JP2025048907A - system - Google Patents

Abstract

To provide a system capable of immediately notifying urgent or important matters only and summarizing the rest of the message.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, a notification unit, and a summary unit. The collection unit collects messages. The analysis unit analyzes the messages collected by the collection unit. The notification unit notifies urgent or important matters identified by the analysis unit. The summary unit creates a summary of the message not identified by the analysis unit.SELECTED DRAWING: Figure 1

Description

The technology disclosed herein relates to a system.

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

JP 2022-180282 A

Conventional technology had the problem of receiving too many message notifications, which reduced concentration.

The system according to the embodiment aims to immediately notify only urgent or important matters and summarize other messages.

The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a summary unit. The collection unit collects messages. The analysis unit analyzes the messages collected by the collection unit. The notification unit notifies of urgent or important matters identified by the analysis unit. The summary unit creates a summary of messages not identified by the analysis unit.

The system according to the embodiment can immediately notify only urgent or important matters and summarize other messages.

1 is a conceptual diagram showing an example of a 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. FIG. 11 is a conceptual diagram showing an example of a configuration of a data processing system according to a second embodiment. FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. FIG. 13 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.

Below, an example of an embodiment of a system related to the technology disclosed herein is described with reference to the attached drawings.

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

In the following embodiments, the signed processor (hereinafter simply referred to as the "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. The processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

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

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

In the following embodiments, a communication I/F (Interface) with a code is an interface including a communication processor and an antenna. The communication I/F controls communication between multiple computers. Examples of communication standards applied to the communication I/F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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 only A, only B, or a combination of A and B. In addition, in this specification, the same concept as "A and/or B" is also applied when three or more things are expressed by connecting them with "and/or."

[First embodiment]
FIG. 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 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by 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 (see FIG. 2) acquires the data indicating the user input.

The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form of expression that the user can perceive (e.g., voice and/or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 transmitting and receiving various types of 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 the 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.

The 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also use these models to perform processing similar to that of the specific processing unit 290.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

(Example 1)
The system according to the embodiment of the present invention is a system that uses a generation AI to integrate communication apps such as email and communication tools and analyze the contents of text messages. This system collects messages from multiple communication apps used by the user, and the generation AI analyzes the collected messages and immediately notifies the user of only urgent or important matters. For other messages, the generation AI creates a summary and provides it to the user. This mechanism can solve the current situation where the user is always distracted by notifications and has low concentration. For example, messages are collected from multiple communication apps used by the user. For example, messages are collected from email apps, chat apps, SNS, etc. At this time, the messages can be obtained using the API of each app. Next, the generation AI analyzes the collected messages. The generation AI analyzes the contents of the messages and identifies urgent or important matters. For example, messages containing specific keywords or phrases can be classified as urgent or important matters. This allows the user to immediately grasp important messages. Only urgent or important matters are immediately notified. The user is immediately notified of urgent or important matters identified by the generation AI. For example, the user can be immediately notified using push notifications, email notifications, etc. This allows the user to respond without missing important messages. For other messages, the generation AI creates summaries. The generation AI summarizes the contents of the collected messages and creates summaries. For example, a summary of all messages can be provided to the user at the end of the day. This allows the user to concentrate on their work without being distracted by unimportant messages. This mechanism can solve the current situation where users are constantly distracted by notifications and their concentration is reduced. Users can efficiently manage communication by immediately understanding only urgent or important matters and checking summaries for other messages. For example, frequent notifications during work will not break the user's concentration, improving work efficiency. This allows the system to quickly grasp important messages while maintaining the user's concentration.

The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a summary unit. The collection unit collects messages from a plurality of communication applications used by the user. The collection unit can collect messages from, for example, an email application, a chat application, an SNS, and the like. The collection unit can acquire messages using the API of each application. For example, the collection unit acquires email messages using the API of an email application. The collection unit can also acquire chat messages using the API of a chat application. Furthermore, the collection unit can acquire SNS messages using the API of an SNS. The analysis unit uses a generation AI to analyze the messages collected by the collection unit. The analysis unit analyzes the contents of the messages and identifies urgent or important matters. For example, the analysis unit can classify messages containing specific keywords or phrases as urgent or important matters. The analysis unit analyzes the contents of the messages using natural language processing technology. For example, the analysis unit analyzes the words of the messages using morphological analysis. The analysis unit can also analyze the sentence structure of the messages using grammatical analysis. Furthermore, the analysis unit can analyze the meaning of the messages using semantic analysis. The notification unit notifies the user of urgent or important matters identified by the analysis unit. The notification unit immediately notifies the user of urgent or important matters, for example, by using push notification or email notification. The notification unit can immediately inform the user. For example, the notification unit notifies the user of urgent or important matters by using push notification. The notification unit can also notify the user of urgent or important matters by using email notification. The summary unit creates summaries of messages that were not identified by the analysis unit. The summary unit uses the generation AI to summarize the contents of the collected messages and create summaries. The summary unit can provide the user with summaries of all messages, for example, at the end of the day. The summary unit creates summaries to be provided to the user. For example, the summary unit summarizes the contents of the collected messages and creates summaries. As a result, the system according to the embodiment can quickly grasp important messages while maintaining the user's concentration. A part or all of the above-mentioned processing in the summary unit may be performed, for example, by using the generation AI, or may be performed without using the generation AI. For example, the summary unit can input the contents of collected messages to the generation AI and have the generation AI create a summary.

The collection unit collects messages from multiple communication apps used by the user. The collection unit can collect messages from, for example, email apps, chat apps, SNS, and the like. Specifically, the collection unit acquires messages using the API of each app. For example, when acquiring email messages using the API of an email app, the API is accessed using the user's authentication information to acquire message data from the inbox or outbox. Similarly, in the case of a chat app, messages are acquired from chat rooms or individual conversations through the API. When using the API of an SNS, messages can be collected from the user's timeline or direct messages. These APIs are usually accessed securely using an authentication protocol such as OAuth. The collection unit centrally manages the message data acquired through these APIs and stores it in a database. The database also includes metadata such as the sender, recipient, sending date and time, and message content of the message. This allows the collection unit to efficiently collect messages from multiple communication apps used by the user and manage them in an integrated manner. Furthermore, the collection unit has a scheduling function that periodically calls the API to acquire new messages, thereby achieving real-time message collection. This allows the user to keep track of the latest messages without missing them.

The analysis unit uses the generation AI to analyze the messages collected by the collection unit. The analysis unit analyzes the contents of the messages and identifies urgent or important cases. Specifically, the analysis unit analyzes the contents of the messages using natural language processing technology. For example, the words of the messages are analyzed using morphological analysis, and the sentence structure of the messages is analyzed using grammatical analysis. Furthermore, the meaning of the message can be analyzed using semantic analysis. The generation AI combines these analysis techniques to deeply understand the contents of the messages and identify urgent or important cases. For example, messages containing specific keywords or phrases can be classified as urgent or important cases. The analysis unit inputs a prompt to the generation AI to have it analyze the contents of the messages. The prompt includes, for example, instructions such as "Please determine whether this message is urgent or important." The generation AI analyzes the contents of the messages based on the prompt and identifies urgent or important cases. The analysis unit lists urgent or important cases based on the output result of the generation AI and passes them to the notification unit. This allows the analysis unit to quickly and accurately analyze the collected messages, allowing the user to respond without missing important messages. Furthermore, the analysis unit can use past message data as learning data to continually improve the analytical accuracy of the generation AI. This allows the analysis unit to always use the latest technology to achieve highly accurate message analysis.

The notification unit notifies the user of the urgent or important matter identified by the analysis unit. Specifically, the notification unit immediately notifies the user of the urgent or important matter using a push notification or an email notification. For example, when a push notification is used to notify the user of an urgent or important matter to a smartphone of the user, the notification unit generates the contents of the push notification based on the information of the urgent or important matter received from the analysis unit. The push notification includes information such as the sender of the message, the subject, and the importance. The notification unit can send a push notification to the smartphone of the user and immediately notify the user. Furthermore, when an email notification is used to notify the user of an urgent or important matter to the user, the notification unit generates the contents of the email based on the information of the urgent or important matter received from the analysis unit. The email includes the sender of the message, the subject, the importance, a part of the message content, and the like. The notification unit can send an email to the user's email address and immediately notify the user. Furthermore, the notification unit can customize the notification method according to the user's settings. For example, when a user prioritizes push notifications, the notification unit prioritizes sending push notifications and uses email notifications as a complement. Also, if a user does not want to receive notifications during a specific time period, the notification unit will send notifications avoiding that time period. This allows the notification unit to provide flexible notification methods according to the user's needs, and ensure that urgent or important matters are communicated to the user.

The summary unit creates summaries of messages that were not identified by the analysis unit. Specifically, the summary unit uses the generation AI to summarize the contents of the collected messages and create summaries. For example, the summary unit can provide a summary of all messages to the user at the end of the day. The summary unit inputs the contents of the collected messages to the generation AI and has the generation AI create summaries. The generation AI analyzes the contents of the messages, extracts important information, and generates summaries. For example, the generation AI extracts the subject and important points of each message and combines them to create a concise summary. The summary unit provides the user with the summary created by the generation AI. The summary is provided to the user, for example, via email or a dedicated dashboard. This allows the user to check the summaries of all messages at the end of the day and understand important information without missing it. Furthermore, the summary unit can continuously improve the accuracy of the summary based on user feedback. For example, if the user provides feedback on the contents of the summary, the summary unit reflects the feedback in the generation AI and uses it to create the next summary. This allows the summary section to provide highly accurate summaries that meet the user's needs, allowing the user to quickly grasp important information while maintaining their concentration.

The collection unit can collect messages by using the API of each app. For example, the collection unit can acquire email messages by using the API of a mail app. The collection unit can also acquire chat messages by using the API of a chat app. The collection unit can also acquire SNS messages by using the API of an SNS. In this way, messages can be collected efficiently by using the API of each app. Some or all of the above-mentioned processing in the collection unit can be performed by using AI, for example, or can be performed without using AI. For example, the collection unit can input the messages acquired by using the API of each app to AI and cause the AI to collect the messages.

The collection unit can collect messages using scraping technology. The collection unit can collect messages from web pages using, for example, HTML parsing. The collection unit can also analyze the structure of a web page using DOM analysis and collect messages. The collection unit can collect messages from apps for which an API is not provided using scraping technology. In this way, by using scraping technology, messages can be collected from apps for which an API is not provided. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI or can be performed without using AI. For example, the collection unit can input messages collected using scraping technology to AI and cause the AI to collect messages.

The analysis unit can analyze the contents of the message using natural language processing technology and detect specific keywords and phrases. For example, the analysis unit can analyze the words of the message using morphological analysis. The analysis unit can also analyze the sentence structure of the message using grammatical analysis. The analysis unit can also analyze the meaning of the message using semantic analysis. This allows the content of the message to be accurately analyzed using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the collected messages to the generation AI and have the generation AI analyze the messages.

The analysis unit can set specific criteria that can be customized by the user. The analysis unit, for example, sets keyword-based filtering. The analysis unit can also set rule-based filtering. This allows the user to customize the specific criteria, enabling more flexible analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the specific criteria set by the user to AI and cause it to perform message analysis based on AI.

The notification unit can immediately notify the user of urgent or important matters by using push notifications or email notifications. The notification unit, for example, notifies the user of urgent or important matters by using push notifications. The notification unit can also notify the user of urgent or important matters by using email notifications. In this way, by using push notifications or email notifications, the user can be immediately notified of important matters. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input urgent or important matters into AI and execute notifications based on AI.

The summary unit can summarize the contents of the collected messages and create summaries. The summary unit can, for example, summarize the contents of the collected messages and create summaries. The summary unit can provide the user with a summary of all messages at the end of the day. This allows the user to efficiently grasp information by summarizing the contents of the messages. Some or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input the collected messages to the generation AI and have the generation AI create summaries.

The collection unit can analyze the user's past message collection history and select the optimal collection method. For example, the collection unit prioritizes collecting messages from apps that the user has used frequently in the past. The collection unit can also concentrate collection during a specific time period based on the user's past collection history. The collection unit can also analyze patterns of messages that the user has judged to be important in the past and prioritize collecting similar messages. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past message collection history into AI and select the optimal collection method based on AI.

When collecting messages, the collection unit can filter based on the user's current project or field of interest. For example, the collection unit preferentially collects messages related to a project currently in progress by the user. The collection unit can also filter highly relevant messages based on the user's field of interest. The collection unit can also collect specific messages based on keywords set by the user. In this way, highly relevant messages can be preferentially collected by filtering messages based on the user's project or field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data for filtering based on the user's current project or field of interest to AI, and cause filtering to be performed based on AI.

When collecting messages, the collection unit can prioritize collecting highly relevant messages by taking into account the geographical location information of the user. For example, when the user is in a specific area, the collection unit prioritizes collecting messages related to that area. When the user is traveling, the collection unit can also prioritize collecting messages related to the travel destination. When the user is at home, the collection unit can also prioritize collecting messages related to the home. In this way, highly relevant messages can be collected preferentially by taking into account the geographical location information of the user. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and preferentially collect highly relevant messages based on AI.

When collecting messages, the collection unit can analyze the user's social media activity and collect related messages. For example, the collection unit collects related messages based on the content that the user frequently posts on a specific social media. The collection unit can also collect related messages based on the content posted by accounts that the user follows. The collection unit can also collect related messages based on the activities of groups and communities in which the user participates. In this way, highly relevant messages can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input the user's social media activity data into AI and collect related messages based on AI.

The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. For example, the analysis unit performs a detailed analysis of messages with high importance. The analysis unit can also perform a concise analysis of messages with low importance. The analysis unit can also determine the priority of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the message. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message importance data to AI and adjust the level of detail of the analysis based on AI.

When analyzing, the analysis unit can apply different analysis algorithms depending on the message category. For example, the analysis unit applies a business analysis algorithm to a business-related message. The analysis unit can also apply a private analysis algorithm to a private message. The analysis unit can also apply a social media analysis algorithm to a social media message. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the message category. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input message category data to the generation AI and apply the analysis algorithm based on the generation AI.

During analysis, the analysis unit can determine the analysis priority based on the time when the message was sent. For example, the analysis unit gives priority to analyzing recently sent messages. The analysis unit can also give priority to analyzing messages sent during important time periods. The analysis unit can also give priority to analyzing messages received by a user during a specific time period. In this way, important messages can be analyzed with priority by determining the analysis priority based on the time when the message was sent. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message sending time data into AI and determine the analysis priority based on AI.

The analysis unit can adjust the order of analysis based on the relevance of the messages during analysis. For example, the analysis unit prioritizes analysis of highly relevant messages. The analysis unit can also postpone analysis of less relevant messages. The analysis unit can also dynamically adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the messages. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message relevance data to AI and adjust the order of analysis based on AI.

The notification unit can adjust the level of detail of the notification based on the importance of the message at the time of notification. For example, the notification unit provides detailed notifications for messages with high importance. The notification unit can also provide brief notifications for messages with low importance. The notification unit can also determine the priority of notifications according to the importance. This allows for efficient notifications by adjusting the level of detail of the notification according to the importance of the message. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input message importance data to AI and adjust the level of detail of the notification based on AI.

The notification unit can apply different notification algorithms depending on the message category at the time of notification. For example, the notification unit applies a business notification algorithm to a business-related message. The notification unit can also apply a private notification algorithm to a private message. The notification unit can also apply a social media notification algorithm to a social media message. This improves notification accuracy by applying an appropriate notification algorithm depending on the message category. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input message category data into AI and apply a notification algorithm based on AI.

When sending a notification, the notification unit can determine the priority of the notification by taking into account the attribute information of the message sender. For example, the notification unit gives priority to notifying messages from important senders. The notification unit can also determine the priority of the notification based on the job title and relationship of the sender. The notification unit can also analyze the sender's past message history and determine the importance. In this way, important messages can be given priority by taking into account the attribute information of the message sender. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the sender's attribute information into AI and determine the priority of the notification based on AI.

The notification unit can adjust the order of notifications based on the relevance of the messages at the time of notification. For example, the notification unit gives priority to notifying highly relevant messages. The notification unit can also postpone notifying less relevant messages. The notification unit can also dynamically adjust the order of notifications according to the relevance. This allows for efficient notifications by adjusting the order of notifications based on the relevance of the messages. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input message relevance data into AI and adjust the order of notifications based on AI.

When creating a summary, the summary unit can adjust the level of detail of the summary based on the importance of the message. For example, the summary unit provides a detailed summary for a message of high importance. For a message of low importance, the summary unit can also provide a concise summary. The summary unit can also determine the priority of the summary according to the importance. This allows for efficient summary creation by adjusting the level of detail of the summary according to the importance of the message. Some or all of the above-mentioned processing in the summary unit may be performed using AI, or may be performed without using AI. For example, the summary unit can input message importance data into AI and adjust the level of detail of the summary based on AI.

When creating a summary, the summary unit can apply different summary algorithms depending on the category of the message. For example, the summary unit applies a business summary algorithm to a business-related message. The summary unit can also apply a private summary algorithm to a private message. The summary unit can also apply a social media summary algorithm to a social media message. This improves the accuracy of the summary by applying an appropriate summary algorithm depending on the message category. Some or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input message category data to the generation AI and apply a summary algorithm based on the generation AI.

When creating a summary, the summary unit can determine the priority of the summary based on the time when the message was sent. For example, the summary unit prioritizes including recently sent messages in the summary. The summary unit can also prioritize including messages sent during important time periods in the summary. The summary unit can also prioritize including messages received by the user during a specific time period in the summary. In this way, important messages can be prioritized for inclusion in the summary by determining the priority of the summary based on the time when the message was sent. Some or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input message sending time data to the generation AI and determine the priority of the summary based on the generation AI.

When creating a summary, the summary unit can adjust the order of summaries based on the relevance of the messages. For example, the summary unit prioritizes including highly relevant messages in the summary. The summary unit can also postpone less relevant messages. The summary unit can also dynamically adjust the order of summaries according to relevance. This allows for efficient summary creation by adjusting the order of summaries based on the relevance of messages. Some or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input message relevance data to the generation AI and adjust the order of summaries based on the generation AI.

The system according to the embodiment is not limited to the above-mentioned example, and various modifications are possible, for example, as follows:

The collection unit can analyze the user's past message collection history and select the optimal collection method. For example, the collection unit prioritizes collection of messages from apps that the user has used frequently in the past. The collection unit can also concentrate collection during a specific time period based on the user's past collection history. The collection unit can also analyze patterns of messages that the user has judged to be important in the past and prioritize collection of similar messages. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past message collection history into AI and select the optimal collection method based on AI.

The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. For example, a detailed analysis is performed for messages with high importance. The analysis unit can also perform a concise analysis for messages with low importance. The analysis unit can also determine the priority of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the message. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message importance data to AI and adjust the level of detail of the analysis based on AI.

The notification unit can adjust the level of detail of the notification based on the importance of the message at the time of notification. For example, for messages of high importance, a detailed notification is provided. For messages of low importance, the notification unit can also provide a brief notification. The notification unit can also determine the priority of the notification according to the importance. This allows for efficient notification by adjusting the level of detail of the notification according to the importance of the message. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input message importance data into AI and adjust the level of detail of the notification based on AI.

When creating a summary, the summary unit can apply different summary algorithms depending on the category of the message. For example, a business summary algorithm can be applied to a business-related message. The summary unit can also apply a private summary algorithm to a private message. The summary unit can also apply a social media summary algorithm to a social media message. This improves the accuracy of the summary by applying an appropriate summary algorithm depending on the message category. Some or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input message category data to the generation AI and apply a summary algorithm based on the generation AI.

When collecting messages, the collection unit can prioritize collecting highly relevant messages by taking into account the geographical location information of the user. For example, when the user is in a specific area, messages related to that area are prioritized. When the user is traveling, the collection unit can also prioritize collecting messages related to the travel destination. When the user is at home, the collection unit can also prioritize collecting messages related to the home. In this way, highly relevant messages can be prioritized by taking into account the geographical location information of the user. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and prioritize collecting highly relevant messages based on AI.

The processing flow of Example 1 is briefly explained below.

Step 1: The collection unit collects messages from multiple communication apps used by the user. The collection unit can collect messages from, for example, an email app, a chat app, an SNS, and the like. The collection unit can acquire messages using the API of each app. For example, the collection unit can acquire email messages using the API of an email app. The collection unit can also acquire chat messages using the API of a chat app. Furthermore, the collection unit can acquire SNS messages using the API of an SNS.
Step 2: The analysis unit uses the generation AI to analyze the messages collected by the collection unit. The analysis unit analyzes the content of the messages to identify urgent or important matters. For example, the analysis unit can classify messages containing specific keywords or phrases as urgent or important matters. The analysis unit analyzes the content of the messages using natural language processing techniques. For example, the analysis unit can analyze the words of the messages using morphological analysis. The analysis unit can also analyze the sentence structure of the messages using grammatical analysis. Furthermore, the analysis unit can analyze the meaning of the messages using semantic analysis.
Step 3: The notification unit notifies the user of the urgent or important matter identified by the analysis unit. The notification unit immediately notifies the user of the urgent or important matter, for example, by using a push notification or an email notification. The notification unit can immediately notify the user. For example, the notification unit notifies the user of the urgent or important matter by using a push notification. The notification unit can also notify the user of the urgent or important matter by using an email notification.
Step 4: The summary unit creates summaries of messages that were not identified by the analysis unit. The summary unit uses the generation AI to summarize the contents of the collected messages and create summaries. The summary unit can provide the user with a summary of all messages at the end of a day, for example. The summary unit creates summaries to be provided to the user. For example, the summary unit summarizes the contents of the collected messages and creates summaries. This allows the system according to the embodiment to quickly grasp important messages while maintaining the user's concentration. Some or all of the above-mentioned processing in the summary unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the summary unit can input the contents of the collected messages into the generation AI and have the generation AI create summaries.

(Example 2)
The system according to the embodiment of the present invention is a system that uses a generation AI to integrate communication apps such as email and communication tools and analyze the contents of text messages. This system collects messages from multiple communication apps used by the user, and the generation AI analyzes the collected messages and immediately notifies the user of only urgent or important matters. For other messages, the generation AI creates a summary and provides it to the user. This mechanism can solve the current situation where the user is always distracted by notifications and has low concentration. For example, messages are collected from multiple communication apps used by the user. For example, messages are collected from email apps, chat apps, SNS, etc. At this time, the messages can be obtained using the API of each app. Next, the generation AI analyzes the collected messages. The generation AI analyzes the contents of the messages and identifies urgent or important matters. For example, messages containing specific keywords or phrases can be classified as urgent or important matters. This allows the user to immediately grasp important messages. Only urgent or important matters are immediately notified. The user is immediately notified of urgent or important matters identified by the generation AI. For example, the user can be immediately notified using push notifications, email notifications, etc. This allows the user to respond without missing important messages. For other messages, the generation AI creates summaries. The generation AI summarizes the contents of the collected messages and creates summaries. For example, a summary of all messages can be provided to the user at the end of the day. This allows the user to concentrate on their work without being distracted by unimportant messages. This mechanism can solve the current situation where users are constantly distracted by notifications and their concentration is reduced. Users can efficiently manage communication by immediately understanding only urgent or important matters and checking summaries for other messages. For example, frequent notifications during work will not break the user's concentration, improving work efficiency. This allows the system to quickly grasp important messages while maintaining the user's concentration.

The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a summary unit. The collection unit collects messages from a plurality of communication applications used by the user. The collection unit can collect messages from, for example, an email application, a chat application, an SNS, and the like. The collection unit can acquire messages using the API of each application. For example, the collection unit acquires email messages using the API of an email application. The collection unit can also acquire chat messages using the API of a chat application. Furthermore, the collection unit can acquire SNS messages using the API of an SNS. The analysis unit uses a generation AI to analyze the messages collected by the collection unit. The analysis unit analyzes the contents of the messages and identifies urgent or important matters. For example, the analysis unit can classify messages containing specific keywords or phrases as urgent or important matters. The analysis unit analyzes the contents of the messages using natural language processing technology. For example, the analysis unit analyzes the words of the messages using morphological analysis. The analysis unit can also analyze the sentence structure of the messages using grammatical analysis. Furthermore, the analysis unit can analyze the meaning of the messages using semantic analysis. The notification unit notifies the user of urgent or important matters identified by the analysis unit. The notification unit immediately notifies the user of urgent or important matters, for example, by using push notification or email notification. The notification unit can immediately inform the user. For example, the notification unit notifies the user of urgent or important matters by using push notification. The notification unit can also notify the user of urgent or important matters by using email notification. The summary unit creates summaries of messages that were not identified by the analysis unit. The summary unit uses the generation AI to summarize the contents of the collected messages and create summaries. The summary unit can provide the user with summaries of all messages, for example, at the end of the day. The summary unit creates summaries to be provided to the user. For example, the summary unit summarizes the contents of the collected messages and creates summaries. As a result, the system according to the embodiment can quickly grasp important messages while maintaining the user's concentration. A part or all of the above-mentioned processing in the summary unit may be performed, for example, by using the generation AI, or may be performed without using the generation AI. For example, the summary unit can input the contents of collected messages to the generation AI and have the generation AI create a summary.

The collection unit collects messages from multiple communication apps used by the user. The collection unit can collect messages from, for example, email apps, chat apps, SNS, and the like. Specifically, the collection unit acquires messages using the API of each app. For example, when acquiring email messages using the API of an email app, the API is accessed using the user's authentication information to acquire message data from the inbox or outbox. Similarly, in the case of a chat app, messages are acquired from chat rooms or individual conversations through the API. When using the API of an SNS, messages can be collected from the user's timeline or direct messages. These APIs are usually accessed securely using an authentication protocol such as OAuth. The collection unit centrally manages the message data acquired through these APIs and stores it in a database. The database also includes metadata such as the sender, recipient, sending date and time, and message content of the message. This allows the collection unit to efficiently collect messages from multiple communication apps used by the user and manage them in an integrated manner. Furthermore, the collection unit has a scheduling function that periodically calls the API to acquire new messages, thereby achieving real-time message collection. This allows the user to keep track of the latest messages without missing them.

The analysis unit uses the generation AI to analyze the messages collected by the collection unit. The analysis unit analyzes the contents of the messages and identifies urgent or important cases. Specifically, the analysis unit analyzes the contents of the messages using natural language processing technology. For example, the words of the messages are analyzed using morphological analysis, and the sentence structure of the messages is analyzed using grammatical analysis. Furthermore, the meaning of the message can be analyzed using semantic analysis. The generation AI combines these analysis techniques to deeply understand the contents of the messages and identify urgent or important cases. For example, messages containing specific keywords or phrases can be classified as urgent or important cases. The analysis unit inputs a prompt to the generation AI to have it analyze the contents of the messages. The prompt includes, for example, instructions such as "Please determine whether this message is urgent or important." The generation AI analyzes the contents of the messages based on the prompt and identifies urgent or important cases. The analysis unit lists urgent or important cases based on the output result of the generation AI and passes them to the notification unit. This allows the analysis unit to quickly and accurately analyze the collected messages, allowing the user to respond without missing important messages. Furthermore, the analysis unit can use past message data as learning data to continually improve the analytical accuracy of the generation AI. This allows the analysis unit to always use the latest technology to achieve highly accurate message analysis.

The notification unit notifies the user of the urgent or important matter identified by the analysis unit. Specifically, the notification unit immediately notifies the user of the urgent or important matter using a push notification or an email notification. For example, when a push notification is used to notify the user of an urgent or important matter to a smartphone of the user, the notification unit generates the contents of the push notification based on the information of the urgent or important matter received from the analysis unit. The push notification includes information such as the sender of the message, the subject, and the importance. The notification unit can send a push notification to the smartphone of the user and immediately notify the user. Furthermore, when an email notification is used to notify the user of an urgent or important matter to the user, the notification unit generates the contents of the email based on the information of the urgent or important matter received from the analysis unit. The email includes the sender of the message, the subject, the importance, a part of the message content, and the like. The notification unit can send an email to the user's email address and immediately notify the user. Furthermore, the notification unit can customize the notification method according to the user's settings. For example, when a user prioritizes push notifications, the notification unit prioritizes sending push notifications and uses email notifications as a complement. Also, if a user does not want to receive notifications during a specific time period, the notification unit will send notifications avoiding that time period. This allows the notification unit to provide flexible notification methods according to the user's needs, and ensure that urgent or important matters are communicated to the user.

The summary unit creates summaries of messages that were not identified by the analysis unit. Specifically, the summary unit uses the generation AI to summarize the contents of the collected messages and create summaries. For example, the summary unit can provide a summary of all messages to the user at the end of the day. The summary unit inputs the contents of the collected messages to the generation AI and has the generation AI create summaries. The generation AI analyzes the contents of the messages, extracts important information, and generates summaries. For example, the generation AI extracts the subject and important points of each message and combines them to create a concise summary. The summary unit provides the user with the summary created by the generation AI. The summary is provided to the user, for example, via email or a dedicated dashboard. This allows the user to check the summaries of all messages at the end of the day and understand important information without missing it. Furthermore, the summary unit can continuously improve the accuracy of the summary based on user feedback. For example, if the user provides feedback on the contents of the summary, the summary unit reflects the feedback in the generation AI and uses it to create the next summary. This allows the summary section to provide highly accurate summaries that meet the user's needs, allowing the user to quickly grasp important information while maintaining their concentration.

The collection unit can collect messages by using the API of each app. For example, the collection unit can acquire email messages by using the API of a mail app. The collection unit can also acquire chat messages by using the API of a chat app. The collection unit can also acquire SNS messages by using the API of an SNS. In this way, messages can be collected efficiently by using the API of each app. Some or all of the above-mentioned processing in the collection unit can be performed by using AI, for example, or can be performed without using AI. For example, the collection unit can input the messages acquired by using the API of each app to AI and cause the AI to collect the messages.

The collection unit can collect messages using scraping technology. The collection unit can collect messages from web pages using, for example, HTML parsing. The collection unit can also analyze the structure of a web page using DOM analysis and collect messages. The collection unit can collect messages from apps for which an API is not provided using scraping technology. In this way, by using scraping technology, messages can be collected from apps for which an API is not provided. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI or can be performed without using AI. For example, the collection unit can input messages collected using scraping technology to AI and cause the AI to collect messages.

The analysis unit can analyze the contents of the message using natural language processing technology and detect specific keywords and phrases. For example, the analysis unit can analyze the words of the message using morphological analysis. The analysis unit can also analyze the sentence structure of the message using grammatical analysis. The analysis unit can also analyze the meaning of the message using semantic analysis. This allows the content of the message to be accurately analyzed using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the collected messages to the generation AI and have the generation AI analyze the messages.

The analysis unit can set specific criteria that can be customized by the user. The analysis unit, for example, sets keyword-based filtering. The analysis unit can also set rule-based filtering. This allows the user to customize the specific criteria, enabling more flexible analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the specific criteria set by the user to AI and cause it to analyze the message based on AI.

The notification unit can immediately notify the user of urgent or important matters by using push notifications or email notifications. The notification unit, for example, notifies the user of urgent or important matters by using push notifications. The notification unit can also notify the user of urgent or important matters by using email notifications. In this way, by using push notifications or email notifications, the user can be immediately notified of important matters. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input urgent or important matters into AI and execute notifications based on AI.

The summary unit can summarize the contents of the collected messages and create summaries. The summary unit can, for example, summarize the contents of the collected messages and create summaries. The summary unit can provide the user with a summary of all messages at the end of the day. This allows the user to efficiently grasp information by summarizing the contents of the messages. Some or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input the collected messages to the generation AI and have the generation AI create summaries.

The collection unit can estimate the user's emotions and adjust the timing of message collection based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit delays the collection timing to reduce the burden on the user. When the user is relaxed, the collection unit can also collect messages in real time to encourage immediate response. When the user is concentrating, the collection unit can also adjust the collection timing so as not to interfere with the work. In this way, the burden on the user can be reduced by adjusting the collection timing according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data to the generation AI and adjust the collection timing based on the generation AI.

The collection unit can analyze the user's past message collection history and select the optimal collection method. For example, the collection unit prioritizes collecting messages from apps that the user has used frequently in the past. The collection unit can also concentrate collection during a specific time period based on the user's past collection history. The collection unit can also analyze patterns of messages that the user has judged to be important in the past and prioritize collecting similar messages. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past message collection history into AI and select the optimal collection method based on AI.

When collecting messages, the collection unit can filter based on the user's current project or field of interest. For example, the collection unit preferentially collects messages related to a project currently in progress by the user. The collection unit can also filter highly relevant messages based on the user's field of interest. The collection unit can also collect specific messages based on keywords set by the user. In this way, highly relevant messages can be preferentially collected by filtering messages based on the user's project or field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data for filtering based on the user's current project or field of interest to AI, and cause filtering to be performed based on AI.

The collection unit can estimate the user's emotions and determine the priority of messages to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit postpones messages of low importance. When the user is relaxed, the collection unit can also collect all messages evenly. When the user is concentrating, the collection unit can also collect messages of high importance preferentially. In this way, important messages can be preferentially collected by determining the priority of messages according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data to the generation AI and determine the priority of messages based on the generation AI.

When collecting messages, the collection unit can prioritize collecting highly relevant messages by taking into account the geographical location information of the user. For example, when the user is in a specific area, the collection unit prioritizes collecting messages related to that area. When the user is traveling, the collection unit can also prioritize collecting messages related to the travel destination. When the user is at home, the collection unit can also prioritize collecting messages related to the home. In this way, highly relevant messages can be collected preferentially by taking into account the geographical location information of the user. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and preferentially collect highly relevant messages based on AI.

When collecting messages, the collection unit can analyze the user's social media activity and collect related messages. For example, the collection unit collects related messages based on the content that the user frequently posts on a specific social media. The collection unit can also collect related messages based on the content posted by accounts that the user follows. The collection unit can also collect related messages based on the activities of groups and communities in which the user participates. In this way, highly relevant messages can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input the user's social media activity data into AI and collect related messages based on AI.

The analysis unit can estimate the user's emotions and adjust the expression method of the analysis based on the estimated user's emotions. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. When the user is relaxed, the analysis unit can also provide a detailed analysis result. When the user is in a hurry, the analysis unit can also provide a concise analysis result that focuses on the main points. This makes it possible to provide an analysis result that is easy for the user to understand by adjusting the expression method of the analysis according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and adjust the expression method of the analysis based on the generation AI.

The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. For example, the analysis unit performs a detailed analysis of messages with high importance. The analysis unit can also perform a concise analysis of messages with low importance. The analysis unit can also determine the priority of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the message. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message importance data to AI and adjust the level of detail of the analysis based on AI.

When analyzing, the analysis unit can apply different analysis algorithms depending on the message category. For example, the analysis unit applies a business analysis algorithm to a business-related message. The analysis unit can also apply a private analysis algorithm to a private message. The analysis unit can also apply a social media analysis algorithm to a social media message. This improves the analysis accuracy by applying an appropriate analysis algorithm depending on the message category. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input message category data to the generation AI and apply the analysis algorithm based on the generation AI.

The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. When the user is relaxed, the analysis unit can also provide a detailed analysis result. When the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect. This allows the analysis result to be optimal for the user by adjusting the length of the analysis according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and adjust the length of the analysis based on the generation AI.

During analysis, the analysis unit can determine the analysis priority based on the time when the message was sent. For example, the analysis unit gives priority to analyzing recently sent messages. The analysis unit can also give priority to analyzing messages sent during important time periods. The analysis unit can also give priority to analyzing messages received by a user during a specific time period. In this way, important messages can be analyzed with priority by determining the analysis priority based on the time when the message was sent. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message sending time data into AI and determine the analysis priority based on AI.

The analysis unit can adjust the order of analysis based on the relevance of the messages during analysis. For example, the analysis unit prioritizes analysis of highly relevant messages. The analysis unit can also postpone analysis of less relevant messages. The analysis unit can also dynamically adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the messages. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message relevance data into AI and adjust the order of analysis based on AI.

The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user's emotions. For example, when the user is nervous, the notification unit uses a quiet notification sound. When the user is relaxed, the notification unit can also use a bright notification sound. When the user is in a hurry, the notification unit can also use a quick and concise notification method. This allows the optimal notification for the user by adjusting the notification method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the user's emotion data into the generation AI and adjust the notification method based on the generation AI.

The notification unit can adjust the level of detail of the notification based on the importance of the message at the time of notification. For example, the notification unit provides detailed notifications for messages with high importance. The notification unit can also provide brief notifications for messages with low importance. The notification unit can also determine the priority of notifications according to the importance. This allows for efficient notifications by adjusting the level of detail of the notification according to the importance of the message. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input message importance data to AI and adjust the level of detail of the notification based on AI.

The notification unit can apply different notification algorithms depending on the message category at the time of notification. For example, the notification unit applies a business notification algorithm to a business-related message. The notification unit can also apply a private notification algorithm to a private message. The notification unit can also apply a social media notification algorithm to a social media message. This improves notification accuracy by applying an appropriate notification algorithm depending on the message category. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input message category data into AI and apply a notification algorithm based on AI.

The notification unit can estimate the user's emotions and adjust the timing of the notification based on the estimated user's emotions. For example, when the user is nervous, the notification unit delays the notification to reduce the burden on the user. When the user is relaxed, the notification unit can also perform the notification in real time. When the user is concentrating, the notification unit can also adjust the notification timing so as not to disturb the work. This allows the notification to be performed at the optimal timing for the user by adjusting the notification timing according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the user's emotion data to the generation AI and adjust the timing of the notification based on the generation AI.

When sending a notification, the notification unit can determine the priority of the notification by taking into account the attribute information of the message sender. For example, the notification unit gives priority to notifying messages from important senders. The notification unit can also determine the priority of the notification based on the job title and relationship of the sender. The notification unit can also analyze the sender's past message history and determine the importance. In this way, important messages can be given priority by taking into account the attribute information of the message sender. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input the sender's attribute information into AI and determine the priority of the notification based on AI.

The notification unit can adjust the order of notifications based on the relevance of the messages at the time of notification. For example, the notification unit gives priority to notifying highly relevant messages. The notification unit can also postpone notifying less relevant messages. The notification unit can also dynamically adjust the order of notifications according to the relevance. This allows for efficient notifications by adjusting the order of notifications based on the relevance of the messages. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input message relevance data into AI and adjust the order of notifications based on AI.

The summary unit can estimate the user's emotions and adjust the way the summary is expressed based on the estimated user's emotions. For example, when the user is nervous, the summary unit provides a simple and highly visible summary. When the user is relaxed, the summary unit can also provide a detailed summary. When the user is in a hurry, the summary unit can also provide a concise summary that covers the main points. This allows the summary to be easily understood by adjusting the way the summary is expressed according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input the user's emotion data into the generation AI and adjust the way the summary is expressed based on the generation AI.

When creating a summary, the summary unit can adjust the level of detail of the summary based on the importance of the message. For example, the summary unit provides a detailed summary for a message of high importance. For a message of low importance, the summary unit can also provide a concise summary. The summary unit can also determine the priority of the summary according to the importance. This allows for efficient summary creation by adjusting the level of detail of the summary according to the importance of the message. Some or all of the above-mentioned processing in the summary unit may be performed using AI, or may be performed without using AI. For example, the summary unit can input message importance data into AI and adjust the level of detail of the summary based on AI.

When creating a summary, the summary unit can apply different summary algorithms depending on the category of the message. For example, the summary unit applies a business summary algorithm to a business-related message. The summary unit can also apply a private summary algorithm to a private message. The summary unit can also apply a social media summary algorithm to a social media message. This improves the accuracy of the summary by applying an appropriate summary algorithm depending on the message category. Some or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input message category data to the generation AI and apply a summary algorithm based on the generation AI.

The summary unit can estimate the user's emotions and adjust the length of the summary based on the estimated user's emotions. For example, when the user is in a hurry, the summary unit provides a short summary that covers the main points. When the user is relaxed, the summary unit can also provide a detailed summary. When the user is excited, the summary unit can also provide a summary with a visually stimulating effect. This allows the summary to be optimal for the user by adjusting the length of the summary according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input the user's emotion data into the generation AI and adjust the length of the summary based on the generation AI.

When creating a summary, the summary unit can determine the priority of the summary based on the time when the message was sent. For example, the summary unit prioritizes including recently sent messages in the summary. The summary unit can also prioritize including messages sent during important time periods in the summary. The summary unit can also prioritize including messages received by the user during a specific time period in the summary. In this way, important messages can be prioritized for inclusion in the summary by determining the priority of the summary based on the time when the message was sent. Some or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input message sending time data to the generation AI and determine the priority of the summary based on the generation AI.

When creating a summary, the summary unit can adjust the order of summaries based on the relevance of the messages. For example, the summary unit prioritizes including highly relevant messages in the summary. The summary unit can also postpone less relevant messages. The summary unit can also dynamically adjust the order of summaries according to relevance. This allows for efficient summary creation by adjusting the order of summaries based on the relevance of messages. Some or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input message relevance data to the generation AI and adjust the order of summaries based on the generation AI.

The system according to the embodiment is not limited to the above-mentioned example, and various modifications are possible, for example, as follows:

The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, messages with high importance are analyzed with priority. If the user is relaxed, all messages can be analyzed equally. If the user is concentrating, messages with low importance can be postponed. In this way, important messages can be analyzed with priority by determining the analysis priority according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and determine the analysis priority based on the generation AI.

The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user's emotions. For example, if the user is nervous, a concise and to the point notification is provided. If the user is relaxed, a detailed notification can be provided. If the user is in a hurry, a quick and concise notification can be provided. This allows the optimal notification for the user to be provided by adjusting the content of the notification according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit is performed using the generation AI. For example, the notification unit can input the user's emotion data into the generation AI and adjust the content of the notification based on the generation AI.

The summary unit can estimate the user's emotions and adjust the format of the summary based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible summary can be provided. If the user is relaxed, a detailed summary can be provided. If the user is in a hurry, a concise summary with the main points can be provided. By adjusting the format of the summary according to the user's emotions, a summary that is easy for the user to understand can be provided. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input the user's emotion data into the generation AI and adjust the format of the summary based on the generation AI.

The collection unit can estimate the user's emotions and determine the type of messages to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, messages of low importance are not collected. If the user is relaxed, all messages can be collected. If the user is concentrating, messages of high importance can be preferentially collected. This reduces the burden on the user by determining the type of messages to be collected according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and determine the type of messages to be collected based on the generation AI.

The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user's emotions. For example, if the user is nervous, a concise and to the point analysis result is provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a quick and concise analysis result can be provided. This allows the analysis result that is optimal for the user to be provided by adjusting the level of detail of the analysis according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and adjust the level of detail of the analysis based on the generation AI.

The collection unit can analyze the user's past message collection history and select the optimal collection method. For example, the collection unit prioritizes collection of messages from apps that the user has used frequently in the past. The collection unit can also concentrate collection during a specific time period based on the user's past collection history. The collection unit can also analyze patterns of messages that the user has judged to be important in the past and prioritize collection of similar messages. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past message collection history into AI and select the optimal collection method based on AI.

The analysis unit can adjust the level of detail of the analysis based on the importance of the message during analysis. For example, a detailed analysis is performed for messages with high importance. The analysis unit can also perform a concise analysis for messages with low importance. The analysis unit can also determine the priority of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the message. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input message importance data to AI and adjust the level of detail of the analysis based on AI.

The notification unit can adjust the level of detail of the notification based on the importance of the message at the time of notification. For example, for messages of high importance, a detailed notification is provided. For messages of low importance, the notification unit can also provide a brief notification. The notification unit can also determine the priority of the notification according to the importance. This allows for efficient notification by adjusting the level of detail of the notification according to the importance of the message. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can input message importance data into AI and adjust the level of detail of the notification based on AI.

When creating a summary, the summary unit can apply different summary algorithms depending on the category of the message. For example, a business summary algorithm can be applied to a business-related message. The summary unit can also apply a private summary algorithm to a private message. The summary unit can also apply a social media summary algorithm to a social media message. This improves the accuracy of the summary by applying an appropriate summary algorithm depending on the message category. Some or all of the above-mentioned processing in the summary unit is performed using the generation AI. For example, the summary unit can input message category data to the generation AI and apply a summary algorithm based on the generation AI.

When collecting messages, the collection unit can prioritize collecting highly relevant messages by taking into account the geographical location information of the user. For example, when the user is in a specific area, messages related to that area are prioritized. When the user is traveling, the collection unit can also prioritize collecting messages related to the travel destination. When the user is at home, the collection unit can also prioritize collecting messages related to the home. In this way, highly relevant messages can be prioritized by taking into account the geographical location information of the user. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and prioritize collecting highly relevant messages based on AI.

The process flow for Example 2 is briefly explained below.

Step 1: The collection unit collects messages from multiple communication apps used by the user. The collection unit can collect messages from, for example, an email app, a chat app, an SNS, and the like. The collection unit can acquire messages using the API of each app. For example, the collection unit can acquire email messages using the API of an email app. The collection unit can also acquire chat messages using the API of a chat app. Furthermore, the collection unit can acquire SNS messages using the API of an SNS.
Step 2: The analysis unit uses the generation AI to analyze the messages collected by the collection unit. The analysis unit analyzes the content of the messages to identify urgent or important matters. For example, the analysis unit can classify messages containing specific keywords or phrases as urgent or important matters. The analysis unit analyzes the content of the messages using natural language processing techniques. For example, the analysis unit can analyze the words of the messages using morphological analysis. The analysis unit can also analyze the sentence structure of the messages using grammatical analysis. Furthermore, the analysis unit can analyze the meaning of the messages using semantic analysis.
Step 3: The notification unit notifies the user of the urgent or important matter identified by the analysis unit. The notification unit immediately notifies the user of the urgent or important matter, for example, by using a push notification or an email notification. The notification unit can immediately notify the user. For example, the notification unit notifies the user of the urgent or important matter by using a push notification. The notification unit can also notify the user of the urgent or important matter by using an email notification.
Step 4: The summary unit creates summaries of messages that were not identified by the analysis unit. The summary unit uses the generation AI to summarize the contents of the collected messages and create summaries. The summary unit can provide the user with a summary of all messages at the end of a day, for example. The summary unit creates summaries to be provided to the user. For example, the summary unit summarizes the contents of the collected messages and creates summaries. This allows the system according to the embodiment to quickly grasp important messages while maintaining the user's concentration. Some or all of the above-mentioned processing in the summary unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the summary unit can input the contents of the collected messages into the generation AI and have the generation AI create summaries.

The specific processing unit 290 transmits the result 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 result of the specific processing. The microphone 38B acquires audio indicating a user input for the result 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.

The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (registered trademark) (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, processing performed by AI, including the generating AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including the generating AI.

The processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. The specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

Each of the multiple elements including the collection unit, analysis unit, notification unit, and summary unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14, and acquires messages using the API of each application. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected messages using the generation AI. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and immediately notifies of urgent or important matters. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a summary of the message using the generation AI. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[Second embodiment]
FIG. 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 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the 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 and/or a LAN.

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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to 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 the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied 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 performed in a secure state.

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

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.

The 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores the specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can use these models to perform processing similar to that of the specific processing unit 290.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result 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 result of the specific processing. The microphone 238 acquires audio indicating a user input for the result 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.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

Each of the multiple elements including the collection unit, analysis unit, notification unit, and summary unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214, and acquires messages using the API of each application. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected messages using the generation AI. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and immediately notifies of urgent or important matters. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a summary of the message using the generation AI. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[Third embodiment]
FIG. 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 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the 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 and/or a LAN.

The headset type 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the 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 by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to 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 the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied 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 performed in a secure state.

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

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.

The 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores the specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input for the result 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.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

Each of the multiple elements including the collection unit, analysis unit, notification unit, and summary unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314, and acquires messages using the API of each application. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected messages using the generation AI. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and immediately notifies of urgent or important matters. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a summary of the message using the generation AI. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[Fourth embodiment]
FIG. 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 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the 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 and/or a LAN.

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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to 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 image sensor or a CCD image sensor, and captures the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied 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 performed in a secure state.

The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and legs. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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.

The 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores the specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result 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 result of the specific processing. The microphone 238 acquires voice indicating the user input for the result of the specific processing. The control unit 46A transmits voice 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 voice data.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

Each of the multiple elements including the collection unit, analysis unit, notification unit, and summary unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414, and acquires messages using the API of each application. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected messages using the generation AI. The notification unit is realized, for example, by the control unit 46A of the robot 414, and immediately notifies of urgent or important matters. The summary unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a summary of the message using the generation AI. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

The emotion identification model 59, which serves as an emotion engine, may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. Emotions that represent states and actions arising from a state of mind are arranged on the outer sides of the concentric circles. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

These emotions are distributed in the three o'clock direction of 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 inside 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 emotions become.

Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery power, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions that belong to an area called "reaction" where sensation is dominant. The right half of the emotion map is lined with emotions that belong to an area called "situation" where situation recognition is dominant.

The emotion map defines two emotions that encourage learning. The first 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 has positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input to 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 learning 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 located 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, "peace of mind," "calm," and "reassuring," have similar emotion values.

In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

In the above embodiment, an example has been 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 Universal Serial Bus (USB) 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.

The specific processing program 56 may also 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 all of the 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The various processors listed below can be used as hardware resources for executing specific processes. Examples of processors include a CPU, which is a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. 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 to execute specific processes. All of these processors have built-in or connected memory, and all of these processors execute specific processes by using the memory.

The hardware resource that executes the specific process 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 (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific process 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 by combining one or more CPUs with software, and this processor functions as a hardware resource that executes a specific process. Secondly, there is a configuration in which a processor is used that realizes the functions of the entire system, including multiple hardware resources that execute a specific process, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the various processors mentioned above as hardware resources.

More specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processing described above is merely an example. It goes without saying that unnecessary steps can be deleted, new steps can be added, and the processing order can be changed without departing from the spirit of the invention.

In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are only examples, and they may be combined with each other, or may be other devices. Also, in the above example, the first and second embodiments have been described separately, but these may be combined.

The above description 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, function, action, and effect is an explanation of an example of the configuration, function, action, and effect 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 replacements may be made to the above description and illustrations, within the scope of the gist of the technology of the present disclosure. Also, in order to avoid confusion and to make it easier to understand the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense 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 into this specification to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

(Appendix 1)
A collection unit that collects messages;
an analysis unit that analyzes the messages collected by the collection unit;
a notification unit that notifies of an urgent or important matter identified by the analysis unit;
a summary unit that creates a summary of messages not identified by the analysis unit.
(Appendix 2)
The collecting unit includes:
The system described in claim 1, characterized in that messages are collected using the API of each application.
(Appendix 3)
The collecting unit includes:
The system of claim 1, further comprising: collecting messages using scraping technology.
(Appendix 4)
The analysis unit is
2. The system of claim 1, further comprising: a natural language processing technique for analyzing the content of messages and detecting specific keywords or phrases.
(Appendix 5)
The analysis unit is
2. The system of claim 1, further comprising: setting specific criteria that can be customized by the user.
(Appendix 6)
The notification unit is
The system according to claim 1, characterized in that it immediately notifies users of urgent or important matters by using push notifications or email notifications.
(Appendix 7)
The summary section includes:
The system of claim 1, further comprising: summarizing the contents of the collected messages to generate a summary.
(Appendix 8)
The collecting unit includes:
The system according to claim 1, further comprising: estimating a user's emotion; and adjusting a message collection timing based on the estimated user's emotion.
(Appendix 9)
The collecting unit includes:
2. The system according to claim 1, further comprising: analyzing a user's past message collection history and selecting an optimal collection method.
(Appendix 10)
The collecting unit includes:
2. The system of claim 1, wherein messages are collected and filtered based on the user's current projects or areas of interest.
(Appendix 11)
The collecting unit includes:
The system according to claim 1, further comprising: estimating a user's emotion; and determining a priority order of messages to be collected based on the estimated user's emotion.
(Appendix 12)
The collecting unit includes:
The system according to claim 1, characterized in that, when collecting messages, highly relevant messages are collected preferentially in consideration of the user's geographical location information.
(Appendix 13)
The collecting unit includes:
The system of claim 1, further comprising: analyzing a user's social media activity when collecting messages; and collecting related messages.
(Appendix 14)
The analysis unit is
2. The system of claim 1, further comprising: estimating a user's emotion; and adjusting a representation of the analysis based on the estimated user's emotion.
(Appendix 15)
The analysis unit is
2. The system of claim 1, further comprising: adjusting a level of detail of the analysis based on the importance of the message during analysis.
(Appendix 16)
The analysis unit is
2. The system of claim 1, wherein during analysis, different analysis algorithms are applied depending on the category of the message.
(Appendix 17)
The analysis unit is
2. The system of claim 1, further comprising: estimating a user's emotion; and adjusting a length of the analysis based on the estimated user's emotion.
(Appendix 18)
The analysis unit is
2. The system of claim 1, wherein during analysis, the system determines a priority for analysis based on when the message was sent.
(Appendix 19)
The analysis unit is
2. The system of claim 1, wherein during analysis, the order of analysis is adjusted based on message relevance.
(Appendix 20)
The notification unit is
The system of claim 1, further comprising: estimating a user's emotion; and adjusting a notification method based on the estimated user's emotion.
(Appendix 21)
The notification unit is
The system of claim 1, further comprising: adjusting the level of detail of the notification based on the importance of the message at the time of notification.
(Appendix 22)
The notification unit is
2. The system of claim 1, wherein at the time of notification, different notification algorithms are applied depending on the message category.
(Appendix 23)
The notification unit is
The system according to claim 1, further comprising: estimating a user's emotion; and adjusting a timing of notification based on the estimated user's emotion.
(Appendix 24)
The notification unit is
The system according to claim 1, further comprising: determining a priority of a notification by taking into consideration attribute information of a message sender at the time of notification.
(Appendix 25)
The notification unit is
2. The system of claim 1, wherein, at the time of notification, the order of notifications is adjusted based on the relevance of the messages.
(Appendix 26)
The summary section includes:
The system of claim 1, further comprising: estimating a user's emotion; and adjusting a summary presentation method based on the estimated user's emotion.
(Appendix 27)
The summary section includes:
The system of claim 1, further comprising: adjusting the level of detail of a summary based on the importance of the message when the summary is created.
(Appendix 28)
The summary section includes:
2. The system of claim 1, wherein different summary algorithms are applied depending on the category of the message when creating the summary.
(Appendix 29)
The summary section includes:
2. The system of claim 1, further comprising: estimating a user's emotion; and adjusting a length of the summary based on the estimated user's emotion.
(Appendix 30)
The summary section includes:
2. The system of claim 1, wherein when creating a summary, the summary is prioritized based on when the message was sent.
(Appendix 31)
The summary section includes:
2. The system of claim 1, wherein when summarizing, the order of summaries is adjusted based on the relevance of messages.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (7)

A collection unit that collects messages;
an analysis unit that analyzes the messages collected by the collection unit;
a notification unit that notifies of an urgent or important matter identified by the analysis unit;
a summary unit that creates a summary of messages not identified by the analysis unit. The collecting unit includes:
The system according to claim 1, characterized in that messages are collected by utilizing an API of each application. The collecting unit includes:
The system of claim 1, further comprising: collecting messages using scraping technology. The analysis unit is
10. The system of claim 1, further comprising: a processor that uses natural language processing techniques to analyze the content of messages and detect specific keywords or phrases. The analysis unit is
The system of claim 1, further comprising: setting specific criteria that are user customizable. The notification unit is
The system according to claim 1, further comprising: a push notification or an email notification that immediately notifies the user of urgent or important matters. The collecting unit includes:
The system according to claim 1 , further comprising: estimating a user's emotion; and adjusting a message collection timing based on the estimated user's emotion.

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