanation

The server receives assignments sent from user devices and temporarily stores them in a database. It then uses a natural language processing engine (e.g., the transformers library) to analyze the received assignments and extract specific keywords. This analysis process clarifies the assignment's category and related keywords.

Based on the analysis results, the server utilizes multiple artificial intelligence models to generate solutions from a multicultural and multi-perspective perspective. For example, one model generates solutions from a "multicultural perspective" and another model generates solutions from a "multi-perspective" perspective.

The generated solutions are then integrated and optimally formatted by the server. The formatted solution is then sent to the user's terminal's display device and presented to the user.

Specific examples

For example, if the problem "Sales are sluggish" is entered, a solution will be generated using the following steps:

Step 1:

The problem "Sales are sluggish" is entered from the user's device.

Step 2:

The server receives this challenge and analyzes it using a natural language processing engine. As a result of the analysis, the keywords "renewal case study," "promotion," "display improvement," and "online marketing" are extracted.

Step 3:

The server generates solutions using multiple artificial intelligence models. For example, it generates "success stories in Japan" and "overseas promotion strategies" from a multicultural perspective, and "ways to improve displays" and "techniques for utilizing online marketing" from multiple perspectives.

Step 4:

These solutions are integrated, formatted optimally, and presented on the user's terminal display device.

Example prompt:

A user has entered the issue of "slumping sales." The following keywords have been extracted: "renewal case study," "promotion," "display improvement," and "online marketing." Based on this, please propose a solution from a multicultural and multi-perspective perspective.

In this way, the system of the present invention allows users to gain new perspectives from a variety of viewpoints and quickly obtain multifaceted solutions. This makes it possible to provide effective solutions to the problems users face.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Program processing steps

Step 1: User inputs assignment

Input: Users enter tasks using their smartphones or tablets.

Operation: The user's device enters the assignment through a web browser interface and sends the assignment to the system.

Output: The submitted assignment arrives at the server.

Step 2: Receive and save the assignment

Input: Assignment submitted from the user's device.

How it works: The server receives assignments submitted by users and temporarily stores them in a database.

Output: Issues saved in the database.

Step 3: Analyze the issue

Input: Issues saved in the database.

How it works: The server uses a natural language processing engine (e.g., the transformers library) to parse the incoming assignment. The parsing process extracts the assignment's category and associated keywords.

Output: Extracted keywords and categories.

Step 4: Generating Solutions - From a Multicultural Perspective

Input: Extracted keywords and categories.

How it works: The server uses an artificial intelligence model to generate solutions from a multicultural perspective. Based on keywords, it generates solutions from a multicultural perspective.

Output: Solutions from a multicultural perspective.

Step 5: Generate solutions - from multiple perspectives

Input: Extracted keywords and categories.

How it works: The server uses an artificial intelligence model to generate solutions from multiple perspectives. Based on keywords, it generates solutions from multiple perspectives.

Output: A solution from multiple perspectives.

Step 6: Integrate and format the solution

Input: Multicultural and multi-perspective solutions.

How it works: The server combines the multiple solutions generated and formats them into the most optimal format.

Output: Integrated solution.

Step 7: Present an integrated solution

Input: Integrated solution.

Operation: The server sends the integrated solution to the user's device. The user's device's display device visually displays it.

Output: The solution presented to the user.

Specific examples of behavior

Step 1:

The user enters the issue of "poor sales" into a smartphone app.

Step 2:

The user's device sends this assignment data to the server, which receives it and stores it in a database.

Step 3:

The server launches a natural language processing engine, analyzes the problem text, and extracts keywords such as "renewal case study," "promotion," "display improvement," and "online marketing."

Step 4:

The server runs an artificial intelligence model using the extracted keywords to generate solutions from a multicultural perspective, generating "success stories in Japan" and "overseas promotion strategies."

Step 5:

The server runs another artificial intelligence model using the extracted keywords to generate solutions from multiple perspectives, generating "ways to improve display" and "techniques for utilizing online marketing."

Step 6:

The server aggregates these solutions and formats them into a format that is easy for the user to understand.

Step 7:

The server sends the integrated solution to the user's terminal, which then visually displays it on its display device.

In this way, users can quickly and effectively access solutions from a variety of perspectives.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

The present invention provides a system that provides multifaceted solutions to issues and problems entered by users. This system combines an emotion engine to recognize the user's emotions and provide more optimized solutions.

User assignment entry

Users enter the issues or problems they want to solve through an interface on their web browser. The information entered by the user is sent to the server as an issue.

Receive and save assignments

The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

Analysis of the problem

The server uses a natural language processing (NLP) engine to analyze the received and stored assignments. This analysis process extracts the assignment's category and related keywords. For example, if the assignment "Looking for new methods for sustainable agriculture" is entered, the server extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Emotion recognition

The device uses an emotion engine to recognize the user's emotions. To do this, it analyzes the user's facial expressions, voice, and keyboard typing patterns when typing. The emotion engine extracts information such as whether the user is tense or relaxed.

Generating solutions

The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. As a specific example, a multicultural AI model and a multi-perspective AI model are used. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

Emotion-based adjustment

The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. It changes the tone and content of the proposed solutions depending on the user's emotions. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is relaxed, it will suggest new and creative ideas.

Integrating and presenting solutions

The server consolidates the generated solutions and organizes them into a coherent format. During this process, each solution is modified as needed and formatted for easy viewing. The organized solutions are then presented to the user. Presentation is done through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Specific examples

For example, here is a specific example of when the task entered is "I'm looking for new methods for sustainable agriculture."

The user enters the problem, which is received and stored by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The device uses an emotion engine to recognize the user's emotions and determines that the user is feeling anxious. The server generates solutions based on the analysis results using a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server adjusts the solutions to provide detailed and specific information to alleviate the user's anxiety. Finally, the server integrates these solutions, formats them in an easy-to-read format, and presents them to the user. The user can obtain specific and detailed solutions such as "Sustainable methods inspired by African agricultural technology" and "Examples of the application of vertical farming in urban areas."

Through these steps, the system of the present invention provides users with diverse and optimal solutions, and by adjusting according to the user's emotional state, supports more effective problem-solving.

The processing flow is explained below.

Step 1:

Users enter the issue or problem they want to solve through a web browser interface by entering the issue in the input field and clicking the submit button.

Step 2:

The server receives assignments submitted by users. The received assignments are temporarily stored in a database, ready for analysis.

Step 3:

The device uses an emotion engine to analyze the user's facial expressions, voice, keyboard typing patterns, etc., and extract emotional information about the user. For example, if the user is feeling anxious, the emotion engine will label it as "anxious."

Step 4:

The server invokes a natural language processing (NLP) engine to analyze the received and stored assignments. During this analysis process, the assignment's category and related keywords are extracted. For example, if the assignment "Looking for new methods for sustainable agriculture" is entered, "Category: Agriculture" and "Keywords: Sustainability, New Methods" are extracted.

Step 5:

The server generates solutions from multiple perspectives based on the analysis results and emotional information. Specific examples include a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Step 6:

The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. If the user is feeling anxious, it will provide detailed and specific solutions, and if the user is relaxed, it will adjust to suggest new and creative ideas.

Step 7:

The server then consolidates the generated solutions and formats them into a consistent format. During this process, each solution is modified and formatted for easy viewing.

Step 8:

The server presents the prepared solutions to the user through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Step 9:

Users can refer to the presented solutions and use them to solve their own problems as needed. This allows users to gain new perspectives and ideas.

Through these steps, the system of the present invention provides users with a variety of optimal solutions and makes adjustments based on the user's emotional state, thereby supporting more effective problem-solving.

(Example 2)

Next, we will explain Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Previous problem-solving systems did not present solutions that took the user's emotional state into consideration, and were unable to make suggestions that were optimized for the user's emotions. Furthermore, the presentation of solutions from different cultural backgrounds or fields of expertise based on analysis results was limited. As a result, the specific and multifaceted solutions that users desired were not adequately provided.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for recognizing the user's emotional state, means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results and the user's emotional state, and means for presenting the generated solutions to the user. This makes it possible to provide optimal solutions tailored to the user's emotions and present multifaceted solutions from different perspectives.

A "user" is a person or organization that uses the system to input issues or problems and receive solutions.

"Issues and problems" refer to the issues or problems that users want to solve through the system.

"Means of receiving" refers to the function that allows the system to receive issues and problems sent by users.

"Means of analysis" refers to the technology and algorithms used to analyze received issues and problems and understand their content.

"Means for recognizing emotional states" refers to technologies and algorithms for analyzing and judging a user's emotions.

"Different cultural backgrounds" refer to the cultures, customs, and values of different regions, countries, and societies.

A "specialty" refers to knowledge or skills specialized in a particular field or occupation.

"Means for generating solutions" is a function for generating appropriate solutions based on the analysis results and the user's emotional state.

"Presentation means" is a function for showing the generated solution to the user.

A "system" is a comprehensive device or software that performs a series of processes to receive and analyze a user's problem, and then generate and present a solution.

The present invention is a system that provides multifaceted and optimal solutions to issues and problems entered by a user. This system includes technology that recognizes the user's emotional state and adjusts solutions based on that. Specific embodiments of the present invention are described below.

System Configuration

User assignment input

Users enter the issues or problems they want to solve through a web browser interface. This interface consists of a simple web page with text input fields and a submit button. The information entered by the user is sent to the server as an HTTP request.

Receive and save assignments

The server receives assignments submitted by users and temporarily stores them in a database. This reception process is carried out using a common web server and database management system. For example, an Apache or Nginx server is used as the front end, and MySQL or PostgreSQL is used as the database.

Analysis of the problem

The server analyzes the received and stored assignments using a natural language processing (NLP) engine. For natural language processing, libraries such as OpenNLP or NLTK can be used. This engine is responsible for extracting assignment categories and related keywords.

Example:

If a user types "I'm looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture, Keywords: Sustainability, New Methods."

Emotion recognition

The device uses an emotion engine to recognize the user's emotions. The emotion engine can be, for example, Microsoft's Azure Cognitive Services or IBM's Watson. This engine analyzes the user's facial expressions, voice, and keyboard typing patterns when typing to determine the user's emotional state.

Example:

Based on the video and audio data when the user enters the task, the emotion engine returns "Emotion: Anxiety."

Generating solutions

The server uses multiple AI models to generate solutions based on the analysis results and the user's emotional state. Specific prompts are input to each AI model. For example, a generative AI model such as GPT-3 can be used.

Example:

Prompt for multicultural AI model: "Examples of agricultural technology applications in Africa"

Prompt for multi-perspective AI model: "The potential of vertical farming for urban agriculture."

Emotion-based adjustment

The server adjusts the generated solution based on the emotion recognition results. For example, if the user feels anxious, the solution will be changed to be more specific and detailed.

Example:

We will add specific procedures and pricing information to the "Examples of Application of Agricultural Technology in Africa" section and adjust the content to reduce user concerns.

Integrating and presenting solutions

The server then combines the generated solutions, formats them, and presents them to the user, for example by using HTML and CSS to display the solutions in an easy-to-read format on a web browser.

Example:

The server combines "Examples of agricultural technology applications in Africa" and "The potential for urban agriculture through vertical farming" into a report, and the solution is displayed when the user reloads the browser.

In this way, the system of the present invention provides optimal solutions tailored to the user's emotions and presents multifaceted solutions from different perspectives.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1: User inputs assignment

Users enter the issue or problem they want to solve through a web browser interface. This involves describing the issue in a text input field and clicking the "Submit" button.

Input: Text data describing the issue or problem (e.g., "I'm looking for new methods for sustainable agriculture.")

Output: Issue data sent to the server as an HTTP request

Step 2: Receive and save the assignment

The server receives the submitted assignment and stores it in a database. This step may also validate the format according to the database schema before saving the assignment.

Input: Issue data sent as an HTTP request

Output: Issue data stored in the database (e.g., "Issue ID: 12345, Content: Searching for new methods for sustainable agriculture")

Step 3: Analyze the issue

The server passes the received assignments to a natural language processing (NLP) engine for analysis. This analysis extracts the assignment's category and related keywords.

Input: Project data stored in the database (e.g., "Project ID: 12345, Content: Searching for new methods for sustainable agriculture")

Data processing and calculation: The NLP engine analyzes the text and extracts important categories and keywords.

Output: Extracted categories and keywords (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods")

Step 4: Recognize emotions

The device uses an emotion engine to analyze the user's emotional state. In this step, the user's facial expressions, voice, and keyboard typing patterns are used as input data.

Input: User facial expression data, voice data, keyboard keystroke data

Data processing and calculation: The emotion engine analyzes this data to identify the user's emotional state.

Output: User's emotional state (e.g., "Emotion: Anxiety")

Step 5: Generate a solution

The server uses multiple AI models to generate solutions based on the analysis results and emotional state. In this step, an appropriate prompt sentence is entered into each AI model to generate a solution.

Input: Analysis results and emotional state (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods, Emotion: Anxiety")

Data processing and data calculation: Input a prompt statement into each AI model and generate a solution based on it.

Output: Generated solutions (e.g., "Application of agricultural technology in Africa" and "Possibilities for urban agriculture through vertical farming")

Step 6: Emotional Adjustment

The server adjusts the solution based on the user's emotional information recognized by the emotion engine. If the user is feeling anxious, the solution will be made more specific and detailed.

Input: User's emotional state and generated solution

Data processing and calculation: Modify and adjust parts of the solution based on emotional information.

Output: Tailored solution (e.g., "Example of agricultural technology application in Africa - specific procedures and pricing information added")

Step 7: Integrate and present the solution

The server consolidates multiple solutions, formats them, and presents them to the user, who then sees them in an easy-to-read format displayed in their web browser.

Input: Tailored solutions (e.g., "Applications of agricultural technology in Africa," "Potential for urban agriculture through vertical farming")

Data processing and calculation: Integrate solutions and format using HTML and CSS.

Output: The integrated solution displayed in the user's browser.

This completes the entire system's processing, allowing users to see a multifaceted, emotionally optimized solution.

(Application Example 2)

Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional systems can provide multifaceted solutions to problems entered by users, but because they do not optimize with user emotions in mind, it is difficult to present more appropriate solutions to individual users. Furthermore, there is a lack of methods for tailoring appropriate solutions based on the user's emotional state and providing personalized information. Therefore, in order to increase user satisfaction, a system that recognizes the user's emotions and adjusts solutions based on them is needed.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results, means for recognizing the user's emotions, means for adjusting the generated solution based on the user's emotions, and means for presenting the generated solution to the user. This makes it possible to provide personalized solutions according to the user's emotional state.

"Means for receiving any issues or problems entered by the user" refers to an interface that receives data such as text, audio, and images entered by the user and sends it to the server.

"Means for analyzing received issues and problems" refers to analytical functions such as natural language processing engines that analyze received data and extract relevant keywords and categories.

"Means for generating solutions from different cultural backgrounds and fields of expertise" refers to a function for generating solutions from various cultures and fields of expertise using multicultural AI models and multi-perspective AI models.

"Means for recognizing user emotions" refers to an emotion engine that analyzes the user's facial expressions, voice, input patterns, etc. to recognize their emotional state.

"Means for adjusting generated solutions based on the user's emotions" is a function for appropriately adjusting the tone and content of solutions according to the user's emotional state recognized by the emotion engine.

"Means for presenting the generated solution to the user" refers to an interface for arranging the adjusted solution in an easy-to-view format and displaying it to the user.

The present invention is a system that provides multifaceted solutions to issues and problems entered by the user. In particular, it includes a function that recognizes the user's emotions and adjusts the solutions accordingly. The system's components include the following elements:

First, a user interface on a smartphone or PC is used as a means of receiving any assignments or problems entered by the user. Through this interface, the user inputs the assignments using text or voice. The received assignments and problems are then sent to the server.

Next, a server is installed as a means of analyzing the received issues and problems. This server uses a natural language processing (NLP) engine to analyze the issues and extract related keywords and categories. For example, if a user types "I want to know about recent environmental issues," the server will extract "Category: Environment" and "Keywords: Recent Environmental Issues."

Furthermore, the server uses multicultural AI models and multi-perspective AI models as a means of generating solutions from different cultural backgrounds and fields of expertise. These multiple AI models generate solutions from different perspectives and address user issues from multiple angles.

The device is also equipped with an emotion engine to recognize the user's emotions. This makes it possible to analyze the user's emotional state from their facial expressions, voice, keystroke patterns, etc. For example, it can recognize whether the user is excited or anxious.

The server also has the means to adjust the generated solutions based on the user's emotions. It adjusts the content and tone of the solutions depending on the user's recognized emotional state. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if they are relaxed, it will suggest more creative ideas.

Finally, to present the generated solutions to the user, the server formats them in an easy-to-read format and presents them to the user through a user interface. This allows the user to view solutions from different perspectives in a list format.

As a concrete example, consider the case where a user types, "What environmental issues are you interested in recently? Please tell me in detail." In this case, the server analyzes the relevant data, and the emotion engine analyzes the user's emotional state. Next, using the multicultural AI model and multi-perspective AI model, it generates solutions such as "The latest research on global warming" or "The current state and future of plastic pollution." These solutions are then adjusted to appeal to the user's interest and proposed to them through the user interface.

In this way, the present invention can analyze the issues and problems entered by the user from multiple angles, provide optimal solutions from different cultures and fields of expertise, and even adjust them according to the user's emotional state.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Receives any issues or problems entered by users.

Specifically, users input tasks via text or voice through a user interface on their smartphone or computer. This input data (tasks or problems) becomes input data for the system. At this stage, the input data is sent to the server, which receives it.

Step 2:

Analyze received issues and problems.

The server uses a natural language processing (NLP) engine to analyze the input data received from the user. The data to be analyzed is the text and voice data entered by the user. Specifically, this analysis process extracts related keywords and categories. For example, if the input is "I want to know about recent environmental issues," "Category: Environment" and "Keywords: Recent Environmental Issues" will be extracted.

Step 3:

Generate solutions from different cultural backgrounds and disciplines.

The server generates solutions from different cultural backgrounds and fields of expertise based on the extracted keywords and categories. To do this, it uses multicultural AI models and multi-perspective AI models. For example, based on "Category: Environment" and "Keywords: Recent Environmental Issues," it generates solutions such as "Latest research on global warming" and "The current state and future of plastic pollution."

Step 4:

Recognize user emotions.

An emotion engine installed on the user's device analyzes the user's facial expressions, voice, and input patterns to recognize their emotional state. This data includes facial recognition data, voice data, and typing patterns. For example, it recognizes whether the user is feeling anxious or excited. This emotional information becomes input data for adjusting the solution generated in the next step.

Step 5:

Adjust the generated solutions based on the user's sentiment.

The server adjusts the content and tone of the generated solution based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is feeling relaxed, it will suggest creative ideas. In this process, emotional data and solution data are input, and adjusted solution data is output.

Step 6:

Present the generated solution to the user.

The server then formats the adjusted solutions in an easy-to-read format and presents them to the user through a user interface. In this step, the formatted solution data is input and display data for the user is output. Specifically, the solutions are presented in a list format on the web browser, allowing the user to review them. For example, "Recent research on global warming" and "The current state and future of plastic pollution" are displayed.

The specific processing unit 290 transmits the results of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). Examples of data generation model 58 include generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>) and Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart glasses 214.

[Third embodiment]

Figure 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

As shown in FIG. 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

Figure 6 shows an example of the main functions of the data processing device 12 and headset terminal 314. As shown in Figure 6, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the headset terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

The present invention is a system that provides multifaceted solutions to issues and problems entered by users. This system works through a process of receiving issues, analyzing them, generating solutions from different cultural backgrounds and areas of expertise, and presenting them to the user.

User assignment entry

Users enter the issues or problems they want to solve through an interface on their web browser. The information entered by the user is sent to the server as an issue.

Receive and save assignments

The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

Analysis of the problem

The server analyzes the received assignment using a natural language processing (NLP) engine. This analysis process extracts the category and related keywords of the input assignment. For example, if the input assignment is "Looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Generating solutions

The server utilizes multiple AI models based on the analysis results to generate solutions from multiple perspectives. Specifically, it generates solutions using a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Integrating and presenting solutions

The server then integrates the generated solutions and formats them before presenting them to the user. The integrated solution is displayed to the user through a web browser interface, allowing the user to view solutions from a variety of perspectives.

Specific examples

For example, here is a specific example of when the task entered is "I'm looking for new methods for sustainable agriculture."

The user enters a problem, which is received and saved by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The server generates solutions based on the analysis results, utilizing a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," and the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server integrates these solutions and presents them to the user in the optimal format. Ultimately, the user is able to obtain solutions such as "Sustainable methods inspired by African agricultural technology" and "Examples of the application of vertical farming in urban areas."

The system of the present invention allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling effective problem-solving.

The processing flow is explained below.

Step 1:

Users enter the issue or problem they want to solve through a web browser interface. By entering the issue in the input field and clicking the submit button, the issue is sent to the system.

Step 2:

The server receives assignments sent by users. The received assignments are temporarily stored in a database. This storage process is for later analysis.

Step 3:

The server invokes a natural language processing (NLP) engine to analyze the received and saved assignments. This analysis process extracts the assignment's category and related keywords. For example, for the assignment "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Step 4:

The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. As a specific example, a multicultural AI model and a multi-perspective AI model are used. The multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Step 5:

The server then combines the generated solutions into a consistent format. During this process, each solution is modified as needed and formatted for easy viewing.

Step 6:

The server presents the prepared solutions to the user through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Step 7:

Users can refer to the presented solutions and use them to solve their own problems as needed. This allows users to gain new perspectives and ideas.

The system of the present invention provides users with a variety of solutions through these steps, supporting effective problem-solving.

(Example 1)

Next, Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

In today's increasingly diverse society, the challenges and problems users face are complex, making it difficult to provide effective solutions from a single perspective or approach. Furthermore, integrating knowledge from different cultural backgrounds and fields of expertise requires collaboration and cooperation among people with expertise in each field. However, the systems and methods that enable this are not well established, making it difficult for users to quickly obtain multifaceted solutions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for receiving any tasks or problems input by the user, means for temporarily storing the received tasks or problems in a database, means for analyzing the stored tasks or problems using a natural language processing engine and extracting categories and related keywords for the input tasks, means for utilizing multiple AI models to generate solutions from different cultural backgrounds and fields of expertise based on the analysis results, means for integrating the multiple generated solutions and formatting them before presenting them to the user, and means for displaying the formatted solutions to the user. This enables users to quickly obtain multifaceted and effective solutions.

"User" refers to the entity that uses the system to input issues and problems and receive solutions.

"Server" refers to a computer system that receives input from users, analyzes it, and generates and presents solutions.

"Challenges and problems" refer to specific situations or difficult circumstances that users are seeking a solution to.

"Means of receiving" refers to the function by which the server acquires and stores information entered by the user.

A "database" refers to an information storage system for temporarily storing and managing data such as issues and problems.

A "natural language processing engine" is a software module that analyzes human language and extracts categories and keywords for input issues or problems.

"Means of analysis" refers to the function of using a natural language processing engine to analyze received issues and problems and extract relevant information.

"AI model" refers to a model trained using artificial intelligence algorithms to solve a specific problem.

A "multicultural AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different cultural backgrounds.

A "multi-perspective AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different fields of expertise.

"Means for generating solutions" refers to the function of using multicultural AI models and multi-perspective AI models to generate specific solutions to problems.

"Means of integration" refers to the function for combining and shaping multiple generated solutions into one.

"Formatting tools" refers to the ability to adjust the format and appearance of a solution before presenting it to the user.

"Means of display" refers to the function of presenting the formatted solution to the user in an easy-to-read format.

The present invention relates to a system for providing multifaceted solutions to issues and problems entered by users. This system receives issues entered by users, stores them in a database, analyzes them using a natural language processing (NLP) engine, and generates solutions using multiple AI models based on the analysis results. It also integrates the generated solutions, formats them, and presents them to the user.

Users enter the problem they want to solve through a web browser. For example, they could enter a specific prompt such as, "I'm looking for new methods for sustainable agriculture."

Once the input is complete, the server receives it and temporarily stores it in the assignment database. It is recommended that a common relational database such as MySQL or PostgreSQL be used for this database. The saved assignment data is also assigned metadata such as a timestamp and user ID.

The server then retrieves the saved task data and analyzes it using an NLP engine. The NLP engine can use natural language processing libraries such as SpaCy or NLTK. Analysis steps include tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, for the task "Looking for new methods for sustainable agriculture," results such as "Category: Agriculture" and "Keywords: Sustainability, New Methods" can be obtained.

Based on the analysis results, the server generates solutions using a multicultural AI model and a multi-perspective AI model. These AI models provide solutions from different cultural backgrounds and areas of expertise. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

The generated solutions are then integrated and formatted by the server, which includes merging and formatting the text. The formatted solutions are then presented to the user through a web browser.

This allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling more effective problem-solving. For example, users can obtain specific solutions such as "sustainable methods based on African agricultural techniques" or "examples of applying vertical farming in urban areas."

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1: User enters assignment

Users enter the issues or problems they wish to solve through a web browser interface. For example, they might enter, "Please provide us with some innovative ideas for managing green spaces in urban areas." Once the information is complete, it is sent to the server via an HTML form.

Input: The prompt text entered by the user

Output: User input data is sent to the server.

Step 2: The server receives and stores the assignment.

The server receives HTTP requests and retrieves the assignment data submitted by the user. The retrieved data is temporarily stored in a relational database such as MySQL or PostgreSQL. Metadata such as timestamps and user IDs are also added to the data for later analysis.

Input: Issue data from the user

Output: Issue data stored in the database

Step 3: The server analyzes the issue.

The server retrieves the saved assignment data and analyzes it using an NLP engine such as SpaCy or NLTK. This analysis process includes tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, if the assignment "Looking for new methods for sustainable agriculture" is input, the NLP engine will extract "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Input: Issue data retrieved from the database

Output: Category and keyword information

Step 4: The server generates a solution.

The server uses a multicultural AI model and a multi-perspective AI model to generate solutions based on the analysis results. The analysis results are input as prompts into the multicultural AI model, which generates solutions from different cultural backgrounds. For example, it generates "Examples of green space management in European urban planning." The same analysis results are input as prompts into the multi-perspective AI model, which generates solutions based on specialized fields. For example, it generates "New methods for managing green spaces in urban areas using technology."

Input: Analysis results (category and keyword information)

Output: Multiple solutions

Step 5: The server integrates and presents the solution.

The server then integrates and formats the generated solutions. Specifically, it merges the solution text and formats it into a visually easy-to-read form. The formatted solution is then saved back into the database and displayed to the user via a web browser. The user can view multiple solutions and consider approaches to solving the problem from multiple perspectives.

Input: Multiple Solutions

Output: Consolidated and formatted solution

These are the main processing steps of this system. This allows users to quickly obtain solutions from a variety of perspectives, enabling more effective problem-solving.

(Application Example 1)

Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Existing solution-providing systems rely on a single cultural background or field of expertise, making it difficult to obtain solutions from diverse perspectives. They also lack the functionality to effectively analyze users' issues, extract related keywords, and generate and present solutions from multiple cultural and perspectives based on those keywords. For this reason, there is a need for systems that can quickly provide optimal solutions to users' problems.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes: means for receiving any task or problem entered by the user; means for analyzing the received task or problem; means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results; means for presenting the generated solution to the user; means including a natural language processing engine for analyzing the task entered by the user and extracting specific keywords; means for utilizing multiple artificial intelligence models to generate solutions from multicultural and multi-perspectives; means for integrating the generated solutions and optimally formatting them; and means for displaying the integrated solution on a display device. This allows the user to gain new perspectives from a variety of points of view, enabling them to quickly obtain multifaceted solutions.

Word definition

"User" refers to a person who uses the system to input issues and problems.

"Issue" refers to a problem or question that a user enters into the system and needs to be solved.

A "natural language processing engine" refers to a software component that analyzes input text and extracts specific keywords.

"Cultural background" refers to the collection of history, customs, values, etc. that exist in different regions and countries.

A "specialty" refers to an area that requires concentrated specific knowledge or skills.

"Solution" refers to specific measures or proposals provided to address users' challenges or problems.

An "artificial intelligence model" refers to an algorithm or system designed to perform a specific task based on training data.

"Format" refers to the standards and rules for arranging information in a particular form or structure.

"Display device" refers to a device for visually presenting the generated solution to the user.

A "multicultural perspective" refers to an approach to solving problems from the perspectives of various cultures.

"Multiple perspectives" refers to an approach that looks at a single issue from multiple different angles or perspectives.

Form for implementing the invention

The present invention is a system that provides multifaceted solutions to issues and problems entered by users. This system includes a process that receives and analyzes issues entered by users via devices such as smartphones, and generates and provides solutions from multiple cultural and perspectives to the user.

System Program

The system consists of the following components:

1. User device: A device such as a smartphone or tablet. Equipped with an interface for users to input tasks.

2. Server: Installed in a cloud environment, it performs the primary processing of analyzing received challenges and generating solutions. Specifically, it includes a database, a natural language processing engine, multiple artificial intelligence models, and software components for integrating and formatting solutions.

3. Display device: Present the solution visually on the user's terminal or other device.

Program processing explanation

The server receives assignments sent from user devices and temporarily stores them in a database. It then uses a natural language processing engine (e.g., the transformers library) to analyze the received assignments and extract specific keywords. This analysis process clarifies the assignment's category and related keywords.

Based on the analysis results, the server utilizes multiple artificial intelligence models to generate solutions from a multicultural and multi-perspective perspective. For example, one model generates solutions from a "multicultural perspective" and another model generates solutions from a "multi-perspective" perspective.

The generated solutions are then integrated and optimally formatted by the server. The formatted solution is then sent to the user's terminal's display device and presented to the user.

Specific examples

For example, if the problem "Sales are sluggish" is entered, a solution will be generated using the following steps:

Step 1:

The problem "Sales are sluggish" is entered from the user's device.

Step 2:

The server receives this challenge and analyzes it using a natural language processing engine. As a result of the analysis, the keywords "renewal case studies," "promotion," "display improvement," and "online marketing" are extracted.

Step 3:

The server generates solutions using multiple artificial intelligence models. For example, it generates "success stories in Japan" and "overseas promotion strategies" from a multicultural perspective, and "ways to improve displays" and "techniques for utilizing online marketing" from multiple perspectives.

Step 4:

These solutions are integrated, formatted optimally, and presented on the user's terminal display device.

Example prompt:

A user has entered the issue of "slumping sales." The following keywords have been extracted: "renewal case study," "promotion," "display improvement," and "online marketing." Based on this, please propose a solution from a multicultural and multi-perspective perspective.

In this way, the system of the present invention allows users to gain new perspectives from a variety of viewpoints and quickly obtain multifaceted solutions. This makes it possible to provide effective solutions to the problems users face.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Program processing steps

Step 1: User inputs assignment

Input: Users enter tasks using their smartphones or tablets.

Operation: The user's device enters the assignment through a web browser interface and sends the assignment to the system.

Output: The submitted assignment arrives at the server.

Step 2: Receive and save the assignment

Input: Assignment submitted from the user's device.

How it works: The server receives assignments submitted by users and temporarily stores them in a database.

Output: Issues saved in the database.

Step 3: Analyze the issue

Input: Issues saved in the database.

How it works: The server uses a natural language processing engine (e.g., the transformers library) to parse the incoming assignment. The parsing process extracts the assignment's category and associated keywords.

Output: Extracted keywords and categories.

Step 4: Generating Solutions - From a Multicultural Perspective

Input: Extracted keywords and categories.

How it works: The server uses an artificial intelligence model to generate solutions from a multicultural perspective. Based on keywords, it generates solutions from a multicultural perspective.

Output: Solutions from a multicultural perspective.

Step 5: Generate solutions - from multiple perspectives

Input: Extracted keywords and categories.

How it works: The server uses an artificial intelligence model to generate solutions from multiple perspectives. Based on keywords, it generates solutions from multiple perspectives.

Output: A solution from multiple perspectives.

Step 6: Integrate and format the solution

Input: Multicultural and multi-perspective solutions.

How it works: The server combines the multiple solutions generated and formats them into the most optimal format.

Output: Integrated solution.

Step 7: Present an integrated solution

Input: Integrated solution.

Operation: The server sends the integrated solution to the user's device. The user's device's display device visually displays it.

Output: The solution presented to the user.

Specific examples of behavior

Step 1:

The user enters the issue of "poor sales" into a smartphone app.

Step 2:

The user's device sends this assignment data to the server, which receives it and stores it in a database.

Step 3:

The server launches a natural language processing engine, analyzes the problem text, and extracts keywords such as "renewal case study," "promotion," "display improvement," and "online marketing."

Step 4:

The server runs an artificial intelligence model using the extracted keywords to generate solutions from a multicultural perspective, generating "success stories in Japan" and "overseas promotion strategies."

Step 5:

The server runs another artificial intelligence model using the extracted keywords to generate solutions from multiple perspectives, generating "ways to improve display" and "techniques for utilizing online marketing."

Step 6:

The server aggregates these solutions and formats them into a format that is easy for the user to understand.

Step 7:

The server sends the integrated solution to the user's terminal, which then visually displays it on its display device.

In this way, users can quickly and effectively access solutions from a variety of perspectives.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

The present invention provides a system that provides multifaceted solutions to issues and problems entered by users. This system combines an emotion engine to recognize the user's emotions and provide more optimized solutions.

User assignment entry

Users enter the issues or problems they want to solve through an interface on their web browser. The information entered by the user is sent to the server as an issue.

Receive and save assignments

The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

Analysis of the problem

The server uses a natural language processing (NLP) engine to analyze the received and stored assignments. This analysis process extracts the assignment's category and related keywords. For example, if the assignment "Looking for new methods for sustainable agriculture" is entered, the server extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Emotion recognition

The device uses an emotion engine to recognize the user's emotions. To do this, it analyzes the user's facial expressions, voice, and keyboard typing patterns when typing. The emotion engine extracts information such as whether the user is tense or relaxed.

Generating solutions

The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. As a specific example, a multicultural AI model and a multi-perspective AI model are used. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

Emotion-based adjustment

The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. It changes the tone and content of the proposed solutions depending on the user's emotions. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is relaxed, it will suggest new and creative ideas.

Integrating and presenting solutions

The server consolidates the generated solutions and organizes them into a coherent format. During this process, each solution is modified as needed and formatted for easy viewing. The organized solutions are then presented to the user. Presentation is done through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Specific examples

For example, here is a specific example of when the task entered is "I'm looking for new methods for sustainable agriculture."

The user enters the problem, which is received and stored by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The device uses an emotion engine to recognize the user's emotions and determines that the user is feeling anxious. The server generates solutions based on the analysis results using a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server adjusts the solutions to provide detailed and specific information to alleviate the user's anxiety. Finally, the server integrates these solutions, formats them in an easy-to-read format, and presents them to the user. The user can obtain specific and detailed solutions such as "Sustainable methods inspired by African agricultural technology" and "Examples of the application of vertical farming in urban areas."

Through these steps, the system of the present invention provides users with diverse and optimal solutions, and by adjusting according to the user's emotional state, supports more effective problem-solving.

The processing flow is explained below.

Step 1:

Users enter the issue or problem they want to solve through a web browser interface by entering the issue in the input field and clicking the submit button.

Step 2:

The server receives assignments submitted by users. The received assignments are temporarily stored in a database, ready for analysis.

Step 3:

The device uses an emotion engine to analyze the user's facial expressions, voice, keyboard typing patterns, etc., and extract emotional information about the user. For example, if the user is feeling anxious, the emotion engine will label it as "anxious."

Step 4:

The server invokes a natural language processing (NLP) engine to analyze the received and stored assignments. During this analysis process, the assignment's category and related keywords are extracted. For example, if the assignment "Looking for new methods for sustainable agriculture" is entered, "Category: Agriculture" and "Keywords: Sustainability, New Methods" are extracted.

Step 5:

The server generates solutions from multiple perspectives based on the analysis results and emotional information. Specific examples include a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Step 6:

The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. If the user is feeling anxious, it will provide detailed and specific solutions, and if the user is relaxed, it will adjust to suggest new and creative ideas.

Step 7:

The server then consolidates the generated solutions and formats them into a consistent format. During this process, each solution is modified and formatted for easy viewing.

Step 8:

The server presents the prepared solutions to the user through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Step 9:

Users can refer to the presented solutions and use them to solve their own problems as needed. This allows users to gain new perspectives and ideas.

Through these steps, the system of the present invention provides users with a variety of optimal solutions and makes adjustments based on the user's emotional state, thereby supporting more effective problem-solving.

(Example 2)

Next, Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Previous problem-solving systems did not present solutions that took the user's emotional state into consideration, and were unable to make suggestions that were optimized for the user's emotions. Furthermore, the presentation of solutions from different cultural backgrounds or fields of expertise based on analysis results was limited. As a result, the specific and multifaceted solutions that users desired were not adequately provided.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for recognizing the user's emotional state, means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results and the user's emotional state, and means for presenting the generated solutions to the user. This makes it possible to provide optimal solutions tailored to the user's emotions and present multifaceted solutions from different perspectives.

A "user" is a person or organization that uses the system to input issues or problems and receive solutions.

"Issues and problems" refer to the issues or problems that users want to solve through the system.

"Means of receiving" refers to the function that allows the system to receive issues and problems sent by users.

"Means of analysis" refers to the technology and algorithms used to analyze received issues and problems and understand their content.

"Means for recognizing emotional states" refers to technologies and algorithms for analyzing and judging a user's emotions.

"Different cultural backgrounds" refer to the cultures, customs, and values of different regions, countries, and societies.

A "specialty" refers to knowledge or skills specialized in a particular field or occupation.

"Means for generating solutions" is a function for generating appropriate solutions based on the analysis results and the user's emotional state.

"Presentation means" is a function for showing the generated solution to the user.

A "system" is a comprehensive device or software that performs a series of processes to receive and analyze a user's problem, and then generate and present a solution.

The present invention is a system that provides multifaceted and optimal solutions to issues and problems entered by a user. This system includes technology that recognizes the user's emotional state and adjusts solutions based on that. Specific embodiments of the present invention are described below.

System Configuration

User assignment input

Users enter the issues or problems they want to solve through a web browser interface. This interface consists of a simple web page with text input fields and a submit button. The information entered by the user is sent to the server as an HTTP request.

Receive and save assignments

The server receives assignments submitted by users and temporarily stores them in a database. This reception process is carried out using a common web server and database management system. For example, an Apache or Nginx server is used as the front end, and MySQL or PostgreSQL is used as the database.

Analysis of the problem

The server analyzes the received and stored assignments using a natural language processing (NLP) engine. For natural language processing, libraries such as OpenNLP or NLTK can be used. This engine is responsible for extracting assignment categories and related keywords.

Example:

If a user types "I'm looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture, Keywords: Sustainability, New Methods."

Emotion recognition

The device uses an emotion engine to recognize the user's emotions. The emotion engine can be, for example, Microsoft's Azure Cognitive Services or IBM's Watson. This engine analyzes the user's facial expressions, voice, and keyboard typing patterns when typing to determine the user's emotional state.

Example:

Based on the video and audio data when the user enters the task, the emotion engine returns "Emotion: Anxiety."

Generating solutions

The server uses multiple AI models to generate solutions based on the analysis results and the user's emotional state. Specific prompts are input to each AI model. For example, a generative AI model such as GPT-3 can be used.

Example:

Prompt for multicultural AI model: "Examples of agricultural technology applications in Africa"

Prompt for multi-perspective AI model: "The potential of vertical farming for urban agriculture."

Emotion-based adjustment

The server adjusts the generated solution based on the emotion recognition results. For example, if the user feels anxious, the solution will be changed to be more specific and detailed.

Example:

We will add specific procedures and pricing information to the "Examples of Application of Agricultural Technology in Africa" section and adjust the content to reduce user concerns.

Integrating and presenting solutions

The server then combines the generated solutions, formats them, and presents them to the user, for example by using HTML and CSS to display the solutions in an easy-to-read format on a web browser.

Example:

The server combines "Examples of agricultural technology applications in Africa" and "The potential for urban agriculture through vertical farming" into a report, and the solution is displayed when the user reloads the browser.

In this way, the system of the present invention provides optimal solutions tailored to the user's emotions and presents multifaceted solutions from different perspectives.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1: User inputs assignment

Users enter the issue or problem they want to solve through a web browser interface. This involves describing the issue in a text input field and clicking the "Submit" button.

Input: Text data describing the issue or problem (e.g., "I'm looking for new methods for sustainable agriculture.")

Output: Issue data sent to the server as an HTTP request

Step 2: Receive and save the assignment

The server receives the submitted assignment and stores it in a database. This step may also validate the format according to the database schema before saving the assignment.

Input: Issue data sent as an HTTP request

Output: Issue data stored in the database (e.g., "Issue ID: 12345, Content: Searching for new methods for sustainable agriculture")

Step 3: Analyze the issue

The server passes the received assignments to a natural language processing (NLP) engine for analysis. This analysis extracts the assignment's category and related keywords.

Input: Project data stored in the database (e.g., "Project ID: 12345, Content: Searching for new methods for sustainable agriculture")

Data processing and calculation: The NLP engine analyzes the text and extracts important categories and keywords.

Output: Extracted categories and keywords (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods")

Step 4: Recognize emotions

The device uses an emotion engine to analyze the user's emotional state. In this step, the user's facial expressions, voice, and keyboard typing patterns are used as input data.

Input: User facial expression data, voice data, keyboard keystroke data

Data processing and calculation: The emotion engine analyzes this data to identify the user's emotional state.

Output: User's emotional state (e.g., "Emotion: Anxiety")

Step 5: Generate a solution

The server uses multiple AI models to generate solutions based on the analysis results and emotional state. In this step, an appropriate prompt sentence is entered into each AI model to generate a solution.

Input: Analysis results and emotional state (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods, Emotion: Anxiety")

Data processing and data calculation: Input a prompt statement into each AI model and generate a solution based on it.

Output: Generated solutions (e.g., "Application of agricultural technology in Africa" and "Possibilities for urban agriculture through vertical farming")

Step 6: Emotional Adjustment

The server adjusts the solution based on the user's emotional information recognized by the emotion engine. If the user is feeling anxious, the solution will be made more specific and detailed.

Input: User's emotional state and generated solution

Data processing and calculation: Modify and adjust parts of the solution based on emotional information.

Output: Tailored solution (e.g., "Example of agricultural technology application in Africa - specific procedures and pricing information added")

Step 7: Integrate and present the solution

The server consolidates multiple solutions, formats them, and presents them to the user, who then sees them in an easy-to-read format displayed in their web browser.

Input: Tailored solutions (e.g., "Applications of agricultural technology in Africa," "Potential for urban agriculture through vertical farming")

Data processing and calculation: Integrate solutions and format using HTML and CSS.

Output: The integrated solution displayed in the user's browser.

This completes the entire system's processing, allowing users to see a multifaceted, emotionally optimized solution.

(Application Example 2)

Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional systems can provide multifaceted solutions to problems entered by users, but because they do not optimize with user emotions in mind, it is difficult to present more appropriate solutions to individual users. Furthermore, there is a lack of methods for tailoring appropriate solutions based on the user's emotional state and providing personalized information. Therefore, in order to increase user satisfaction, a system that recognizes the user's emotions and adjusts solutions based on them is needed.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results, means for recognizing the user's emotions, means for adjusting the generated solution based on the user's emotions, and means for presenting the generated solution to the user. This makes it possible to provide personalized solutions according to the user's emotional state.

"Means for receiving any issues or problems entered by the user" refers to an interface that receives data such as text, audio, and images entered by the user and sends it to the server.

"Means for analyzing received issues and problems" refers to analytical functions such as natural language processing engines that analyze received data and extract relevant keywords and categories.

"Means for generating solutions from different cultural backgrounds and fields of expertise" refers to a function for generating solutions from various cultures and fields of expertise using multicultural AI models and multi-perspective AI models.

"Means for recognizing user emotions" refers to an emotion engine that analyzes the user's facial expressions, voice, input patterns, etc. to recognize their emotional state.

"Means for adjusting generated solutions based on the user's emotions" is a function for appropriately adjusting the tone and content of solutions according to the user's emotional state recognized by the emotion engine.

"Means for presenting the generated solution to the user" refers to an interface for arranging the adjusted solution in an easy-to-view format and displaying it to the user.

The present invention is a system that provides multifaceted solutions to issues and problems entered by the user. In particular, it includes a function that recognizes the user's emotions and adjusts the solutions accordingly. The system's components include the following elements:

First, a user interface on a smartphone or PC is used as a means of receiving any assignments or problems entered by the user. Through this interface, the user inputs the assignments using text or voice. The received assignments and problems are then sent to the server.

Next, a server is installed as a means of analyzing the received issues and problems. This server uses a natural language processing (NLP) engine to analyze the issues and extract related keywords and categories. For example, if a user types "I want to know about recent environmental issues," the server will extract "Category: Environment" and "Keywords: Recent Environmental Issues."

Furthermore, the server uses multicultural AI models and multi-perspective AI models as a means of generating solutions from different cultural backgrounds and fields of expertise. These multiple AI models generate solutions from different perspectives and address user issues from multiple angles.

The device is also equipped with an emotion engine to recognize the user's emotions. This makes it possible to analyze the user's emotional state from their facial expressions, voice, keystroke patterns, etc. For example, it can recognize whether the user is excited or anxious.

The server also has the means to adjust the generated solutions based on the user's emotions. It adjusts the content and tone of the solutions depending on the user's recognized emotional state. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if they are relaxed, it will suggest more creative ideas.

Finally, to present the generated solutions to the user, the server formats the solutions in an easy-to-read format and presents them to the user through a user interface. This allows the user to view solutions from different perspectives in a list format.

As a concrete example, consider the case where a user types, "What environmental issues are you interested in recently? Please tell me in detail." In this case, the server analyzes the relevant data, and the emotion engine analyzes the user's emotional state. Next, using the multicultural AI model and multi-perspective AI model, it generates solutions such as "The latest research on global warming" or "The current state and future of plastic pollution." These solutions are then adjusted to appeal to the user's interest and proposed to them through the user interface.

In this way, the present invention can analyze the issues and problems entered by the user from multiple angles, provide optimal solutions from different cultures and fields of expertise, and even adjust them according to the user's emotional state.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Receives any issues or problems entered by users.

Specifically, users input tasks via text or voice through a user interface on their smartphone or computer. This input data (tasks or problems) becomes input data for the system. At this stage, the input data is sent to the server, which receives it.

Step 2:

Analyze received issues and problems.

The server uses a natural language processing (NLP) engine to analyze the input data received from the user. The data to be analyzed is the text and voice data entered by the user. Specifically, this analysis process extracts related keywords and categories. For example, if the input is "I want to know about recent environmental issues," "Category: Environment" and "Keywords: Recent Environmental Issues" will be extracted.

Step 3:

Generate solutions from different cultural backgrounds and disciplines.

The server generates solutions from different cultural backgrounds and fields of expertise based on the extracted keywords and categories. To do this, it uses multicultural AI models and multi-perspective AI models. For example, based on "Category: Environment" and "Keywords: Recent Environmental Issues," it generates solutions such as "Latest research on global warming" and "The current state and future of plastic pollution."

Step 4:

Recognize user emotions.

An emotion engine installed on the user's device analyzes the user's facial expressions, voice, and input patterns to recognize their emotional state. This data includes facial recognition data, voice data, and typing patterns. For example, it recognizes whether the user is feeling anxious or excited. This emotional information becomes input data for adjusting the solution generated in the next step.

Step 5:

Adjust the generated solutions based on the user's sentiment.

The server adjusts the content and tone of the generated solution based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is feeling relaxed, it will suggest creative ideas. In this process, emotional data and solution data are input, and adjusted solution data is output.

Step 6:

Present the generated solution to the user.

The server then formats the adjusted solutions in an easy-to-read format and presents them to the user through a user interface. In this step, the formatted solution data is input and display data for the user is output. Specifically, the solutions are presented in a list format on the web browser, allowing the user to review them. For example, "Recent research on global warming" and "The current state and future of plastic pollution" are displayed.

The specific processing unit 290 transmits the results of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). Examples of data generation model 58 include generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>) and Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the headset-type terminal 314.

[Fourth embodiment]

Figure 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

As shown in FIG. 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a control target 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and control target 443 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

The present invention is a system that provides multifaceted solutions to issues and problems entered by users. This system works through a process of receiving issues, analyzing them, generating solutions from different cultural backgrounds and areas of expertise, and presenting them to the user.

User assignment entry

Users enter the issues or problems they want to solve through an interface on their web browser. The information entered by the user is sent to the server as an issue.

Receive and save assignments

The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

Analysis of the problem

The server analyzes the received assignment using a natural language processing (NLP) engine. This analysis process extracts the category and related keywords of the input assignment. For example, if the input assignment is "Looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Generating solutions

The server utilizes multiple AI models based on the analysis results to generate solutions from multiple perspectives. Specifically, it generates solutions using a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Integrating and presenting solutions

The server then integrates the generated solutions and formats them before presenting them to the user. The integrated solution is displayed to the user through a web browser interface, allowing the user to view solutions from a variety of perspectives.

Specific examples

For example, here is a specific example of when the task entered is "I'm looking for new methods for sustainable agriculture."

The user enters a problem, which is received and saved by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The server generates solutions based on the analysis results, utilizing a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," and the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server integrates these solutions and presents them to the user in the optimal format. Ultimately, the user is able to obtain solutions such as "Sustainable methods inspired by African agricultural technology" and "Examples of the application of vertical farming in urban areas."

The system of the present invention allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling effective problem-solving.

The processing flow is explained below.

Step 1:

Users enter the issue or problem they want to solve through a web browser interface. By entering the issue in the input field and clicking the submit button, the issue is sent to the system.

Step 2:

The server receives assignments sent by users. The received assignments are temporarily stored in a database. This storage process is for later analysis.

Step 3:

The server invokes a natural language processing (NLP) engine to analyze the received and saved assignments. This analysis process extracts the assignment's category and related keywords. For example, for the assignment "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Step 4:

The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. As a specific example, a multicultural AI model and a multi-perspective AI model are used. The multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Step 5:

The server then combines the generated solutions into a consistent format. During this process, each solution is modified as needed and formatted for easy viewing.

Step 6:

The server presents the prepared solutions to the user through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Step 7:

Users can refer to the presented solutions and use them to solve their own problems as needed. This allows users to gain new perspectives and ideas.

The system of the present invention provides users with a variety of solutions through these steps, supporting effective problem-solving.

(Example 1)

Next, Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

In today's increasingly diverse society, the challenges and problems users face are complex, making it difficult to provide effective solutions from a single perspective or approach. Furthermore, integrating knowledge from different cultural backgrounds and fields of expertise requires collaboration and cooperation among people with expertise in each field. However, the systems and methods that enable this are not well established, making it difficult for users to quickly obtain multifaceted solutions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for receiving any tasks or problems input by the user, means for temporarily storing the received tasks or problems in a database, means for analyzing the stored tasks or problems using a natural language processing engine and extracting categories and related keywords for the input tasks, means for utilizing multiple AI models to generate solutions from different cultural backgrounds and fields of expertise based on the analysis results, means for integrating the multiple generated solutions and formatting them before presenting them to the user, and means for displaying the formatted solutions to the user. This enables users to quickly obtain multifaceted and effective solutions.

"User" refers to the entity that uses the system to input issues and problems and receive solutions.

"Server" refers to a computer system that receives input from users, analyzes it, and generates and presents solutions.

"Challenges and problems" refer to specific situations or difficult circumstances that users are seeking a solution to.

"Means of receiving" refers to the function by which the server acquires and stores information entered by the user.

A "database" refers to an information storage system for temporarily storing and managing data such as issues and problems.

A "natural language processing engine" is a software module that analyzes human language and extracts categories and keywords for input issues or problems.

"Means of analysis" refers to the function of using a natural language processing engine to analyze received issues and problems and extract relevant information.

"AI model" refers to a model trained using artificial intelligence algorithms to solve a specific problem.

A "multicultural AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different cultural backgrounds.

A "multi-perspective AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different fields of expertise.

"Means for generating solutions" refers to the function of using multicultural AI models and multi-perspective AI models to generate specific solutions to problems.

"Means of integration" refers to the function for combining and shaping multiple generated solutions into one.

"Formatting tools" refers to the ability to adjust the format and appearance of a solution before presenting it to the user.

"Means of display" refers to the function of presenting the formatted solution to the user in an easy-to-read format.

The present invention relates to a system for providing multifaceted solutions to issues and problems entered by users. This system receives issues entered by users, stores them in a database, analyzes them using a natural language processing (NLP) engine, and generates solutions using multiple AI models based on the analysis results. It also integrates the generated solutions, formats them, and presents them to the user.

Users enter the problem they want to solve through a web browser. For example, they could enter a specific prompt such as, "I'm looking for new methods for sustainable agriculture."

Once the input is complete, the server receives it and temporarily stores it in the assignment database. It is recommended that a common relational database such as MySQL or PostgreSQL be used for this database. The saved assignment data is also assigned metadata such as a timestamp and user ID.

The server then retrieves the saved task data and analyzes it using an NLP engine. The NLP engine can use natural language processing libraries such as SpaCy or NLTK. Analysis steps include tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, for the task "Looking for new methods for sustainable agriculture," results such as "Category: Agriculture" and "Keywords: Sustainability, New Methods" can be obtained.

Based on the analysis results, the server generates solutions using a multicultural AI model and a multi-perspective AI model. These AI models provide solutions from different cultural backgrounds and areas of expertise. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

The generated solutions are then integrated and formatted by the server, which includes merging and formatting the text. The formatted solutions are then presented to the user through a web browser.

This allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling more effective problem-solving. For example, users can obtain specific solutions such as "sustainable methods based on African agricultural techniques" or "examples of applying vertical farming in urban areas."

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1: User enters assignment

Users enter the issues or problems they wish to solve through a web browser interface. For example, they might enter, "Please provide us with some innovative ideas for managing green spaces in urban areas." Once the information is complete, it is sent to the server via an HTML form.

Input: The prompt text entered by the user

Output: User input data is sent to the server.

Step 2: The server receives and stores the assignment.

The server receives HTTP requests and retrieves the assignment data submitted by the user. The retrieved data is temporarily stored in a relational database such as MySQL or PostgreSQL. Metadata such as timestamps and user IDs are also added to the data for later analysis.

Input: Issue data from the user

Output: Issue data stored in the database

Step 3: The server analyzes the issue.

The server retrieves the saved assignment data and analyzes it using an NLP engine such as SpaCy or NLTK. This analysis process includes tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, if the assignment "Looking for new methods for sustainable agriculture" is input, the NLP engine will extract "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Input: Issue data retrieved from the database

Output: Category and keyword information

Step 4: The server generates a solution.

The server uses a multicultural AI model and a multi-perspective AI model to generate solutions based on the analysis results. The analysis results are input as prompts into the multicultural AI model, which generates solutions from different cultural backgrounds. For example, it generates "Examples of green space management in European urban planning." The same analysis results are input as prompts into the multi-perspective AI model, which generates solutions based on specialized fields. For example, it generates "New methods for managing green spaces in urban areas using technology."

Input: Analysis results (category and keyword information)

Output: Multiple solutions

Step 5: The server integrates and presents the solution.

The server then integrates and formats the generated solutions. Specifically, it merges the solution text and formats it into a visually easy-to-read form. The formatted solution is then saved back into the database and displayed to the user via a web browser. The user can view multiple solutions and consider approaches to solving the problem from multiple perspectives.

Input: Multiple Solutions

Output: Consolidated and formatted solution

These are the main processing steps of this system. This allows users to quickly obtain solutions from a variety of perspectives, enabling more effective problem-solving.

(Application Example 1)

Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Existing solution-providing systems rely on a single cultural background or field of expertise, making it difficult to obtain solutions from diverse perspectives. They also lack the functionality to effectively analyze users' issues, extract related keywords, and generate and present solutions from multiple cultural and perspectives based on those keywords. For this reason, there is a need for systems that can quickly provide optimal solutions to users' problems.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes: means for receiving any task or problem entered by the user; means for analyzing the received task or problem; means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results; means for presenting the generated solution to the user; means including a natural language processing engine for analyzing the task entered by the user and extracting specific keywords; means for utilizing multiple artificial intelligence models to generate solutions from multicultural and multi-perspectives; means for integrating the generated solutions and optimally formatting them; and means for displaying the integrated solution on a display device. This allows the user to gain new perspectives from a variety of points of view, enabling them to quickly obtain multifaceted solutions.

Word definition

"User" refers to a person who uses the system to input issues and problems.

"Issue" refers to a problem or question that a user enters into the system and needs to be solved.

A "natural language processing engine" refers to a software component that analyzes input text and extracts specific keywords.

"Cultural background" refers to the collection of history, customs, values, etc. that exist in different regions and countries.

A "specialty" refers to an area that requires concentrated specific knowledge or skills.

"Solution" refers to specific measures or proposals provided to address users' challenges or problems.

An "artificial intelligence model" refers to an algorithm or system designed to perform a specific task based on training data.

"Format" refers to the standards and rules for arranging information in a particular form or structure.

"Display device" refers to a device for visually presenting the generated solution to the user.

A "multicultural perspective" refers to an approach to solving problems from the perspectives of various cultures.

"Multiple perspectives" refers to an approach that looks at a single issue from multiple different angles or perspectives.

Form for implementing the invention

The present invention is a system that provides multifaceted solutions to issues and problems entered by users. This system includes a process that receives and analyzes issues entered by users via devices such as smartphones, and generates and provides solutions from multiple cultural and perspectives to the user.

System Program

The system consists of the following components:

1. User device: A device such as a smartphone or tablet. Equipped with an interface for users to input tasks.

2. Server: Installed in a cloud environment, it performs the primary processing of analyzing received challenges and generating solutions. Specifically, it includes a database, a natural language processing engine, multiple artificial intelligence models, and software components for integrating and formatting solutions.

3. Display device: Present the solution visually on the user's terminal or other device.

Program processing explanation

The server receives assignments sent from user devices and temporarily stores them in a database. It then uses a natural language processing engine (e.g., the transformers library) to analyze the received assignments and extract specific keywords. This analysis process clarifies the assignment's category and related keywords.

Based on the analysis results, the server utilizes multiple artificial intelligence models to generate solutions from a multicultural and multi-perspective perspective. For example, one model generates solutions from a "multicultural perspective" and another model generates solutions from a "multi-perspective" perspective.

The generated solutions are then integrated and optimally formatted by the server. The formatted solution is then sent to the user's terminal's display device and presented to the user.

Specific examples

For example, if the problem "Sales are sluggish" is entered, a solution will be generated using the following steps:

Step 1:

The problem "Sales are sluggish" is entered from the user's device.

Step 2:

The server receives this challenge and analyzes it using a natural language processing engine. As a result of the analysis, the keywords "renewal case studies," "promotion," "display improvement," and "online marketing" are extracted.

Step 3:

The server generates solutions using multiple artificial intelligence models. For example, it generates "success stories in Japan" and "overseas promotion strategies" from a multicultural perspective, and "ways to improve displays" and "techniques for utilizing online marketing" from multiple perspectives.

Step 4:

These solutions are integrated, formatted optimally, and presented on the user's terminal display device.

Example prompt:

A user has entered the issue of "slumping sales." The following keywords have been extracted: "renewal case study," "promotion," "display improvement," and "online marketing." Based on this, please propose a solution from a multicultural and multi-perspective perspective.

In this way, the system of the present invention allows users to gain new perspectives from a variety of viewpoints and quickly obtain multifaceted solutions. This makes it possible to provide effective solutions to the problems users face.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Program processing steps

Step 1: User inputs assignment

Input: Users enter tasks using their smartphones or tablets.

Operation: The user's device enters the assignment through a web browser interface and sends the assignment to the system.

Output: The submitted assignment arrives at the server.

Step 2: Receive and save the assignment

Input: Assignment submitted from the user's device.

How it works: The server receives assignments submitted by users and temporarily stores them in a database.

Output: Issues saved in the database.

Step 3: Analyze the issue

Input: Issues saved in the database.

How it works: The server uses a natural language processing engine (e.g., the transformers library) to parse the incoming assignment. The parsing process extracts the assignment's category and associated keywords.

Output: Extracted keywords and categories.

Step 4: Generating Solutions - From a Multicultural Perspective

Input: Extracted keywords and categories.

How it works: The server uses an artificial intelligence model to generate solutions from a multicultural perspective. Based on keywords, it generates solutions from a multicultural perspective.

Output: Solutions from a multicultural perspective.

Step 5: Generate solutions - from multiple perspectives

Input: Extracted keywords and categories.

How it works: The server uses an artificial intelligence model to generate solutions from multiple perspectives. Based on keywords, it generates solutions from multiple perspectives.

Output: A solution from multiple perspectives.

Step 6: Integrate and format the solution

Input: Multicultural and multi-perspective solutions.

How it works: The server combines the multiple solutions generated and formats them into the most optimal format.

Output: Integrated solution.

Step 7: Present an integrated solution

Input: Integrated solution.

Operation: The server sends the integrated solution to the user's device. The user's device's display device visually displays it.

Output: The solution presented to the user.

Specific examples of behavior

Step 1:

The user enters the issue of "poor sales" into a smartphone app.

Step 2:

The user's device sends this assignment data to the server, which receives it and stores it in a database.

Step 3:

The server launches a natural language processing engine, analyzes the problem text, and extracts keywords such as "renewal case study," "promotion," "display improvement," and "online marketing."

Step 4:

The server runs an artificial intelligence model using the extracted keywords to generate solutions from a multicultural perspective, generating "success stories in Japan" and "overseas promotion strategies."

Step 5:

The server runs another artificial intelligence model using the extracted keywords to generate solutions from multiple perspectives, generating "ways to improve display" and "techniques for utilizing online marketing."

Step 6:

The server aggregates these solutions and formats them into a format that is easy for the user to understand.

Step 7:

The server sends the integrated solution to the user's terminal, which then visually displays it on its display device.

In this way, users can quickly and effectively access solutions from a variety of perspectives.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

The present invention provides a system that provides multifaceted solutions to issues and problems entered by users. This system combines an emotion engine to recognize the user's emotions and provide more optimized solutions.

User assignment entry

Users enter the issues or problems they want to solve through an interface on their web browser. The information entered by the user is sent to the server as an issue.

Receive and save assignments

The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

Analysis of the problem

The server uses a natural language processing (NLP) engine to analyze the received and stored assignments. This analysis process extracts the assignment's category and related keywords. For example, if the assignment "Looking for new methods for sustainable agriculture" is entered, the server extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

Emotion recognition

The device uses an emotion engine to recognize the user's emotions. To do this, it analyzes the user's facial expressions, voice, and keyboard typing patterns when typing. The emotion engine extracts information such as whether the user is tense or relaxed.

Generating solutions

The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. As a specific example, a multicultural AI model and a multi-perspective AI model are used. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

Emotion-based adjustment

The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. It changes the tone and content of the proposed solutions depending on the user's emotions. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is relaxed, it will suggest new and creative ideas.

Integrating and presenting solutions

The server consolidates the generated solutions and organizes them into a coherent format. During this process, each solution is modified as needed and formatted for easy viewing. The organized solutions are then presented to the user. Presentation is done through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Specific examples

For example, here is a specific example of when the task entered is "I'm looking for new methods for sustainable agriculture."

The user enters the problem, which is received and stored by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The device uses an emotion engine to recognize the user's emotions and determines that the user is feeling anxious. The server generates solutions based on the analysis results using a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," while the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server adjusts the solutions to provide detailed and specific information to alleviate the user's anxiety. Finally, the server integrates these solutions, formats them in an easy-to-read format, and presents them to the user. The user can obtain specific and detailed solutions such as "Sustainable methods inspired by African agricultural technology" and "Examples of the application of vertical farming in urban areas."

Through these steps, the system of the present invention provides users with diverse and optimal solutions, and by adjusting according to the user's emotional state, supports more effective problem-solving.

The processing flow is explained below.

Step 1:

Users enter the issue or problem they want to solve through a web browser interface by entering the issue in the input field and clicking the submit button.

Step 2:

The server receives assignments submitted by users. The received assignments are temporarily stored in a database, ready for analysis.

Step 3:

The device uses an emotion engine to analyze the user's facial expressions, voice, keyboard typing patterns, etc. to extract emotional information about the user. For example, if the user is feeling anxious, the emotion engine will label it as "anxious."

Step 4:

The server invokes a natural language processing (NLP) engine to analyze the received and stored assignments. During this analysis process, the assignment's category and related keywords are extracted. For example, if the assignment "Looking for new methods for sustainable agriculture" is entered, "Category: Agriculture" and "Keywords: Sustainability, New Methods" are extracted.

Step 5:

The server generates solutions from multiple perspectives based on the analysis results and emotional information. Specific examples include a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

Step 6:

The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. If the user is feeling anxious, it will provide detailed and specific solutions, and if the user is relaxed, it will adjust to suggest new and creative ideas.

Step 7:

The server then consolidates the generated solutions and formats them into a consistent format. During this process, each solution is modified and formatted for easy viewing.

Step 8:

The server presents the prepared solutions to the user through a web browser interface, allowing the user to view a list of solutions from different perspectives.

Step 9:

Users can refer to the presented solutions and use them to solve their own problems as needed. This allows users to gain new perspectives and ideas.

Through these steps, the system of the present invention provides users with a variety of optimal solutions and makes adjustments based on the user's emotional state, thereby supporting more effective problem-solving.

(Example 2)

Next, Example 2 will be described. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

Previous problem-solving systems did not present solutions that took the user's emotional state into consideration, and were unable to make suggestions that were optimized for the user's emotions. Furthermore, the presentation of solutions from different cultural backgrounds or fields of expertise based on analysis results was limited. As a result, the specific and multifaceted solutions that users desired were not adequately provided.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for recognizing the user's emotional state, means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results and the user's emotional state, and means for presenting the generated solutions to the user. This makes it possible to provide optimal solutions tailored to the user's emotions and present multifaceted solutions from different perspectives.

A "user" is a person or organization that uses the system to input issues or problems and receive solutions.

"Issues and problems" refer to the issues or problems that users want to solve through the system.

"Means of receiving" refers to the function that allows the system to receive issues and problems sent by users.

"Means of analysis" refers to the technology and algorithms used to analyze received issues and problems and understand their content.

"Means for recognizing emotional states" refers to technologies and algorithms for analyzing and judging a user's emotions.

"Different cultural backgrounds" refer to the cultures, customs, and values of different regions, countries, and societies.

A "specialty" refers to knowledge or skills specialized in a particular field or occupation.

"Means for generating solutions" is a function for generating appropriate solutions based on the analysis results and the user's emotional state.

"Presentation means" is a function for showing the generated solution to the user.

A "system" is a comprehensive device or software that performs a series of processes to receive and analyze a user's problem, and then generate and present a solution.

The present invention is a system that provides multifaceted and optimal solutions to issues and problems entered by a user. This system includes technology that recognizes the user's emotional state and adjusts solutions based on that. Specific embodiments of the present invention are described below.

System Configuration

User assignment input

Users enter the issues or problems they want to solve through a web browser interface. This interface consists of a simple web page with text input fields and a submit button. The information entered by the user is sent to the server as an HTTP request.

Receive and save assignments

The server receives assignments submitted by users and temporarily stores them in a database. This reception process is carried out using a common web server and database management system. For example, an Apache or Nginx server is used as the front end, and MySQL or PostgreSQL is used as the database.

Analysis of the problem

The server analyzes the received and stored assignments using a natural language processing (NLP) engine. For natural language processing, libraries such as OpenNLP or NLTK can be used. This engine is responsible for extracting assignment categories and related keywords.

Example:

If a user types "I'm looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture, Keywords: Sustainability, New Methods."

Emotion recognition

The device uses an emotion engine to recognize the user's emotions. The emotion engine can be, for example, Microsoft's Azure Cognitive Services or IBM's Watson. This engine analyzes the user's facial expressions, voice, and keyboard typing patterns when typing to determine the user's emotional state.

Example:

Based on the video and audio data when the user enters the task, the emotion engine returns "Emotion: Anxiety."

Generating solutions

The server uses multiple AI models to generate solutions based on the analysis results and the user's emotional state. Specific prompts are input to each AI model. For example, a generative AI model such as GPT-3 can be used.

Example:

Prompt for multicultural AI model: "Examples of agricultural technology applications in Africa"

Prompt for multi-perspective AI model: "The potential of vertical farming for urban agriculture."

Emotion-based adjustment

The server adjusts the generated solution based on the emotion recognition results. For example, if the user feels anxious, the solution will be changed to be more specific and detailed.

Example:

We will add specific procedures and pricing information to the "Examples of Application of Agricultural Technology in Africa" section and adjust the content to reduce user concerns.

Integrating and presenting solutions

The server then combines the generated solutions, formats them, and presents them to the user, for example by using HTML and CSS to display the solutions in an easy-to-read format on a web browser.

Example:

The server combines "Examples of agricultural technology applications in Africa" and "The potential for urban agriculture through vertical farming" into a report, and the solution is displayed when the user reloads the browser.

In this way, the system of the present invention provides optimal solutions tailored to the user's emotions and presents multifaceted solutions from different perspectives.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1: User inputs assignment

Users enter the issue or problem they want to solve through a web browser interface. This involves describing the issue in a text input field and clicking the "Submit" button.

Input: Text data describing the issue or problem (e.g., "I'm looking for new methods for sustainable agriculture.")

Output: Issue data sent to the server as an HTTP request

Step 2: Receive and save the assignment

The server receives the submitted assignment and stores it in a database. This step may also validate the format according to the database schema before saving the assignment.

Input: Issue data sent as an HTTP request

Output: Issue data stored in the database (e.g., "Issue ID: 12345, Content: Searching for new methods for sustainable agriculture")

Step 3: Analyze the issue

The server passes the received assignments to a natural language processing (NLP) engine for analysis. This analysis extracts the assignment's category and related keywords.

Input: Project data stored in the database (e.g., "Project ID: 12345, Content: Searching for new methods for sustainable agriculture")

Data processing and calculation: The NLP engine analyzes the text and extracts important categories and keywords.

Output: Extracted categories and keywords (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods")

Step 4: Recognize emotions

The device uses an emotion engine to analyze the user's emotional state. In this step, the user's facial expressions, voice, keyboard typing patterns, etc. are used as input data.

Input: User facial expression data, voice data, keyboard keystroke data

Data processing and calculation: The emotion engine analyzes this data to identify the user's emotional state.

Output: User's emotional state (e.g., "Emotion: Anxiety")

Step 5: Generate a solution

The server uses multiple AI models to generate solutions based on the analysis results and emotional state. In this step, an appropriate prompt sentence is entered into each AI model to generate a solution.

Input: Analysis results and emotional state (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods, Emotion: Anxiety")

Data processing and data calculation: Input a prompt statement into each AI model and generate a solution based on it.

Output: Generated solutions (e.g., "Application of agricultural technology in Africa" and "Possibilities for urban agriculture through vertical farming")

Step 6: Emotional Adjustment

The server adjusts the solution based on the user's emotional information recognized by the emotion engine. If the user is feeling anxious, the solution will be made more specific and detailed.

Input: User's emotional state and generated solution

Data processing and calculation: Modify and adjust parts of the solution based on emotional information.

Output: Tailored solution (e.g., "Example of agricultural technology application in Africa - specific procedures and pricing information added")

Step 7: Integrate and present the solution

The server consolidates multiple solutions, formats them, and presents them to the user, who then sees them in an easy-to-read format displayed in their web browser.

Input: Tailored solutions (e.g., "Applications of agricultural technology in Africa," "Potential for urban agriculture through vertical farming")

Data processing and calculation: Integrate solutions and format using HTML and CSS.

Output: The integrated solution displayed in the user's browser.

This completes the entire system's processing, allowing users to see a multifaceted, emotionally optimized solution.

(Application Example 2)

Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional systems can provide multifaceted solutions to problems entered by users, but because they do not optimize with user emotions in mind, it is difficult to present more appropriate solutions to individual users. Furthermore, there is a lack of methods for tailoring appropriate solutions based on the user's emotional state and providing personalized information. Therefore, in order to increase user satisfaction, a system that recognizes the user's emotions and adjusts solutions based on them is needed.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results, means for recognizing the user's emotions, means for adjusting the generated solution based on the user's emotions, and means for presenting the generated solution to the user. This makes it possible to provide personalized solutions according to the user's emotional state.

"Means for receiving any issues or problems entered by the user" refers to an interface that receives data such as text, audio, and images entered by the user and sends it to the server.

"Means for analyzing received issues and problems" refers to analytical functions such as natural language processing engines that analyze received data and extract relevant keywords and categories.

"Means for generating solutions from different cultural backgrounds and fields of expertise" refers to a function for generating solutions from various cultures and fields of expertise using multicultural AI models and multi-perspective AI models.

"Means for recognizing user emotions" refers to an emotion engine that analyzes the user's facial expressions, voice, input patterns, etc. to recognize their emotional state.

"Means for adjusting generated solutions based on the user's emotions" is a function for appropriately adjusting the tone and content of solutions according to the user's emotional state recognized by the emotion engine.

"Means for presenting the generated solution to the user" refers to an interface for arranging the adjusted solution in an easy-to-view format and displaying it to the user.

The present invention is a system that provides multifaceted solutions to issues and problems entered by the user. In particular, it includes a function that recognizes the user's emotions and adjusts the solutions accordingly. The system's components include the following elements:

First, a user interface on a smartphone or PC is used as a means of receiving any assignments or problems entered by the user. Through this interface, the user inputs the assignments using text or voice. The received assignments and problems are then sent to the server.

Next, a server is installed as a means of analyzing the received issues and problems. This server uses a natural language processing (NLP) engine to analyze the issues and extract related keywords and categories. For example, if a user types "I want to know about recent environmental issues," the server will extract "Category: Environment" and "Keywords: Recent Environmental Issues."

Furthermore, the server uses multicultural AI models and multi-perspective AI models as a means of generating solutions from different cultural backgrounds and fields of expertise. These multiple AI models generate solutions from different perspectives and address user issues from multiple angles.

The device is also equipped with an emotion engine to recognize the user's emotions. This makes it possible to analyze the user's emotional state from their facial expressions, voice, keystroke patterns, etc. For example, it can recognize whether the user is excited or anxious.

The server also has the means to adjust the generated solutions based on the user's emotions. It adjusts the content and tone of the solutions depending on the user's recognized emotional state. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if they are relaxed, it will suggest more creative ideas.

Finally, to present the generated solutions to the user, the server formats the solutions in an easy-to-read format and presents them to the user through a user interface. This allows the user to view solutions from different perspectives in a list format.

As a concrete example, consider the case where a user types, "What environmental issues are you interested in recently? Please tell me in detail." In this case, the server analyzes the relevant data, and the emotion engine analyzes the user's emotional state. Next, using the multicultural AI model and multi-perspective AI model, it generates solutions such as "The latest research on global warming" or "The current state and future of plastic pollution." These solutions are then adjusted to appeal to the user's interest and proposed to them through the user interface.

In this way, the present invention can analyze the issues and problems entered by the user from multiple angles, provide optimal solutions from different cultures and fields of expertise, and even adjust them according to the user's emotional state.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Receives any issues or problems entered by users.

Specifically, users input tasks via text or voice through a user interface on their smartphone or computer. This input data (tasks or problems) becomes input data for the system. At this stage, the input data is sent to the server, which receives it.

Step 2:

Analyze received issues and problems.

The server uses a natural language processing (NLP) engine to analyze the input data received from the user. The data to be analyzed is the text and voice data entered by the user. Specifically, this analysis process extracts related keywords and categories. For example, if the input is "I want to know about recent environmental issues," "Category: Environment" and "Keywords: Recent Environmental Issues" will be extracted.

Step 3:

Generate solutions from different cultural backgrounds and disciplines.

The server generates solutions from different cultural backgrounds and fields of expertise based on the extracted keywords and categories. To do this, it uses multicultural AI models and multi-perspective AI models. For example, based on "Category: Environment" and "Keywords: Recent Environmental Issues," it generates solutions such as "Latest research on global warming" and "The current state and future of plastic pollution."

Step 4:

Recognize user emotions.

An emotion engine installed on the user's device analyzes the user's facial expressions, voice, and input patterns to recognize their emotional state. This data includes facial recognition data, voice data, and typing patterns. For example, it recognizes whether the user is feeling anxious or excited. This emotional information becomes input data for adjusting the solution generated in the next step.

Step 5:

Adjust the generated solutions based on the user's sentiment.

The server adjusts the content and tone of the generated solution based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is feeling relaxed, it will suggest creative ideas. In this process, emotional data and solution data are input, and adjusted solution data is output.

Step 6:

Present the generated solution to the user.

The server then formats the adjusted solutions in an easy-to-read format and presents them to the user through a user interface. In this step, the formatted solution data is input and display data for the user is output. Specifically, the solutions are presented in a list format on the web browser, allowing the user to review them. For example, "Recent research on global warming" and "The current state and future of plastic pollution" are displayed.

The specific processing unit 290 transmits the results of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). Examples of data generation model 58 include generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>) and Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the robot 414.

The emotion identification model 59, which serves as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see Figure 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

Figure 9 shows an emotion map 400 on which multiple emotions are mapped. In emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both emotions and mental states. Emotions that are generally generated from reactions that occur in the brain are arranged on the left side of the concentric circles. Emotions that are generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged above and below the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the top side of the concentric circles, and the emotion of "discomfort" is arranged on the bottom side. In this way, emotion map 400 maps multiple emotions based on the structure by which emotions are generated, with emotions that tend to occur simultaneously being mapped close together.

These emotions are distributed in the 3 o'clock direction on emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a sense of calm.

The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible (expressed in behavior) the emotion becomes.

Here, human emotions are based on various balances such as posture and blood sugar levels, and when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery life, and when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotion maps may be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and emotional brain physiological signal analysis systems, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions belonging to an area called "reaction," where sensation is dominant. The right half of the emotion map is lined with emotions belonging to an area called "situation," where situational awareness is dominant.

The emotion map defines two emotions that encourage learning. One is the negative emotion around the middle of "repentance" or "reflection" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are close to each other have similar values, as in the emotion map 900 shown in Figure 10. Figure 10 shows an example in which multiple emotions, such as "relieved," "calm," and "reassuring," have similar emotion values.

The system according to the present disclosure has been described above primarily in terms of the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may also be implemented as a general information processing system. The present disclosure may also be implemented, for example, as a software program that runs on a personal computer or an application that runs on a smartphone, etc. The method according to the present disclosure may also be provided to users in the form of SaaS (Software as a Service).

In the above embodiment, an example was given in which a specific process was performed by a single computer 22, but the technology of the present disclosure is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including the computer 22. For example, the data generation model 58 may be provided in a device external to the data processing device 12, and data may be generated in that external device in response to input data.

In the above embodiment, an example was described in which the specific processing program 56 is stored in the storage 32, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

It is not necessary to store the entire specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entire specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The following types of processors can be used as hardware resources for executing specific processes. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically for executing specific processes. All processors have built-in or connected memory, and all use the memory to execute specific processes.

The hardware resource that executes the specific processing may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific processing may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured using a combination of one or more CPUs and software, and this processor functions as a hardware resource that executes specific processing. Second, there is a configuration in which a processor is used to realize the functions of an entire system, including multiple hardware resources that execute specific processing, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, specific processing is realized using one or more of the various processors listed above as hardware resources.

More specifically, the hardware structure of these various processors can be an electrical circuit that combines circuit elements such as semiconductor devices. Furthermore, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps can be added, or the processing order can be rearranged, all within the scope of the spirit of the invention.

The above-described written content and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above explanation of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or substitutions may be made to the above-described written content and illustrations, as long as they do not deviate from the spirit of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described written content and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

All publications, patent applications, and technical standards mentioned in this specification are incorporated by reference herein to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

The following is further disclosed regarding the above embodiments.

(Claim 1)

A means for receiving any issues or problems entered by users,

Means of analyzing received issues and problems,

A means of generating solutions from different cultural backgrounds and fields of expertise based on the analysis results,

A means of presenting the generated solution to the user,

A system including

(Claim 2)

The system of claim 1, further comprising means for generating solutions using multiple AI models based on the analysis results.

(Claim 3)

The system of claim 1 further comprising means for integrating and formatting the generated solutions before presenting them to the user.

"Example 1"

(Claim 1)

A means for receiving any issues or problems entered by users,

A means to temporarily store received issues and problems in a database,

A method for analyzing saved issues and problems using a natural language processing engine and extracting categories and related keywords for the entered issues,

A means of utilizing multiple AI models to generate solutions from different cultural backgrounds and areas of expertise based on the analysis results,

A means to integrate the generated solutions and format them before presenting them to the user,

A way to display a formatted solution to the user,

A system including

(Claim 2)

The system described in claim 1 is equipped with a means for analyzing input tasks using a natural language processing engine.

(Claim 3)

The system described in claim 1, further comprising means for generating solutions using a multicultural AI model and a multi-perspective AI model based on the analysis results.

(Claim 4)

The system of claim 1 further comprises means for integrating generated solutions and merging and formatting the text before presenting it to the user.

"Application Example 1"

New patent claims

(Claim 1)

A means for receiving any issues or problems entered by users,

Means of analyzing received issues and problems,

A means of generating solutions from different cultural backgrounds and fields of expertise based on the analysis results,

A means of presenting the generated solution to the user,

Means including a natural language processing engine that analyzes the task entered by the user and extracts specific keywords;

A means of utilizing multiple artificial intelligence models to generate solutions from multicultural and multi-perspectives,

A means of integrating and optimally formatting the generated solutions,

A means for displaying the integrated solution on a display device;

A system including

(Claim 2)

The system of claim 1, further comprising means for generating solutions using multiple artificial intelligence models based on the analysis results.

(Claim 3)

The system of claim 1 further comprising means for integrating and formatting the generated solutions before presenting them to the user.

"Example 2: Combining Emotion Engines"

(Claim 1)

A means for receiving any issues or problems entered by users,

Means of analyzing received issues and problems,

Means for recognizing the user's emotional state,

A means of generating solutions from different cultural backgrounds and areas of expertise based on analysis results and the user's emotional state,

A means of presenting the generated solution to the user,

A system including

(Claim 2)

The system described in claim 1, further comprising means for generating solutions using multiple learning models based on the analysis results and the user's emotional state.

(Claim 3)

The system of claim 1 further comprising means for integrating and formatting the generated solutions before presenting them to the user.

"Application Example 2: Combining Emotion Engines"

(Claim 1)

A means for receiving any issues or problems entered by users,

Means of analyzing received issues and problems,

A means of generating solutions from different cultural backgrounds and fields of expertise based on the analysis results,

Means to recognize user emotions,

A means of adjusting the generated solution based on the user's emotions,

A means of presenting the generated solution to the user,

A system including

(Claim 2)

The system of claim 1, further comprising means for generating solutions using multiple AI models based on the analysis results.

(Claim 3)

The system of claim 1 further comprising means for integrating and formatting the generated solutions before presenting them to the user.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (3)

means for receiving any issues or problems input by a user;
A means of analyzing received issues and problems;
A means of generating solutions from different cultural backgrounds and disciplines based on the analysis results;
means for presenting the generated solution to a user;
A system including: The system of claim 1, further comprising means for generating solutions using multiple AI models based on the analysis results. The system of claim 1 further comprising means for integrating and formatting the generated solutions before presenting them to the user.