system

Abstract

The system according to this embodiment aims to enable users to easily create games of their choice. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a feedback unit, and an improvement unit. The reception unit inputs the user's desired game genre, difficulty level, and character design. The generation unit automatically generates the main parts of the game based on the information input by the reception unit. The feedback unit allows the user to test play the game generated by the generation unit and provide feedback. The improvement unit analyzes the feedback provided by the feedback unit and reflects it in improving the game.

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] With conventional technology, creating a game required high level of skill and time, making it difficult for newcomers to enter the market.

[0005] The system according to this embodiment aims to enable users to easily create games of their choice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a feedback unit, and an improvement unit. The reception unit inputs the user's desired game genre, difficulty level, and character design. The generation unit automatically generates the main parts of the game based on the information input by the reception unit. The feedback unit allows the user to test play the game generated by the generation unit and provide feedback. The improvement unit analyzes the feedback provided by the feedback unit and reflects it in improving the game. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to easily create games of their choice. [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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 of form 1) An automatic game generation system according to an embodiment of the present invention utilizes a generation AI to automatically generate most of a game. This system allows users to input desired game genres, difficulty levels, character designs, and other information, and the generation AI automatically generates most of the game based on that information. Users can test play the generated game and provide feedback. The generation AI analyzes the feedback and incorporates it into game improvements. This system significantly lowers the barrier to game creation, potentially attracting new entrants and casual gamers. Furthermore, an increase in casual gamers will relatively increase the value of heavy gamers, further revitalizing the game market. For example, users input their desired game genres, difficulty levels, and character designs. A user interface is provided to facilitate input using drop-down menus and sliders. For example, a user might input information such as "action game," "difficulty: high," and "characters: fantasy-style." The generation AI then analyzes the input information and automatically generates most of the game. The generation AI automatically generates the game's storyline, level design, character behavior, and other information. For example, it generates an appropriate storyline and level design based on the user's desired genre and difficulty level. Users can test play the generated game and provide feedback. Users play the generated game and use an interface to provide feedback on improvements and requests. For example, users can provide feedback such as "This level is too difficult" or "The character's movements are unnatural." The generation AI analyzes the user's feedback and reflects it in improving the game. For example, it may adjust the level difficulty or modify the character's movements based on the user's feedback. In this way, the generation AI continuously improves the game and increases user satisfaction. This system significantly lowers the barrier to game creation, potentially attracting new entrants and casual users. For example, even those without programming knowledge can easily create high-quality games.In addition, the ability to provide high-quality games at low prices is expected to attract more light users. Furthermore, an increase in light users will relatively increase the value of heavy users, further stimulating the game market. For example, when playing the same game, the value of current gamers will increase, allowing more users to enjoy the game. This allows the game auto-generation system to easily create the games users want and to continuously improve them based on feedback.

[0029] An automatic game generation system according to an embodiment includes a reception unit, a generation unit, a feedback unit, and an improvement unit. The reception unit receives input of a user's desired game genre, difficulty level, and character design. Examples of the user's desired game genre, difficulty level, and character design include, but are not limited to, action, RPG, easy, difficult, fantasy, and realistic. The reception unit, for example, provides a user interface, allowing the user to input the user's desired game genre, difficulty level, and character design using a drop-down menu or slider. The generation unit automatically generates most of the game based on the information input by the reception unit using a generation AI. The generation unit automatically generates, for example, the game's storyline, level design, character movements, and the like. The generation AI generates each element of the game using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates an appropriate storyline and level design based on, for example, the user's desired genre and difficulty level. The feedback unit provides an interface that allows the user to test play the game generated by the generation unit and provide feedback. The feedback unit, for example, provides an interface that allows the user to play the generated game and provide feedback on improvements and requests. The feedback unit provides an input form and evaluation items for users to provide feedback such as, for example, "This level is too difficult" or "The character's movements are unnatural." The improvement unit analyzes the feedback provided by the feedback unit and reflects it in improving the game. For example, the improvement unit adjusts the difficulty of a level or modifies the character's movements based on the user's feedback. For example, the improvement unit analyzes the feedback using text analysis technology and improves each element of the game based on the content of the feedback. This allows the automatic game generation system according to the embodiment to easily create a game that the user desires and continuously improve it based on the feedback.

[0030] The reception unit may provide a user interface that allows the user to input the desired game genre, difficulty level, and character design using a drop-down menu or slider. Examples of user interfaces include, but are not limited to, drop-down menus and sliders. The reception unit may provide an intuitive interface that allows the user to easily input the desired game genre, difficulty level, and character design. For example, the reception unit may provide a drop-down menu or slider that allows the user to input information such as "action game," "difficulty: high," and "character: fantasy style." The reception unit may also provide a preview function that allows the user to check the information entered in real time. For example, the reception unit may provide a real-time preview of how the character design selected by the user will appear. This allows the user to easily input game settings.

[0031] The generation unit can use a generation AI to automatically generate parts of a game, including a storyline, level design, and character actions. Examples of generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The generation unit generates an appropriate storyline and level design based on, for example, the genre and difficulty level desired by the user. For example, in the case of an action game, the generation unit generates a storyline and level design that emphasizes action elements. In addition, in the case of a puzzle game, the generation unit can also generate a storyline and level design that emphasizes puzzle elements. Furthermore, the generation unit generates character actions based on a character design selected by the user. For example, in the case of a fantasy-style character, the generation unit generates magic-using actions and fantasy-style actions. In this way, the generation AI can automatically generate a large portion of the game.

[0032] The feedback unit may provide an interface that allows a user to test play the generated game and provide feedback on improvements or requests. The feedback unit may, for example, provide an input form or evaluation items that allow a user to play the generated game and provide feedback on improvements or requests. For example, an interface may be provided that allows a user to provide feedback such as "this level is too difficult" or "the character's movements are unnatural." The feedback unit may also provide an intuitive interface that makes it easy for a user to provide feedback. For example, a function may be provided that previews the effect of the feedback in real time when the user enters feedback. This allows a user to test play the generated game and provide feedback.

[0033] The improvement unit can analyze feedback provided by users and reflect it in improving the game. For example, the improvement unit adjusts the difficulty of a level or modifies the character's movements based on the user's feedback. For example, the improvement unit analyzes the feedback using text analysis technology and improves each element of the game based on the content of the feedback. For example, if a user provides feedback that "this level is too difficult," the improvement unit adjusts the difficulty of the level. Also, if a user provides feedback that "the character's movements are unnatural," the improvement unit modifies the character's movements. In this way, the game can be improved based on user feedback.

[0034] The reception unit can analyze the user's past game play history and automatically suggest an appropriate game genre or difficulty level. The reception unit can, for example, suggest an optimal game genre or difficulty level based on the genres and difficulty levels of games the user has played in the past. The reception unit can also analyze the user's past play time and frequency to suggest an appropriate game genre or difficulty level. Furthermore, the reception unit can suggest an optimal game genre or difficulty level based on the features of games that the user has given high ratings to in the past. This allows optimal suggestions to be made based on the user's past play history.

[0035] The reception unit can instantly display a preview based on the user's input, allowing the user to visually confirm the elements selected by the user. For example, when the user selects a game genre, the reception unit can display a sample game screen based on the genre in real time. Also, when the user selects a character design, the reception unit can preview in real time how the character with the selected design will appear in the game. Furthermore, when the user selects a difficulty level, the reception unit can display in real time a portion of gameplay based on the difficulty level. This allows the user to confirm the elements selected in real time.

[0036] The reception unit can suggest game elements specific to a region, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can suggest game genres and storylines related to that region. Furthermore, the reception unit can also suggest game genres and character designs that are popular in that region, based on the user's geographical location information. Furthermore, if the user is traveling, game elements incorporating the tourist attractions and culture of that region can be suggested. This allows optimal game elements to be suggested based on the user's geographical location information.

[0037] The reception unit can analyze the user's social media activity and suggest related game genres or character designs. The reception unit can make optimal suggestions based on, for example, game genres and character designs that the user frequently mentions on social media. The reception unit can also analyze topics and themes of interest from the user's social media activity and suggest game elements based on those topics and themes. Furthermore, the reception unit can suggest related game genres and character designs based on information about influencers and communities that the user follows on social media. This allows optimal suggestions to be made based on the user's social media activity.

[0038] The generation unit can generate more personalized game elements by referring to the user's past feedback during generation. For example, the generation unit generates a preferred storyline or character design based on feedback provided by the user in the past. The generation unit can also generate a game that incorporates elements that were particularly highly rated based on the user's past feedback. Furthermore, the generation unit generates a game that reflects improvements previously pointed out by the user. This makes it possible to generate personalized game elements based on the user's past feedback.

[0039] The generation unit can use different generation algorithms during generation depending on the genre and difficulty level selected by the user. For example, if the user selects an action game, the generation unit applies a generation algorithm that emphasizes action elements. Also, if the user selects a puzzle game, the generation unit can apply a generation algorithm that emphasizes puzzle elements. Furthermore, if the user selects a high difficulty level, the generation unit applies a generation algorithm that increases the difficulty level. This makes it possible to apply the optimal generation algorithm depending on the user's selection.

[0040] The generation unit can generate a storyline or character specific to a region by taking into account the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate a storyline that incorporates the history and culture of that region. The generation unit can also generate a character design that is popular in that region based on the user's geographical location information. Furthermore, if the user is traveling, the generation unit can generate a character that incorporates the tourist attractions and culture of that region. This allows the generation of optimal storylines and characters based on the user's geographical location information.

[0041] The generation unit can analyze the user's social media activity during generation and generate relevant game elements. For example, it can generate relevant game elements based on topics and themes that the user frequently mentions on social media. It can also generate character designs that the user is interested in based on their social media activity. Furthermore, it can generate relevant storylines and characters based on information about influencers and communities that the user follows on social media. This allows for the generation of optimal game elements based on the user's social media activity.

[0042] The feedback unit can suggest appropriate feedback items by referring to the user's past feedback history when feedback is provided. For example, the feedback unit can suggest the most suitable feedback items based on the user's past feedback. It can also prioritize displaying items that received particularly high ratings from the user's past feedback history. Furthermore, it can suggest feedback items that reflect the areas for improvement the user has pointed out in the past. In this way, it can suggest the most suitable feedback items based on the user's past feedback history.

[0043] The feedback section can instantly preview the impact of feedback, allowing users to visually confirm areas for improvement. For example, when a user submits feedback, the feedback section can display a real-time preview of how that feedback will affect the game. It can also display in real-time how improvements will be reflected in the game when a user submits improvements. Furthermore, when a user submits a request, they can see in real-time how that request will be incorporated into the game. This allows users to see the impact of their feedback in real time.

[0044] When providing feedback, the feedback unit can suggest improvements specific to a region by taking into account the user's geographical location information. For example, if the user is in a specific region, the feedback unit can suggest improvements related to that region. The feedback unit can also suggest improvements that are popular in that region based on the user's geographical location information. Furthermore, if the user is traveling, the feedback unit can suggest improvements that incorporate the tourist attractions and culture of that region. This makes it possible to suggest optimal improvements based on the user's geographical location information.

[0045] When providing feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback items. For example, the feedback unit can suggest optimal feedback items based on game elements and areas for improvement that the user frequently mentions on social media. The feedback unit can also analyze topics and themes of interest from the user's social media activity and suggest feedback items based on that. Furthermore, the feedback unit can suggest relevant feedback items based on information about influencers and communities that the user follows on social media. This makes it possible to suggest optimal feedback items based on the user's social media activity.

[0046] When making improvements, the improvement unit can select an appropriate improvement method by referring to the user's past feedback history. The improvement unit selects the optimal improvement method, for example, based on feedback provided by the user in the past. The improvement unit can also preferentially select improvement methods that have received particularly high ratings from the user's past feedback history. Furthermore, the improvement unit selects an improvement method that reflects improvements that the user has previously pointed out. This makes it possible to select the optimal improvement method based on the user's past feedback history.

[0047] When making an improvement, the improvement unit can use different improvement algorithms depending on the genre and difficulty level selected by the user. For example, if the user selects an action game, the improvement unit applies an improvement algorithm that emphasizes action elements. Also, if the user selects a puzzle game, the improvement unit can apply an improvement algorithm that emphasizes puzzle elements. Furthermore, if the user selects a high difficulty level, the improvement unit applies an improvement algorithm for increasing the difficulty level. This makes it possible to apply the optimal improvement algorithm depending on the user's selection.

[0048] When making improvements, the improvement unit can take into account the user's geographical location information and suggest improvement methods specific to the region. For example, if the user is in a specific region, the improvement unit can suggest improvement methods that take into account the culture and customs of that region. The improvement unit can also suggest improvement methods that are popular in that region based on the user's geographical location information. Furthermore, if the user is traveling, the improvement unit can suggest improvement methods that incorporate the tourist attractions and culture of that region. This makes it possible to suggest the optimal improvement method based on the user's geographical location information.

[0049] When making improvements, the improvement department can analyze the user's social media activity and suggest related improvement methods. For example, the improvement department can suggest the optimal improvement method based on the improvement points that the user frequently mentions on social media. The improvement department can also analyze the topics and themes that the user is interested in from their social media activity and suggest improvement methods based on that. Furthermore, related improvement methods can be suggested based on information about influencers and communities that the user follows on social media. This makes it possible to suggest the optimal improvement method based on the user's social media activity.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can analyze the user's voice input and input the game genre, difficulty level, and character design using voice recognition technology. For example, if the user vocally commands "action game," "difficulty: high," and "character: fantasy style," the reception unit analyzes this and inputs the appropriate information. It can also provide real-time feedback on the voice input to help the user input more accurately. Furthermore, to improve the accuracy of voice input, it can refer to the user's past voice input history and use an individualized voice recognition model. This allows the user to easily input game settings without using their hands.

[0052] The generator can automatically generate different music and sound effects depending on the genre and difficulty level selected by the user. For example, tense music and sound effects can be generated for an action game, while relaxing music and sound effects can be generated for a puzzle game. The generator can also generate character voices and sound effects based on a character design selected by the user. Furthermore, the generated music and sound effects can be adjusted based on user feedback. This allows the sound elements of the game to be optimized according to the user's selection.

[0053] The feedback unit can share useful feedback with other users based on the feedback provided by the user. For example, if there is a lot of feedback that a particular level is too difficult, the feedback unit can notify other users of this information and provide advice on how to avoid similar problems. The feedback unit can also provide a template for other users to report similar problems based on the feedback provided by the user. Furthermore, the feedback unit can display statistics on the feedback provided by users, allowing users to visually see which feedback has received the most attention. This promotes information sharing among users and provides a better gaming experience.

[0054] The generation unit can automatically generate different visual styles depending on the genre and difficulty level selected by the user. For example, for action games, it generates a dynamic and vibrant visual style, while for puzzle games, it generates a simple and calm visual style. It can also generate character appearances and animations based on the character design selected by the user. Furthermore, the generated visual style can be adjusted based on user feedback. This optimizes the game's visual elements according to the user's choices.

[0055] The feedback unit can share useful feedback with other users based on the feedback provided by the user. For example, if there is a lot of feedback that a particular level is too difficult, the feedback unit can notify other users of this information and provide advice on how to avoid similar problems. The feedback unit can also provide a template for other users to report similar problems based on the feedback provided by the user. Furthermore, the feedback unit can display statistics on the feedback provided by users, allowing users to visually see which feedback has received the most attention. This promotes information sharing among users and provides a better gaming experience.

[0056] The generation unit can automatically generate different visual styles depending on the genre and difficulty level selected by the user. For example, for action games, it generates a dynamic and vibrant visual style, while for puzzle games, it generates a simple and calm visual style. It can also generate character appearances and animations based on the character design selected by the user. Furthermore, the generated visual style can be adjusted based on user feedback. This optimizes the game's visual elements according to the user's choices.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit inputs the user's desired game genre, difficulty level, and character design. A user interface is provided that allows the user to input the desired game genre, difficulty level, and character design using drop-down menus and sliders. Step 2: The generation unit automatically generates most of the game based on the information entered by the reception unit. Using the generation AI, it automatically generates the game's storyline, level design, character behavior, etc. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to generate each element of the game. Step 3: The feedback unit provides an interface that allows users to test play the game generated by the generation unit and provide feedback. An interface is provided that allows users to play the generated game and provide feedback on improvements and requests. For example, an input form and evaluation items are provided that allow users to provide feedback such as "This level is too difficult" or "The character's movements are unnatural." Step 4: The Improvement Department analyzes the feedback provided by the Feedback Department and reflects it in improving the game. Based on user feedback, they may adjust the difficulty of the levels or modify the character's movements. They analyze the feedback using text analysis technology and improve each element of the game based on the content of the feedback.

[0059] (Example 2) An automatic game generation system according to an embodiment of the present invention utilizes a generation AI to automatically generate most of a game. This system allows users to input desired game genres, difficulty levels, character designs, and other information, and the generation AI automatically generates most of the game based on that information. Users can test play the generated game and provide feedback. The generation AI analyzes the feedback and incorporates it into game improvements. This system significantly lowers the barrier to game creation, potentially attracting new entrants and casual gamers. Furthermore, an increase in casual gamers will relatively increase the value of heavy gamers, further revitalizing the game market. For example, users input their desired game genres, difficulty levels, and character designs. A user interface is provided to facilitate input using drop-down menus and sliders. For example, a user might input information such as "action game," "difficulty: high," and "characters: fantasy-style." The generation AI then analyzes the input information and automatically generates most of the game. The generation AI automatically generates the game's storyline, level design, character behavior, and other information. For example, it generates an appropriate storyline and level design based on the user's desired genre and difficulty level. Users can test play the generated game and provide feedback. Users play the generated game and use an interface to provide feedback on improvements and requests. For example, users can provide feedback such as "This level is too difficult" or "The character's movements are unnatural." The generation AI analyzes the user's feedback and reflects it in improving the game. For example, it may adjust the level difficulty or modify the character's movements based on the user's feedback. In this way, the generation AI continuously improves the game and increases user satisfaction. This system significantly lowers the barrier to game creation, potentially attracting new entrants and casual users. For example, even those without programming knowledge can easily create high-quality games.In addition, the ability to provide high-quality games at low prices is expected to attract more light users. Furthermore, an increase in light users will relatively increase the value of heavy users, further stimulating the game market. For example, when playing the same game, the value of current gamers will increase, allowing more users to enjoy the game. This allows the game auto-generation system to easily create the games users want and to continuously improve them based on feedback.

[0060] An automatic game generation system according to an embodiment includes a reception unit, a generation unit, a feedback unit, and an improvement unit. The reception unit receives input of a user's desired game genre, difficulty level, and character design. Examples of the user's desired game genre, difficulty level, and character design include, but are not limited to, action, RPG, easy, difficult, fantasy, and realistic. The reception unit, for example, provides a user interface, allowing the user to input the user's desired game genre, difficulty level, and character design using a drop-down menu or slider. The generation unit automatically generates most of the game based on the information input by the reception unit using a generation AI. The generation unit automatically generates, for example, the game's storyline, level design, character movements, and the like. The generation AI generates each element of the game using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates an appropriate storyline and level design based on, for example, the user's desired genre and difficulty level. The feedback unit provides an interface that allows the user to test play the game generated by the generation unit and provide feedback. The feedback unit, for example, provides an interface that allows the user to play the generated game and provide feedback on improvements and requests. The feedback unit provides an input form and evaluation items for users to provide feedback such as, for example, "This level is too difficult" or "The character's movements are unnatural." The improvement unit analyzes the feedback provided by the feedback unit and reflects it in improving the game. For example, the improvement unit adjusts the difficulty of a level or modifies the character's movements based on the user's feedback. For example, the improvement unit analyzes the feedback using text analysis technology and improves each element of the game based on the content of the feedback. This allows the automatic game generation system according to the embodiment to easily create a game that the user desires and continuously improve it based on the feedback.

[0061] The reception unit may provide a user interface that allows the user to input the desired game genre, difficulty level, and character design using a drop-down menu or slider. Examples of user interfaces include, but are not limited to, drop-down menus and sliders. The reception unit may provide an intuitive interface that allows the user to easily input the desired game genre, difficulty level, and character design. For example, the reception unit may provide a drop-down menu or slider that allows the user to input information such as "action game," "difficulty: high," and "character: fantasy style." The reception unit may also provide a preview function that allows the user to check the information entered in real time. For example, the reception unit may provide a real-time preview of how the character design selected by the user will appear. This allows the user to easily input game settings.

[0062] The generation unit can use a generation AI to automatically generate parts of a game, including a storyline, level design, and character actions. Examples of generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. The generation unit generates an appropriate storyline and level design based on, for example, the genre and difficulty level desired by the user. For example, in the case of an action game, the generation unit generates a storyline and level design that emphasizes action elements. In addition, in the case of a puzzle game, the generation unit can also generate a storyline and level design that emphasizes puzzle elements. Furthermore, the generation unit generates character actions based on a character design selected by the user. For example, in the case of a fantasy-style character, the generation unit generates magic-using actions and fantasy-style actions. In this way, the generation AI can automatically generate a large portion of the game.

[0063] The feedback unit may provide an interface that allows a user to test play the generated game and provide feedback on improvements or requests. The feedback unit may, for example, provide an input form or evaluation items that allow a user to play the generated game and provide feedback on improvements or requests. For example, an interface may be provided that allows a user to provide feedback such as "this level is too difficult" or "the character's movements are unnatural." The feedback unit may also provide an intuitive interface that makes it easy for a user to provide feedback. For example, a function may be provided that previews the effect of the feedback in real time when the user enters feedback. This allows a user to test play the generated game and provide feedback.

[0064] The improvement unit can analyze feedback provided by users and reflect it in improving the game. For example, the improvement unit adjusts the difficulty of a level or modifies the character's movements based on the user's feedback. For example, the improvement unit analyzes the feedback using text analysis technology and improves each element of the game based on the content of the feedback. For example, if a user provides feedback that "this level is too difficult," the improvement unit adjusts the difficulty of the level. Also, if a user provides feedback that "the character's movements are unnatural," the improvement unit modifies the character's movements. In this way, the game can be improved based on user feedback.

[0065] The reception desk can estimate the user's emotions and change the design of the input interface in real time based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, it can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of game genre, difficulty level, and character design. This allows the input interface to be optimized according to the user's emotions.

[0066] The reception unit can analyze the user's past game play history and automatically suggest an appropriate game genre or difficulty level. The reception unit can, for example, suggest an optimal game genre or difficulty level based on the genres and difficulty levels of games the user has played in the past. The reception unit can also analyze the user's past play time and frequency to suggest an appropriate game genre or difficulty level. Furthermore, the reception unit can suggest an optimal game genre or difficulty level based on the features of games that the user has given high ratings to in the past. This allows optimal suggestions to be made based on the user's past play history.

[0067] The reception unit can instantly display a preview based on the user's input, allowing the user to visually confirm the elements selected by the user. For example, when the user selects a game genre, the reception unit can display a sample game screen based on the genre in real time. Also, when the user selects a character design, the reception unit can preview in real time how the character with the selected design will appear in the game. Furthermore, when the user selects a difficulty level, the reception unit can display in real time a portion of gameplay based on the difficulty level. This allows the user to confirm the elements selected in real time.

[0068] The reception desk can estimate the user's emotions and change the priority of input fields in real time based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize displaying the most important input fields and postpone other fields. If the user is relaxed, it can display all input fields equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, it will prioritize displaying the easiest fields to complete, enabling quick completion. In this way, the priority of input fields can be optimized according to the user's emotions.

[0069] The reception unit can suggest game elements specific to a region, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can suggest game genres and storylines related to that region. Furthermore, the reception unit can also suggest game genres and character designs that are popular in that region, based on the user's geographical location information. Furthermore, if the user is traveling, game elements incorporating the tourist attractions and culture of that region can be suggested. This allows optimal game elements to be suggested based on the user's geographical location information.

[0070] The reception unit can analyze the user's social media activity and suggest related game genres or character designs. The reception unit can make optimal suggestions based on, for example, game genres and character designs that the user frequently mentions on social media. The reception unit can also analyze topics and themes of interest from the user's social media activity and suggest game elements based on those topics and themes. Furthermore, the reception unit can suggest related game genres and character designs based on information about influencers and communities that the user follows on social media. This allows optimal suggestions to be made based on the user's social media activity.

[0071] The generation unit can use the generation AI to estimate the user's emotions and adjust the tone and atmosphere of the generated game based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a game with a calm tone and atmosphere. Alternatively, if the user is excited, the generation unit can generate a game with an emphasis on action elements. Furthermore, if the user is stressed, the generation unit can generate a game that incorporates music and visuals that have a relaxing effect. This allows the tone and atmosphere of the game to be optimized according to the user's emotions.

[0072] The generation unit can generate more personalized game elements by referring to the user's past feedback during generation. For example, the generation unit generates a preferred storyline or character design based on feedback provided by the user in the past. The generation unit can also generate a game that incorporates elements that were particularly highly rated based on the user's past feedback. Furthermore, the generation unit generates a game that reflects improvements previously pointed out by the user. This makes it possible to generate personalized game elements based on the user's past feedback.

[0073] The generation unit can use different generation algorithms during generation depending on the genre and difficulty level selected by the user. For example, if the user selects an action game, the generation unit applies a generation algorithm that emphasizes action elements. Also, if the user selects a puzzle game, the generation unit can apply a generation algorithm that emphasizes puzzle elements. Furthermore, if the user selects a high difficulty level, the generation unit applies a generation algorithm that increases the difficulty level. This makes it possible to apply the optimal generation algorithm depending on the user's selection.

[0074] The generation unit can estimate the user's emotions and change the difficulty level of the game to be generated based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a game with a low difficulty level. Alternatively, if the user is excited, the generation unit can generate a game with a high difficulty level. Furthermore, if the user is stressed, the generation unit can set the difficulty level to a medium level to generate a balanced game. This makes it possible to optimize the difficulty level of the game according to the user's emotions.

[0075] The generation unit can generate a storyline or character specific to a region by taking into account the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate a storyline that incorporates the history and culture of that region. The generation unit can also generate a character design that is popular in that region based on the user's geographical location information. Furthermore, if the user is traveling, the generation unit can generate a character that incorporates the tourist attractions and culture of that region. This allows the generation of optimal storylines and characters based on the user's geographical location information.

[0076] The generation unit can analyze the user's social media activity during generation and generate relevant game elements. For example, it can generate relevant game elements based on topics and themes that the user frequently mentions on social media. It can also generate character designs that the user is interested in based on their social media activity. Furthermore, it can generate relevant storylines and characters based on information about influencers and communities that the user follows on social media. This allows for the generation of optimal game elements based on the user's social media activity.

[0077] The feedback unit can estimate the user's emotions and change the feedback input interface in real time based on the estimated user emotions. For example, if the user is feeling stressed, the feedback unit can provide a simple and intuitive feedback interface to minimize input steps. Alternatively, if the user is relaxed, the feedback unit can provide detailed feedback options and suggest customizable input methods. Furthermore, if the user is in a hurry, the feedback unit can prioritize voice input to provide quick feedback. This makes it possible to optimize the feedback input interface according to the user's emotions.

[0078] The feedback unit can suggest appropriate feedback items by referring to the user's past feedback history when feedback is provided. For example, the feedback unit can suggest the most suitable feedback items based on the user's past feedback. It can also prioritize displaying items that received particularly high ratings from the user's past feedback history. Furthermore, it can suggest feedback items that reflect the areas for improvement the user has pointed out in the past. In this way, it can suggest the most suitable feedback items based on the user's past feedback history.

[0079] The feedback section can instantly preview the impact of feedback, allowing users to visually confirm areas for improvement. For example, when a user submits feedback, the feedback section can display a real-time preview of how that feedback will affect the game. It can also display in real-time how improvements will be reflected in the game when a user submits improvements. Furthermore, when a user submits a request, they can see in real-time how that request will be incorporated into the game. This allows users to see the impact of their feedback in real time.

[0080] The feedback unit can estimate the user's emotions and change the priority of feedback in real time based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can prioritize displaying the most important feedback items and postpone other items. Also, if the user is relaxed, the feedback unit can display all feedback items equally, allowing the user to freely select. Furthermore, if the user is in a hurry, the feedback item that can be entered most easily can be prioritized, allowing the user to complete the feedback quickly. In this way, the priority of feedback can be optimized according to the user's emotions.

[0081] When providing feedback, the feedback unit can suggest improvements specific to a region by taking into account the user's geographical location information. For example, if the user is in a specific region, the feedback unit can suggest improvements related to that region. The feedback unit can also suggest improvements that are popular in that region based on the user's geographical location information. Furthermore, if the user is traveling, the feedback unit can suggest improvements that incorporate the tourist attractions and culture of that region. This makes it possible to suggest optimal improvements based on the user's geographical location information.

[0082] When providing feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback items. For example, the feedback unit can suggest optimal feedback items based on game elements and areas for improvement that the user frequently mentions on social media. The feedback unit can also analyze topics and themes of interest from the user's social media activity and suggest feedback items based on that. Furthermore, the feedback unit can suggest relevant feedback items based on information about influencers and communities that the user follows on social media. This makes it possible to suggest optimal feedback items based on the user's social media activity.

[0083] The improvement unit can estimate the user's emotions and change the priority of improvements in real time based on the estimated user emotions. For example, if the user is feeling stressed, the improvement unit can prioritize the most important improvement points and postpone other items. Alternatively, if the user is relaxed, the improvement unit can equally handle all improvement points, allowing the user to freely select. Furthermore, if the user is in a hurry, the improvement points that can be most easily improved can be prioritized, allowing the improvement to be completed quickly. In this way, the priority of improvements can be optimized according to the user's emotions.

[0084] When making improvements, the improvement unit can select an appropriate improvement method by referring to the user's past feedback history. The improvement unit selects the optimal improvement method, for example, based on feedback provided by the user in the past. The improvement unit can also preferentially select improvement methods that have received particularly high ratings from the user's past feedback history. Furthermore, the improvement unit selects an improvement method that reflects improvements that the user has previously pointed out. This makes it possible to select the optimal improvement method based on the user's past feedback history.

[0085] When making an improvement, the improvement unit can use different improvement algorithms depending on the genre and difficulty level selected by the user. For example, if the user selects an action game, the improvement unit applies an improvement algorithm that emphasizes action elements. Also, if the user selects a puzzle game, the improvement unit can apply an improvement algorithm that emphasizes puzzle elements. Furthermore, if the user selects a high difficulty level, the improvement unit applies an improvement algorithm for increasing the difficulty level. This makes it possible to apply the optimal improvement algorithm depending on the user's selection.

[0086] The improvement unit can estimate the user's emotions and change the improvement method based on the estimated user's emotions. For example, if the user is relaxed, the improvement unit applies a gentle improvement method. Also, if the user is excited, the improvement unit can apply an improvement method that emphasizes action elements. Furthermore, if the user is feeling stressed, the improvement unit applies an improvement method that has a relaxing effect. In this way, the improvement method can be optimized according to the user's emotions.

[0087] When making improvements, the improvement unit can take into account the user's geographical location information and suggest improvement methods specific to the region. For example, if the user is in a specific region, the improvement unit can suggest improvement methods that take into account the culture and customs of that region. The improvement unit can also suggest improvement methods that are popular in that region based on the user's geographical location information. Furthermore, if the user is traveling, the improvement unit can suggest improvement methods that incorporate the tourist attractions and culture of that region. This makes it possible to suggest the optimal improvement method based on the user's geographical location information.

[0088] When making improvements, the improvement department can analyze the user's social media activity and suggest related improvement methods. For example, the improvement department can suggest the optimal improvement method based on the improvement points that the user frequently mentions on social media. The improvement department can also analyze the topics and themes that the user is interested in from their social media activity and suggest improvement methods based on that. Furthermore, related improvement methods can be suggested based on information about influencers and communities that the user follows on social media. This makes it possible to suggest the optimal improvement method based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, generation unit, feedback unit, and improvement unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit uses a user interface of the smart device 14 to input the user's desired game genre, difficulty level, and character design. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates most of the game using a generation AI. The feedback unit uses the interface of the smart device 14 to test play the generated game and provide feedback. The improvement unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the feedback and reflects it in improving the game. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, feedback unit, and improvement unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit uses a user interface of the smart glasses 214 to input the user's desired game genre, difficulty level, and character design. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates most of the game using a generation AI. The feedback unit uses the interface of the smart glasses 214 to test play the generated game and provide feedback. The improvement unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the feedback and reflects it in improving the game. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, feedback unit, and improvement unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit uses the user interface of the headset terminal 314 to input the desired game genre, difficulty level, and character design. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically generates most of the game using a generation AI. The feedback unit uses the interface of the headset terminal 314 to allow the user to test play the generated game and provide feedback. The improvement unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the feedback and reflects it in improving the game. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, feedback unit, and improvement unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit uses the user interface of the robot 414 to input the desired game genre, difficulty level, and character design. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically generates most of the game using a generation AI. The feedback unit uses the interface of the robot 414 to allow the user to test play the generated game and provide feedback. The improvement unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the feedback and reflects it in improving the game.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The reception unit can analyze the user's voice input and input the game genre, difficulty level, and character design using voice recognition technology. For example, if the user vocally commands "action game," "difficulty: high," and "character: fantasy style," the reception unit analyzes this and inputs the appropriate information. It can also provide real-time feedback on the voice input to help the user input more accurately. Furthermore, to improve the accuracy of voice input, it can refer to the user's past voice input history and use an individualized voice recognition model. This allows the user to easily input game settings without using their hands.

[0091] The generator can automatically generate different music and sound effects depending on the genre and difficulty level selected by the user. For example, tense music and sound effects can be generated for an action game, while relaxing music and sound effects can be generated for a puzzle game. The generator can also generate character voices and sound effects based on a character design selected by the user. Furthermore, the generated music and sound effects can be adjusted based on user feedback. This allows the sound elements of the game to be optimized according to the user's selection.

[0092] The feedback unit can share useful feedback with other users based on the feedback provided by the user. For example, if there is a lot of feedback that a particular level is too difficult, the feedback unit can notify other users of this information and provide advice on how to avoid similar problems. The feedback unit can also provide a template for other users to report similar problems based on the feedback provided by the user. Furthermore, the feedback unit can display statistics on the feedback provided by users, allowing users to visually see which feedback has received the most attention. This promotes information sharing among users and provides a better gaming experience.

[0093] When analyzing user feedback, the improvement department can use natural language processing technology to analyze the sentiment of the feedback and classify it into positive and negative feedback. For example, if a user gives feedback such as "This level is fun," it can be classified as positive feedback, and conversely, if the user gives feedback such as "This level is too difficult," it can be classified as negative feedback. Furthermore, it can determine the priority of improvements based on the sentiment of the feedback. Furthermore, it can identify game elements that are recommended to other users based on positive feedback. This allows for effective improvements based on user sentiment.

[0094] The reception unit acquires the user's biometric information and can estimate the user's emotional state based on data such as heart rate and skin galvanic response. For example, if the user shows a high heart rate, it can estimate that the user is nervous and provide a simple and intuitive interface. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input and allow the user to quickly input the game genre, difficulty level, and character design. This makes it possible to optimize the input interface based on the user's biometric information.

[0095] The generation unit can automatically generate different visual styles depending on the genre and difficulty level selected by the user. For example, for action games, it generates a dynamic and vibrant visual style, while for puzzle games, it generates a simple and calm visual style. It can also generate character appearances and animations based on the character design selected by the user. Furthermore, the generated visual style can be adjusted based on user feedback. This optimizes the game's visual elements according to the user's choices.

[0096] The feedback unit can share useful feedback with other users based on the feedback provided by the user. For example, if there is a lot of feedback that a particular level is too difficult, the feedback unit can notify other users of this information and provide advice on how to avoid similar problems. The feedback unit can also provide a template for other users to report similar problems based on the feedback provided by the user. Furthermore, the feedback unit can display statistics on the feedback provided by users, allowing users to visually see which feedback has received the most attention. This promotes information sharing among users and provides a better gaming experience.

[0097] When analyzing user feedback, the improvement department can use natural language processing technology to analyze the sentiment of the feedback and classify it into positive and negative feedback. For example, if a user gives feedback such as "This level is fun," it can be classified as positive feedback, and conversely, if the user gives feedback such as "This level is too difficult," it can be classified as negative feedback. Furthermore, it can determine the priority of improvements based on the sentiment of the feedback. Furthermore, it can identify game elements that are recommended to other users based on positive feedback. This allows for effective improvements based on user sentiment.

[0098] The reception unit acquires the user's biometric information and can estimate the user's emotional state based on data such as heart rate and skin galvanic response. For example, if the user shows a high heart rate, it can estimate that the user is nervous and provide a simple and intuitive interface. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input and allow the user to quickly input the game genre, difficulty level, and character design. This makes it possible to optimize the input interface based on the user's biometric information.

[0099] The generation unit can automatically generate different visual styles depending on the genre and difficulty level selected by the user. For example, for action games, it generates a dynamic and vibrant visual style, while for puzzle games, it generates a simple and calm visual style. It can also generate character appearances and animations based on the character design selected by the user. Furthermore, the generated visual style can be adjusted based on user feedback. This optimizes the game's visual elements according to the user's choices.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The reception unit inputs the user's desired game genre, difficulty level, and character design. A user interface is provided that allows the user to input the desired game genre, difficulty level, and character design using drop-down menus and sliders. Step 2: The generation unit automatically generates most of the game based on the information entered by the reception unit. Using the generation AI, it automatically generates the game's storyline, level design, character behavior, etc. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to generate each element of the game. Step 3: The feedback unit provides an interface that allows users to test play the game generated by the generation unit and provide feedback. An interface is provided that allows users to play the generated game and provide feedback on improvements and requests. For example, an input form and evaluation items are provided that allow users to provide feedback such as "This level is too difficult" or "The character's movements are unnatural." Step 4: The Improvement Department analyzes the feedback provided by the Feedback Department and reflects it in improving the game. Based on user feedback, they may adjust the difficulty of the levels or modify the character's movements. They analyze the feedback using text analysis technology and improve each element of the game based on the content of the feedback.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various 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 an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0130] 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.

[0131] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 reception section where users input their desired game genre, difficulty level, and character design; a generation unit that automatically generates a main part of a game based on the information input by the reception unit; a feedback unit for allowing a user to test play the game generated by the generation unit and provide feedback; an improvement unit that analyzes the feedback provided by the feedback unit and reflects the analysis results in improving the game. A system characterized by:

2. The reception unit Create a user interface that allows users to input their desired game genre, difficulty level, and character design using drop-down menus or sliders.

2. The system of claim 1.

3. The generation unit Using generative AI, we automatically generate parts of the game, including the storyline, level design, and character behavior.

2. The system of claim 1.

4. The feedback unit Provide an interface for users to test play the generated game and provide feedback on improvements or requests 2. The system of claim 1.

5. The improvement unit Analyze feedback provided by users and use it to improve the game 2. The system of claim 1.

6. The reception unit Estimate user emotions and change the design of the input interface in real time based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past gameplay history and automatically suggest appropriate game genres or difficulty levels 2. The system of claim 1.

8. The reception unit Based on the user's input, an instant preview is displayed, allowing the user to visually confirm the element they selected.

2. The system of claim 1.

Images