System

Abstract

An advantage of some aspects of the invention is to provide a system that allows a general participant who does not have a skill in game production to participate in a game planning contest.SOLUTION: A system includes a prompt analysis unit, a game generation unit, and an evaluation unit. The prompt analysis unit analyzes a prompt input by a participant. The game generation section generates a game based on a result of the analysis by the prompt analysis section. The evaluation unit evaluates the game generated by the game generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that it is difficult for general participants who do not have game creation skills to participate in game planning contests.

[0005] The system according to the embodiment aims to enable general participants who do not have game creation skills to participate in the game planning contest. [Means for solving the problem]

[0006] 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 input 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. [Effects of the Invention]

[0007] The system according to the embodiment allows general participants who do not have game creation skills to participate in the game planning contest. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] 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. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] 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. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] 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. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, 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).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. 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).

[0015] 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] 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, RAM 30, and 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).

[0019] 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.

[0020] 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 (for example, a pen or a finger) to receive user input by the touch of the pointer. 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.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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 process 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.

[0025] 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.

[0026] 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 together 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. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] 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. Furthermore, 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.

[0028] (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.

[0029] A game planning contest system according to an embodiment includes a prompt analysis unit, a game generation unit, and an evaluation unit. The prompt analysis unit analyzes prompts input by participants. For example, the prompt analysis unit analyzes prompts input 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 a game using a multimodal generation AI. For example, the generation AI generates specific game designs, storylines, characters, and gameplay elements based on the prompts input by participants. 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 to evaluate the game's balance, difficulty, user reaction, 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 reaction, etc. As a result, the game planning contest system according to the embodiment realizes a contest that can be participated in by general participants who do not have game development skills, enables game development through simple prompts using AI, and can achieve a higher level of evaluation of plans than general planning contests.

[0030] 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 in the prompt analysis unit analyzes the participant's past prompt input history and learns individual preferences and tendencies. For example, for a participant who has input many fantasy prompts in the past, the prompt analysis unit can 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 generates characters and story elements that the participant likes based on the participant's past selection history. This makes it possible to generate game elements that match the participant's preferences.

[0031] The prompt analysis unit can refer to a related existing game database and propose an optimal game design based on past examples of success and failure. In the prompt analysis unit, for example, the generation AI refers to an existing game database and analyzes prompts based on past examples of success and failure. For example, it proposes a game design that incorporates elements of successful RPG games. The prompt analysis unit can also propose an optimal game design based on past examples of success and failure. For example, the generation AI proposes a game design that improves the user experience and adjusts the balance based on the past game database. This makes it possible to propose an optimal game design based on past examples of success and failure.

[0032] 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 support a variety of input formats.

[0033] 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 games 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 allows a new game genre to be created.

[0034] The generation AI can create characters with more depth by setting the character's backstory and personality in detail. The generation AI can, for example, set the character's past events and relationships. The generation AI can also create characters with more depth by setting the character's personality in detail. For example, it can set the character's personality traits and behavior patterns. This allows for the creation of characters with more depth.

[0035] Generative AI can generate multicultural games that incorporate different cultures and histories in game design and story generation. For example, generative AI 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, generative AI can generate a story based on European history. This makes it possible to generate multicultural games.

[0036] 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.

[0037] The generation AI can analyze the play data of the playable demo and evaluate the replayability and user engagement of the game. The generation AI can, for example, analyze the play data of the playable demo and evaluate the replayability of the game. 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 the playable demo and evaluate user engagement. For example, it can evaluate how involved the user is in the game. This makes it possible to evaluate replayability and user engagement.

[0038] Generative AI can add the social impact and educational value of a game to its evaluation criteria, enabling 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 the game's educational value. For example, it can evaluate the learning effect and knowledge provided. This allows it to evaluate social impact and educational value.

[0039] The generation AI evaluates the playable demo for user groups of different age groups and genders, allowing evaluations from diverse perspectives. The generation AI, for example, evaluates the playable demo for user groups of different age groups. For example, it collects 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 collects opinions from both males and females. This allows evaluations from diverse perspectives.

[0040] 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 inputs 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.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The prompt analysis unit analyzes prompts entered by participants. For example, the prompt analysis unit analyzes prompts entered by participants such as "an adventure RPG in a fantasy world" 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 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 a game using a 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 response, etc. For example, the evaluation unit analyzes play data of a playable demo generated by the generation AI and evaluates the game's balance, difficulty, user response, etc. As a result, the game planning contest system according to the embodiment realizes a contest that even general participants without game development skills can participate in, enables game development through simple prompts using AI, and can achieve a higher level of project evaluation accuracy than general planning contests.

[0043] 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 can 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 can generate characters and story elements that the participant likes based on the participant's past selection history. This makes it possible to generate game elements that match the participant's preferences.

[0044] The prompt analysis unit can refer to a related existing game database 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 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 the past game database. This makes it possible to propose optimal game designs based on past examples of success and failure.

[0045] 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 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 can generate a character model based on a character illustration drawn by a participant. This makes it possible to support a variety of input formats.

[0046] The prompt analysis unit can analyze prompts for hybrid games that combine game elements from different genres and 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 allows for the creation of new game genres.

[0047] The generation AI can create characters with more depth by setting detailed backstories and personalities. 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.

[0048] Generative 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.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] 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.

[0051] (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.

[0052] A game planning contest system according to an embodiment includes a prompt analysis unit, a game generation unit, and an evaluation unit. The prompt analysis unit analyzes prompts input by participants. For example, the prompt analysis unit analyzes prompts input 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 a game using a multimodal generation AI. For example, the generation AI generates specific game designs, storylines, characters, and gameplay elements based on the prompts input by participants. 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 to evaluate the game's balance, difficulty, user reaction, 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 reaction, etc. As a result, the game planning contest system according to the embodiment realizes a contest that can be participated in by general participants who do not have game development skills, enables game development through simple prompts using AI, and can achieve a higher level of evaluation of plans than general planning contests.

[0053] 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 in the prompt analysis unit analyzes the participant's past prompt input history and learns individual preferences and tendencies. For example, for a participant who has input many fantasy prompts in the past, the prompt analysis unit can 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 generates characters and story elements that the participant likes based on the participant's past selection history. This makes it possible to generate game elements that match the participant's preferences.

[0054] The prompt analysis unit can refer to a related existing game database and propose an optimal game design based on past examples of success and failure. In the prompt analysis unit, for example, the generation AI refers to an existing game database and analyzes prompts based on past examples of success and failure. For example, it proposes a game design that incorporates elements of successful RPG games. The prompt analysis unit can also propose an optimal game design based on past examples of success and failure. For example, the generation AI proposes a game design that improves the user experience and adjusts the balance based on the past game database. This makes it possible to propose an optimal game design based on past examples of success and failure.

[0055] 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 prompts. For example, if the positive emotions are strong, the prompt analysis unit generates 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 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.

[0056] 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 support a variety of input formats.

[0057] 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 games 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 allows a new game genre to be created.

[0058] Using the emotion estimation function, it is possible to provide real-time feedback on the emotions of participants when they enter a prompt and suggest the most appropriate prompt. For example, the prompt analysis unit uses the generation AI to analyze the emotions of participants in real time and suggest prompts that elicit positive emotions. For example, an encouraging message may 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 emotions of participants when they enter a prompt and suggest the most appropriate prompt. For example, the generation AI adjusts the content of the prompt based on the emotions of the participant. This makes it possible to suggest the most appropriate prompt.

[0059] The generation AI can create characters with more depth by setting the character's backstory and personality in detail. The generation AI can, for example, set the character's past events and relationships. The generation AI can also create characters with more depth by setting the character's personality in detail. For example, it can set the character's personality traits and behavior patterns. This allows for the creation of characters with more depth.

[0060] 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 many 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.

[0061] Generative AI can generate multicultural games that incorporate different cultures and histories in game design and story generation. For example, generative AI 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, generative AI can generate a story based on European history. This makes it possible to generate multicultural games.

[0062] 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.

[0063] 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 uses 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.

[0064] The generation AI can analyze the play data of the playable demo and evaluate the replayability and user engagement of the game. The generation AI can, for example, analyze the play data of the playable demo and evaluate the replayability of the game. 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 the playable demo and evaluate user engagement. For example, it can evaluate how involved the user is in the game. This makes it possible to evaluate replayability and user engagement.

[0065] Generative AI can add the social impact and educational value of a game to its evaluation criteria, enabling 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 the game's educational value. For example, it can evaluate the learning effect and knowledge provided. This allows it to evaluate social impact and educational value.

[0066] The generation AI evaluates the playable demo for user groups of different age groups and genders, allowing evaluations from diverse perspectives. The generation AI, for example, evaluates the playable demo for user groups of different age groups. For example, it collects 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 collects opinions from both males and females. This allows evaluations from diverse perspectives.

[0067] 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 inputs 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.

[0068] 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, if positive emotions are strong, the evaluation criteria can be relaxed. 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, if negative emotions are strong, the evaluation criteria can be tightened. This allows the evaluation criteria to be dynamically adjusted.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The prompt analysis unit analyzes prompts entered by participants. For example, the prompt analysis unit analyzes prompts entered by participants such as "an adventure RPG in a fantasy world" 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 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 a game using a 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 response, etc. For example, the evaluation unit analyzes play data of a playable demo generated by the generation AI and evaluates the game's balance, difficulty, user response, etc. As a result, the game planning contest system according to the embodiment realizes a contest that even general participants without game development skills can participate in, enables game development through simple prompts using AI, and can achieve a higher level of project evaluation accuracy than general planning contests.

[0071] 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 can 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 can generate characters and story elements that the participant likes based on the participant's past selection history. This makes it possible to generate game elements that match the participant's preferences.

[0072] The prompt analysis unit can refer to a related existing game database 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 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 the past game database. This makes it possible to propose optimal game designs based on past examples of success and failure.

[0073] 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 can analyze the participants' facial expressions and voices to estimate the emotions they had when entering the prompts. 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 and story development based on the participants' emotions. This makes it possible to generate game elements that elicit positive emotions from participants.

[0074] 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 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 can generate a character model based on a character illustration drawn by a participant. This makes it possible to support a variety of input formats.

[0075] The prompt analysis unit can analyze prompts for hybrid games that combine game elements from different genres and 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 allows for the creation of new game genres.

[0076] Using the emotion estimation function, it is possible to provide real-time feedback on the participant's emotions when entering a prompt and suggest the most appropriate prompt. For example, the generation AI can analyze the participant's emotions in real time and suggest 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 emotions when entering a prompt and 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.

[0077] The generation AI can create characters with more depth by setting detailed backstories and personalities. 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.

[0078] The emotion estimation function can be used to analyze participants' emotional reactions to generated game elements, and strengthen elements that elicit positive reactions. For example, the emotion estimation function can be used to analyze participants' emotional reactions to generated game elements. For example, elements that elicit many 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.

[0079] Generative 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.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] 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.

[0082] 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 regarding 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification 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 model 58 includes AIs other than the generative AI. The 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 various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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, RAM 30, and 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.

[0088] 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.

[0089] 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 audio according to instructions from the processor 46.

[0090] 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).

[0091] The communication I / F 44 is connected to a 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 state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 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.

[0093] 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.

[0094] 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.

[0095] In the smart glasses 214, the 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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 a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] 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 regarding 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.

[0098] 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. 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 instruction indicated by the prompt and outputs the inference result in a data format 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 model 58 includes AIs other than the generative AI. The 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 various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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 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. Furthermore, 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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, RAM 30, and 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.

[0103] 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.

[0104] 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 audio according to instructions from the processor 46.

[0105] 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).

[0106] The communication I / F 44 is connected to a 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 state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0111] 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 a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] 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 regarding 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.

[0113] 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. 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 instruction indicated by the prompt and outputs the inference result in a data format 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 model 58 includes AIs other than the generative AI. The 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 various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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, RAM 30, and 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.

[0118] 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.

[0119] 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 audio according to instructions from the processor 46.

[0120] 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 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).

[0121] The communication I / F 44 is connected to a 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 state.

[0122] 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.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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 a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] 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 a user input regarding 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.

[0129] 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. 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 instruction indicated by the prompt and outputs the inference result in a data format 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 model 58 includes AIs other than the generative AI. The 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 various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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. 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.

[0131] The emotion identification model 59 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 FIG. 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.

[0132] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and 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, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] 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 you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] 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 located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] 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.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, 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.

[0140] 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.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an 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 above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described 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 description 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 replacements may be made to the above-described description and illustrations within the scope of the gist 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 description 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. 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; an evaluation unit that evaluates the game generated by the game generation unit; A system characterized by:

2. The prompt input format will be diversified to allow not only text but also voice and image input.

2. The system of claim 1.

3. 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; an evaluation unit that evaluates the game generated by the game generation unit; A system characterized by:

4. The generated AI is Analyze play data from the playable demo to evaluate the game's replayability and user engagement 2. The system of claim 1.

5. The prompt analysis unit Using an emotion estimation function, the emotion of the participant when entering the prompt is analyzed, and game elements that elicit the positive emotion are generated.

2. The system of claim 1.

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