JP2026018448A - system - Google Patents

Abstract

The system according to the embodiment aims to enable general participants who do not have game creation skills to participate in a game planning contest.
[Solution] A system according to an embodiment includes a prompt analysis unit, a game generation unit, and an evaluation unit. The prompt analysis unit analyzes prompts entered by participants. The game generation unit generates a game based on the results of the analysis by the prompt analysis unit. The evaluation unit evaluates the game generated by the game generation unit.
[Selected Figure] Figure 1

Description

The technology disclosed herein relates to a system.

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

Japanese Patent Application Laid-Open No. 2022-180282

With conventional technology, it was difficult for general participants who did not have game development skills to participate in game planning contests.

The system according to this embodiment aims to enable general participants who do not have game development skills to participate in game planning contests.

The system according to the embodiment includes a prompt analysis unit, a game generation unit, and an evaluation unit. The prompt analysis unit analyzes prompts entered by participants. The game generation unit generates a game based on the results of the analysis by the prompt analysis unit. The evaluation unit evaluates the game generated by the game generation unit.

The system according to the embodiment allows general participants who do not have game development skills to participate in game planning contests.

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

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

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

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

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

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

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

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

[First embodiment]
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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

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

The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input via the pointer. The microphone 38B detects the user's voice to receive user input via voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see Figure 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 that the user can perceive (e.g., audio and/or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores the 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 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 device 14 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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 communicates with the server device having the data generation model 58 to obtain processing results (prediction results, etc.) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

(Example 1)
A game planning contest system according to an embodiment of the present invention is a system in which even general participants without game development skills can participate. This system utilizes AI to enable game development based on simple prompts, and achieves a more accurate evaluation of game plans than general planning contests. As a result, the game planning contest system realizes a contest in which even general participants without game development skills can participate, and utilizes AI to enable game development based on simple prompts, and achieves a more accurate evaluation of game plans than general planning contests.

The game planning contest system according to the embodiment includes a prompt analysis unit, a game generation unit, and an evaluation unit. The prompt analysis unit analyzes prompts entered by participants. For example, the prompt analysis unit analyzes prompts entered by participants, such as "an RPG with an adventure in a fantasy world" or "a shooting game set in space." The prompt analysis unit can also analyze prompts using natural language processing technology. For example, the prompt analysis unit analyzes prompts using a text generation AI (e.g., LLM). The game generation unit generates a game based on the results of the analysis by the prompt analysis unit. For example, the game generation unit uses a generation AI to automatically generate game graphics, music, character models, stage designs, etc., and combines them to create a playable demo. The game generation unit can also generate each element of the game using a multimodal generation AI. For example, the generation AI generates specific game designs, storylines, characters, and gameplay elements based on the prompts entered by participants. The evaluation unit evaluates the games generated by the game generation unit. For example, the evaluation unit evaluates the game's completeness, originality, playability, etc. based on the playable demo created by the generation AI. The evaluation unit can also analyze play data to evaluate the game's balance, difficulty, user reaction, etc. For example, the evaluation unit analyzes play data from the playable demo created by the generation AI and evaluates the game's balance, difficulty, user reaction, etc. As a result, the game planning contest system according to the embodiment realizes a contest that is open to general participants who do not have game development skills, enables game development through simple prompts using AI, and can achieve a higher level of project evaluation accuracy than general planning contests.

The prompt analysis unit can learn the participant's past input history and preferences and generate more personalized game elements. For example, the generation AI analyzes the participant's past prompt input history and learns their individual preferences and tendencies. For example, for a participant who has input many fantasy prompts in the past, it will generate game elements with enhanced fantasy elements. The prompt analysis unit can also learn the participant's preferences and generate more personalized game elements. For example, the generation AI will generate characters and story elements that the participant prefers based on the participant's past selection history. This makes it possible to generate game elements that suit the participant's preferences.

The prompt analysis unit can refer to existing relevant game databases and propose optimal game designs based on past examples of success and failure. For example, the generation AI refers to existing game databases and analyzes prompts based on past examples of success and failure. For example, it can propose a game design that incorporates elements of successful RPG games. The prompt analysis unit can also propose optimal game designs based on past examples of success and failure. For example, the generation AI can propose game designs that improve user experience and adjust balance based on past game databases. This makes it possible to propose optimal game designs based on past examples of success and failure.

The prompt input format can be diversified, allowing not only text but also voice and image input. In the prompt analysis unit, for example, the generation AI analyzes voice input and generates game elements based on what the participants have said. For example, it generates a game story based on the story told by the participants. The prompt analysis unit can also analyze image input and generate game elements based on illustrations drawn by participants. For example, it generates a character model based on a character illustration drawn by a participant. This makes it possible to accommodate a variety of input formats.

The prompt analysis unit performs prompt analysis of hybrid games that combine game elements from different genres, and can create new game genres. For example, the generation AI analyzes prompts that combine game elements from different genres, and creates new game genres. For example, it generates a hybrid game that combines an RPG and a shooting game. The prompt analysis unit can also perform prompt analysis of prompts that combine game elements from different genres, and create new game genres. For example, the generation AI generates a hybrid game that combines an action game and a puzzle game. This makes it possible to create new game genres.

The generation AI can create characters with more depth by setting detailed backstories and personalities. For example, the generation AI can create characters with more depth by setting detailed backstories. For example, it can set the character's past events and relationships. The generation AI can also create characters with more depth by setting detailed personalities. For example, it can set the character's personality traits and behavior patterns. This allows it to create characters with more depth.

Generation AI can generate multicultural games that incorporate different cultures and histories in game design and story generation. For example, generation AI can generate games that incorporate different cultures and histories. For example, it can generate an RPG that incorporates traditional Asian culture. Generation AI can also generate multicultural games that incorporate different cultures and histories in game design and story generation. For example, generation AI can generate a story based on European history. This makes it possible to generate multicultural games.

The generation AI can add a collaboration function that allows the generated game elements to be shared with other participants and for them to jointly create a game. The generation AI can add a collaboration function that allows the generated game elements to be shared with other participants and for them to jointly create a game. For example, multiple participants can jointly create a story. The generation AI can also add a collaboration function that allows the generated game elements to be shared with other participants and for them to jointly create a game. For example, the generation AI can provide a real-time collaborative editing function. This allows them to jointly create a game.

The generation AI can analyze the play data of a playable demo and evaluate the game's replayability and user engagement. For example, the generation AI can analyze the play data of a playable demo and evaluate the game's replayability. For example, it can identify elements that make players want to play the game multiple times. The generation AI can also analyze the play data of a playable demo and evaluate user engagement. For example, it can evaluate how engaged the user is with the game. This makes it possible to evaluate replayability and user engagement.

Generative AI can add a game's social impact and educational value to its evaluation criteria, allowing for more multifaceted evaluations. For example, to evaluate a game's social impact, generative AI can analyze play data and identify the game's impact on society. For example, it can evaluate elements that raise awareness of environmental and social issues. Generative AI can also analyze play data to identify the game's impact on education to evaluate a game's educational value. For example, it can evaluate the learning effect and knowledge provided. This allows for the evaluation of social impact and educational value.

The generation AI can evaluate the playable demo for user groups of different ages and genders, allowing evaluations from a variety of perspectives. For example, the generation AI can evaluate the playable demo for user groups of different ages. For example, it can collect opinions from a wide range of ages, from children to adults. The generation AI can also evaluate the playable demo for user groups of different genders. For example, it can collect opinions from both men and women. This allows evaluations from a variety of perspectives.

The generation AI can automatically suggest improvements to the game based on the evaluation results and reflect them in the next prompt input. The generation AI can automatically suggest improvements to the game based on the evaluation results. For example, it can analyze play data and suggest balance adjustments or difficulty adjustments. The generation AI can also reflect the evaluation results in the next prompt input. For example, when a participant enters a prompt again, it can suggest improvements based on the previous evaluation results. This makes it possible to suggest improvements to the game based on the evaluation results.

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

The prompt analysis unit analyzes prompts entered by participants. For example, the prompt analysis unit analyzes prompts entered by participants such as "a fantasy adventure RPG" or "a space shooter." The prompt analysis unit can also analyze prompts using natural language processing technology. For example, the prompt analysis unit analyzes prompts using text generation AI (e.g., LLM). The game generation unit generates a game based on the results of the analysis by the prompt analysis unit. For example, the game generation unit automatically generates game graphics, music, character models, stage designs, etc., and combines them to create a playable demo. The game generation unit can also generate each element of the game using multimodal generation AI. For example, the generation AI generates specific game design, story, characters, and gameplay elements based on the prompts entered by participants. The evaluation unit evaluates the game generated by the game generation unit. For example, the evaluation unit evaluates the game's completeness, originality, playability, etc. based on the playable demo created by the generation AI. The evaluation unit can also analyze play data and evaluate the game's balance, difficulty, user reaction, etc. For example, the evaluation unit analyzes play data from a playable demo generated by the generation AI and evaluates the game's balance, difficulty, user reaction, etc. As a result, the game planning contest system according to the embodiment realizes a contest that even general participants without game production skills can participate in, utilizes AI to enable game production from simple prompts, and can achieve a higher level of accuracy in the evaluation of plans than general planning contests.

The prompt analysis unit can learn from participants' past input history and preferences to generate more personalized game elements. For example, the generation AI analyzes participants' past prompt input history to learn their individual preferences and tendencies. For example, for a participant who has input many fantasy-related prompts in the past, it can generate game elements with enhanced fantasy elements. The prompt analysis unit can also learn participants' preferences to generate more personalized game elements. For example, the generation AI can generate characters and story elements that participants prefer based on the participant's past selection history. This makes it possible to generate game elements that suit the participant's preferences.

The prompt analysis unit can refer to a database of existing related games and propose optimal game designs based on past examples of success and failure. For example, the generation AI can refer to an existing game database and analyze prompts based on past examples of success and failure. For example, it can propose a game design that incorporates elements of a successful RPG game. The prompt analysis unit can also propose optimal game designs based on past examples of success and failure. For example, the generation AI can propose game designs that improve the user experience and adjust the balance based on a database of past games. This makes it possible to propose optimal game designs based on past examples of success and failure.

The prompt input format can be diversified, allowing not only text but also voice and image input. For example, the generation AI analyzes voice input and generates game elements based on what the participants have said. For example, it can generate a game story based on the story told by the participants. The prompt analysis unit can also analyze image input and generate game elements based on illustrations drawn by participants. For example, it can generate a character model based on an illustration of a character drawn by a participant. This makes it possible to accommodate a variety of input formats.

The prompt analysis unit analyzes prompts for hybrid games that combine game elements from different genres, and can create new game genres. For example, the generation AI analyzes prompts that combine game elements from different genres and creates new game genres. For example, it generates a hybrid game that combines an RPG and a shooting game. The prompt analysis unit can also analyze prompts that combine game elements from different genres and create new game genres. For example, the generation AI generates a hybrid game that combines an action game and a puzzle game. This makes it possible to create new game genres.

The generation AI can create characters with more depth by setting detailed backstories and personalities. For example, it can set a character's backstory in detail to create characters with more depth. For example, it can set a character's past events and relationships. The generation AI can also create characters with more depth by setting detailed personalities. For example, it can set a character's personality traits and behavior patterns. This allows it to create characters with more depth.

Generation AI can generate multicultural games that incorporate different cultures and histories in game design and story generation. For example, it can generate games that incorporate different cultures and histories. For example, it can generate an RPG that incorporates traditional Asian culture. Generative AI can also generate multicultural games that incorporate different cultures and histories in game design and story generation. For example, it can generate a story based on European history. This makes it possible to generate multicultural games.

The processing flow for Example 1 is briefly explained below.

Step 1: The prompt analyzer analyzes the prompt entered by the participant. For example, the prompt analyzer analyzes prompts entered by the participant, such as "a fantasy adventure RPG" or "a space shooter." The prompt analyzer can also analyze the prompt using natural language processing technology. For example, the prompt analyzer analyzes the prompt using a text generation AI (e.g., LLM).
Step 2: The game generation unit generates a game based on the results of the analysis by the prompt analysis unit. For example, the game generation unit uses a generation AI to automatically generate game graphics, music, character models, stage designs, etc., and combines them to create a playable demo. The game generation unit can also use multimodal generation AI to generate each element of the game. For example, the generation AI generates specific game designs, storylines, characters, and gameplay elements based on prompts entered by participants.
Step 3: The evaluation unit evaluates the game generated by the game generation unit. For example, the evaluation unit evaluates the game from the perspectives of completeness, originality, playability, etc. based on the playable demo created by the generation AI. The evaluation unit can also analyze play data and evaluate the game's balance, difficulty, user response, etc. For example, the evaluation unit analyzes play data of the playable demo created by the generation AI and evaluates the game's balance, difficulty, user response, etc.

(Example 2)
A game planning contest system according to an embodiment of the present invention is a system in which even general participants without game development skills can participate. This system utilizes AI to enable game development based on simple prompts, and achieves a more accurate evaluation of game plans than general planning contests. As a result, the game planning contest system realizes a contest in which even general participants without game development skills can participate, and utilizes AI to enable game development based on simple prompts, and achieves a more accurate evaluation of game plans than general planning contests.

The game planning contest system according to the embodiment includes a prompt analysis unit, a game generation unit, and an evaluation unit. The prompt analysis unit analyzes prompts entered by participants. For example, the prompt analysis unit analyzes prompts entered by participants, such as "an RPG with an adventure in a fantasy world" or "a shooting game set in space." The prompt analysis unit can also analyze prompts using natural language processing technology. For example, the prompt analysis unit analyzes prompts using a text generation AI (e.g., LLM). The game generation unit generates a game based on the results of the analysis by the prompt analysis unit. For example, the game generation unit uses a generation AI to automatically generate game graphics, music, character models, stage designs, etc., and combines them to create a playable demo. The game generation unit can also generate each element of the game using a multimodal generation AI. For example, the generation AI generates specific game designs, storylines, characters, and gameplay elements based on the prompts entered by participants. The evaluation unit evaluates the games generated by the game generation unit. For example, the evaluation unit evaluates the game's completeness, originality, playability, etc. based on the playable demo created by the generation AI. The evaluation unit can also analyze play data to evaluate the game's balance, difficulty, user reaction, etc. For example, the evaluation unit analyzes play data from the playable demo created by the generation AI and evaluates the game's balance, difficulty, user reaction, etc. As a result, the game planning contest system according to the embodiment realizes a contest that is open to general participants who do not have game development skills, enables game development through simple prompts using AI, and can achieve a higher level of project evaluation accuracy than general planning contests.

The prompt analysis unit can learn the participant's past input history and preferences and generate more personalized game elements. For example, the generation AI analyzes the participant's past prompt input history and learns their individual preferences and tendencies. For example, for a participant who has input many fantasy prompts in the past, it will generate game elements with enhanced fantasy elements. The prompt analysis unit can also learn the participant's preferences and generate more personalized game elements. For example, the generation AI will generate characters and story elements that the participant prefers based on the participant's past selection history. This makes it possible to generate game elements that suit the participant's preferences.

The prompt analysis unit can refer to existing relevant game databases and propose optimal game designs based on past examples of success and failure. For example, the generation AI refers to existing game databases and analyzes prompts based on past examples of success and failure. For example, it can propose a game design that incorporates elements of successful RPG games. The prompt analysis unit can also propose optimal game designs based on past examples of success and failure. For example, the generation AI can propose game designs that improve user experience and adjust balance based on past game databases. This makes it possible to propose optimal game designs based on past examples of success and failure.

The prompt analysis unit uses the emotion estimation function to analyze the emotions participants have when entering prompts, and can generate game elements that elicit positive emotions. For example, the prompt analysis unit uses a generation AI to analyze the participants' facial expressions and voices and estimate the emotions they had when entering the prompt. For example, if the emotion is strong, it generates game elements that reinforce that emotion. The prompt analysis unit can also use the emotion estimation function to analyze the emotions participants have when entering prompts, and generate game elements that elicit positive emotions. For example, the generation AI adjusts the reward system and story development based on the participants' emotions. This makes it possible to generate game elements that elicit positive emotions from participants.

The prompt input format can be diversified, allowing not only text but also voice and image input. In the prompt analysis unit, for example, the generation AI analyzes voice input and generates game elements based on what the participants have said. For example, it generates a game story based on the story told by the participants. The prompt analysis unit can also analyze image input and generate game elements based on illustrations drawn by participants. For example, it generates a character model based on a character illustration drawn by a participant. This makes it possible to accommodate a variety of input formats.

The prompt analysis unit performs prompt analysis of hybrid games that combine game elements from different genres, and can create new game genres. For example, the generation AI analyzes prompts that combine game elements from different genres, and creates new game genres. For example, it generates a hybrid game that combines an RPG and a shooting game. The prompt analysis unit can also perform prompt analysis of prompts that combine game elements from different genres, and create new game genres. For example, the generation AI generates a hybrid game that combines an action game and a puzzle game. This makes it possible to create new game genres.

Using the emotion estimation function, the emotion of the participant when entering the prompt can be fed back in real time, and the optimal prompt can be suggested. For example, the generation AI in the prompt analysis unit analyzes the participant's emotion in real time and suggests a prompt that elicits positive emotions. For example, an encouraging message can be displayed if the emotion score is low. The prompt analysis unit can also use the emotion estimation function to provide real time feedback on the participant's emotion when entering the prompt, and suggest the optimal prompt. For example, the generation AI adjusts the content of the prompt based on the participant's emotion. This makes it possible to suggest the optimal prompt.

The generation AI can create characters with more depth by setting detailed backstories and personalities. For example, the generation AI can create characters with more depth by setting detailed backstories. For example, it can set the character's past events and relationships. The generation AI can also create characters with more depth by setting detailed personalities. For example, it can set the character's personality traits and behavior patterns. This allows it to create characters with more depth.

The emotion estimation function can be used to analyze participants' emotional reactions to generated game elements, and strengthen elements that elicit positive reactions. The generation AI, for example, uses the emotion estimation function to analyze participants' emotional reactions to generated game elements. For example, elements that elicit a large number of positive reactions can be strengthened. The generation AI can also use the emotion estimation function to analyze participants' emotional reactions to generated game elements, and strengthen elements that elicit positive reactions. For example, the generation AI can adjust the reward system or story development based on participants' emotional reactions. This can strengthen elements that elicit positive reactions.

Generation AI can generate multicultural games that incorporate different cultures and histories in game design and story generation. For example, generation AI can generate games that incorporate different cultures and histories. For example, it can generate an RPG that incorporates traditional Asian culture. Generation AI can also generate multicultural games that incorporate different cultures and histories in game design and story generation. For example, generation AI can generate a story based on European history. This makes it possible to generate multicultural games.

The generation AI can add a collaboration function that allows the generated game elements to be shared with other participants and for them to jointly create a game. The generation AI can add a collaboration function that allows the generated game elements to be shared with other participants and for them to jointly create a game. For example, multiple participants can jointly create a story. The generation AI can also add a collaboration function that allows the generated game elements to be shared with other participants and for them to jointly create a game. For example, the generation AI can provide a real-time collaborative editing function. This allows them to jointly create a game.

The emotion estimation function can be used to collect the emotional reactions of other participants to the generated game elements, and the elements can be improved based on the feedback. For example, the generation AI can use the emotion estimation function to collect the emotional reactions of other participants to the generated game elements. For example, it can strengthen elements that receive a lot of positive reactions. The generation AI can also use the emotion estimation function to collect the emotional reactions of other participants to the generated game elements, and improve the elements based on the feedback. For example, the generation AI can adjust game mechanics or story development based on the emotional reactions of other participants. This allows the elements to be improved based on the feedback.

The generation AI can analyze the play data of a playable demo and evaluate the game's replayability and user engagement. For example, the generation AI can analyze the play data of a playable demo and evaluate the game's replayability. For example, it can identify elements that make players want to play the game multiple times. The generation AI can also analyze the play data of a playable demo and evaluate user engagement. For example, it can evaluate how engaged the user is with the game. This makes it possible to evaluate replayability and user engagement.

Generative AI can add a game's social impact and educational value to its evaluation criteria, allowing for more multifaceted evaluations. For example, to evaluate a game's social impact, generative AI can analyze play data and identify the game's impact on society. For example, it can evaluate elements that raise awareness of environmental and social issues. Generative AI can also analyze play data to identify the game's impact on education to evaluate a game's educational value. For example, it can evaluate the learning effect and knowledge provided. This allows for the evaluation of social impact and educational value.

The generation AI can evaluate the playable demo for user groups of different ages and genders, allowing evaluations from a variety of perspectives. For example, the generation AI can evaluate the playable demo for user groups of different ages. For example, it can collect opinions from a wide range of ages, from children to adults. The generation AI can also evaluate the playable demo for user groups of different genders. For example, it can collect opinions from both men and women. This allows evaluations from a variety of perspectives.

The generation AI can automatically suggest improvements to the game based on the evaluation results and reflect them in the next prompt input. The generation AI can automatically suggest improvements to the game based on the evaluation results. For example, it can analyze play data and suggest balance adjustments or difficulty adjustments. The generation AI can also reflect the evaluation results in the next prompt input. For example, when a participant enters a prompt again, it can suggest improvements based on the previous evaluation results. This makes it possible to suggest improvements to the game based on the evaluation results.

The emotion estimation function can be used to monitor the user's emotional reactions during evaluation in real time and dynamically adjust the evaluation criteria. The generation AI, for example, uses the emotion estimation function to monitor the user's emotional reactions during evaluation in real time. For example, it can relax the evaluation criteria if positive emotions are strong. The generation AI can also use the emotion estimation function to monitor the user's emotional reactions during evaluation in real time and dynamically adjust the evaluation criteria. For example, it can tighten the evaluation criteria if negative emotions are strong. This allows the evaluation criteria to be dynamically adjusted.

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

The prompt analysis unit analyzes prompts entered by participants. For example, the prompt analysis unit analyzes prompts entered by participants such as "a fantasy adventure RPG" or "a space shooter." The prompt analysis unit can also analyze prompts using natural language processing technology. For example, the prompt analysis unit analyzes prompts using text generation AI (e.g., LLM). The game generation unit generates a game based on the results of the analysis by the prompt analysis unit. For example, the game generation unit automatically generates game graphics, music, character models, stage designs, etc., and combines them to create a playable demo. The game generation unit can also generate each element of the game using multimodal generation AI. For example, the generation AI generates specific game design, story, characters, and gameplay elements based on the prompts entered by participants. The evaluation unit evaluates the game generated by the game generation unit. For example, the evaluation unit evaluates the game's completeness, originality, playability, etc. based on the playable demo created by the generation AI. The evaluation unit can also analyze play data and evaluate the game's balance, difficulty, user reaction, etc. For example, the evaluation unit analyzes play data from a playable demo generated by the generation AI and evaluates the game's balance, difficulty, user reaction, etc. As a result, the game planning contest system according to the embodiment realizes a contest that even general participants without game production skills can participate in, utilizes AI to enable game production from simple prompts, and can achieve a higher level of accuracy in the evaluation of plans than general planning contests.

The prompt analysis unit can learn from participants' past input history and preferences to generate more personalized game elements. For example, the generation AI analyzes participants' past prompt input history to learn their individual preferences and tendencies. For example, for a participant who has input many fantasy-related prompts in the past, it can generate game elements with enhanced fantasy elements. The prompt analysis unit can also learn participants' preferences to generate more personalized game elements. For example, the generation AI can generate characters and story elements that participants prefer based on the participant's past selection history. This makes it possible to generate game elements that suit the participant's preferences.

The prompt analysis unit can refer to a database of existing related games and propose optimal game designs based on past examples of success and failure. For example, the generation AI can refer to an existing game database and analyze prompts based on past examples of success and failure. For example, it can propose a game design that incorporates elements of a successful RPG game. The prompt analysis unit can also propose optimal game designs based on past examples of success and failure. For example, the generation AI can propose game designs that improve the user experience and adjust the balance based on a database of past games. This makes it possible to propose optimal game designs based on past examples of success and failure.

The prompt analysis unit can use the emotion estimation function to analyze the emotions participants have when entering prompts, and generate game elements that elicit positive emotions. For example, the generation AI analyzes the participant's facial expressions and voice to estimate the emotions they had when entering the prompt. For example, if the positive emotions were strong, it can generate game elements that reinforce those emotions. The prompt analysis unit can also use the emotion estimation function to analyze the emotions participants have when entering prompts, and generate game elements that elicit positive emotions. For example, the generation AI can adjust the reward system or story development based on the participants' emotions. This makes it possible to generate game elements that elicit positive emotions from participants.

The prompt input format can be diversified, allowing not only text but also voice and image input. For example, the generation AI analyzes voice input and generates game elements based on what the participants have said. For example, it can generate a game story based on the story told by the participants. The prompt analysis unit can also analyze image input and generate game elements based on illustrations drawn by participants. For example, it can generate a character model based on an illustration of a character drawn by a participant. This makes it possible to accommodate a variety of input formats.

The prompt analysis unit analyzes prompts for hybrid games that combine game elements from different genres, and can create new game genres. For example, the generation AI analyzes prompts that combine game elements from different genres and creates new game genres. For example, it generates a hybrid game that combines an RPG and a shooting game. The prompt analysis unit can also analyze prompts that combine game elements from different genres and create new game genres. For example, the generation AI generates a hybrid game that combines an action game and a puzzle game. This makes it possible to create new game genres.

The emotion estimation function can be used to provide real-time feedback on the participant's emotions when entering a prompt, and to suggest the most appropriate prompt. For example, the generation AI can analyze the participant's emotions in real time and suggest prompts that elicit positive emotions. For example, an encouraging message can be displayed if the emotion score is low. The prompt analysis unit can also use the emotion estimation function to provide real-time feedback on the participant's emotions when entering a prompt, and to suggest the most appropriate prompt. For example, the generation AI can adjust the content of the prompt based on the participant's emotions. This makes it possible to suggest the most appropriate prompt.

The generation AI can create characters with more depth by setting detailed backstories and personalities. For example, it can set a character's backstory in detail to create characters with more depth. For example, it can set a character's past events and relationships. The generation AI can also create characters with more depth by setting detailed personalities. For example, it can set a character's personality traits and behavior patterns. This allows it to create characters with more depth.

The emotion estimation function can be used to analyze participants' emotional responses to generated game elements, and strengthen elements that elicit positive responses. For example, the emotion estimation function can be used to analyze participants' emotional responses to generated game elements. For example, elements that elicit a large number of positive responses can be strengthened. The generation AI can also use the emotion estimation function to analyze participants' emotional responses to generated game elements, and strengthen elements that elicit positive responses. For example, the generation AI can adjust the reward system or story development based on participants' emotional responses. This can strengthen elements that elicit positive responses.

Generation AI can generate multicultural games that incorporate different cultures and histories in game design and story generation. For example, it can generate games that incorporate different cultures and histories. For example, it can generate an RPG that incorporates traditional Asian culture. Generative AI can also generate multicultural games that incorporate different cultures and histories in game design and story generation. For example, it can generate a story based on European history. This makes it possible to generate multicultural games.

The processing flow for Example 2 is briefly explained below.

Step 1: The prompt analyzer analyzes the prompt entered by the participant. For example, the prompt analyzer analyzes prompts entered by the participant, such as "a fantasy adventure RPG" or "a space shooter." The prompt analyzer can also analyze the prompt using natural language processing technology. For example, the prompt analyzer analyzes the prompt using a text generation AI (e.g., LLM).
Step 2: The game generation unit generates a game based on the results of the analysis by the prompt analysis unit. For example, the game generation unit uses a generation AI to automatically generate game graphics, music, character models, stage designs, etc., and combines them to create a playable demo. The game generation unit can also use multimodal generation AI to generate each element of the game. For example, the generation AI generates specific game designs, storylines, characters, and gameplay elements based on prompts entered by participants.
Step 3: The evaluation unit evaluates the game generated by the game generation unit. For example, the evaluation unit evaluates the game from the perspectives of completeness, originality, playability, etc. based on the playable demo created by the generation AI. The evaluation unit can also analyze play data and evaluate the game's balance, difficulty, user response, etc. For example, the evaluation unit analyzes play data of the playable demo created by the generation AI and evaluates the game's balance, difficulty, user response, etc.

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

The data generation model 58 is what is known as generative AI (artificial intelligence). An example of a data generation model 58 is generative AI such as ChatGPT (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 containing an instruction is input to the data generation model 58, and inference data such as voice data representing speech, text data representing text, and image data representing an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AI other than the generation AI. Examples of AI other than the generation AI include linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes, and can perform a variety of processes, but are not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. The processing performed by AI, including the generation AI, may be replaced with rule-based processing.

Furthermore, 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. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, etc., and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN 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, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

The microphone 238 receives instructions and other information 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 audio in accordance with instructions from the processor 46.

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

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

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

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

In the smart glasses 214, specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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 communicates with the server device having the data generation model 58 to obtain processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

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

The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12, etc., includes multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than the generative 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 can perform a variety of processes, but is not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. The processing performed by AI, including the generative AI, may be replaced with rule-based processing.

The data processing system 210 according to the second embodiment performs processing similar to that of the data processing system 10 according to the first embodiment. 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 also 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 necessary for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

[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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN and/or a LAN.

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

The microphone 238 receives instructions and other information 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 audio in accordance with instructions from the processor 46.

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

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

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

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

In the headset type terminal 314, the specific processing is performed by the processor 46. A specific program 60 is stored in the storage 50. 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. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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 communicates with the server device having the data generation model 58 to obtain processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

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

The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12, etc., includes multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than the generative 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 can perform a variety of processes, but is not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. The processing performed by AI, including the generative AI, may be replaced with rule-based processing.

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. 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 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 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 terminal 314 or an external device, and the headset terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN 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, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

The microphone 238 receives instructions and other information 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 audio in accordance with instructions from the processor 46.

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

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

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

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

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

In robot 414, specific processing is performed by processor 46. Specific program 60 is stored in storage 50. Processor 46 reads specific program 60 from storage 50 and executes the read specific program 60 on RAM 48. The specific processing is realized by processor 46 operating as control unit 46A in accordance with specific program 60 executed on RAM 48. Note that robot 414 may have a data generation model and emotion identification model similar to data generation model 58 and emotion identification model 59.

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 communicates with the server device having the data generation model 58 to obtain processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the results of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12, etc., includes multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than the generative 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 can perform a variety of processes, but is not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. The processing performed by AI, including the generative AI, may be replaced with rule-based processing.

The data processing system 410 according to the fourth embodiment performs processing similar to that of the data processing system 10 according to the first embodiment. 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. Furthermore, 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.

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

Figure 9 shows an emotion map 400 on which multiple emotions are mapped. In emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both emotions and mental states. Emotions that are generally generated from reactions that occur in the brain are arranged on the left side of the concentric circles. Emotions that are generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, 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, emotion map 400 maps multiple emotions based on the structure by which emotions are generated, with emotions that tend to occur simultaneously being mapped close together.

These emotions are distributed in the 3 o'clock direction on emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a calm impression.

The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible (expressed in behavior) the emotion becomes.

Here, human emotions are based on various balances such as posture and blood sugar levels, and when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery life, and when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotion maps can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and emotional brain physiological signal analysis systems, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions belonging to an area called "reaction," where sensation is dominant. The right half of the emotion map is lined with emotions belonging to an area called "situation," where situational awareness is dominant.

The emotion map defines two emotions that encourage learning. One is the negative emotion around the middle of "repentance" or "reflection" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are close to each other have similar values, as in the emotion map 900 shown in Figure 10. Figure 10 shows an example in which multiple emotions, such as "relieved," "calm," and "reassuring," have similar emotion values.

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

In the above embodiment, an example was described in which the specific processing program 56 is stored in the storage 32, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

It is not necessary to store the entire specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entire specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The following types of processors can be used as hardware resources for executing specific processes. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically for executing specific processes. All processors have built-in or connected memory, and all use the memory to execute specific processes.

The hardware resource that executes the specific processing may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific processing may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured using a combination of one or more CPUs and software, and this processor functions as a hardware resource that executes specific processing. Second, there is a configuration in which a processor is used to realize the functions of an entire system, including multiple hardware resources that execute specific processing, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, specific processing is realized using one or more of the various processors listed above as hardware resources.

More specifically, the hardware structure of these various processors can be an electrical circuit that combines circuit elements such as semiconductor devices. Furthermore, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps can be added, or the processing order can be rearranged, all within the scope of the spirit of the invention.

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

The above-described written content and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above explanation of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or substitutions may be made to the above-described written content and illustrations, as long as they do not deviate from the spirit of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described written content and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

All publications, patent applications, and technical standards mentioned in this specification are incorporated by reference herein to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

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

Claims (5)

a prompt analysis unit that analyzes the prompts entered by the participants;
a game generation unit that generates a game based on the results of the analysis by the prompt analysis unit;
and an evaluation unit that evaluates the game generated by the game generation unit. The system according to claim 1, wherein the prompt input format is diversified to allow not only text but also voice input and image input. a prompt analysis unit that analyzes the prompts entered by the participants;
a game generation unit that generates a game based on the results of the analysis by the prompt analysis unit;
and an evaluation unit that evaluates the game generated by the game generation unit. The generated AI is
The system of claim 1, further comprising: analyzing play data of a playable demo to evaluate replayability and user engagement of the game. The prompt analysis unit
The system of claim 1, further comprising: an emotion estimation function for analyzing the emotion of the participant when entering the prompt; and generating game elements that elicit the positive emotion.