system

Abstract

The system according to the embodiment aims to generate original games according to the preferences of users and deploy them on a platform. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a play unit, and a deployment unit. The reception unit receives preferred settings from a user. The generation unit generates a game based on the settings received by the reception unit. The play unit allows the user to play the game generated by the generation unit. The deployment unit deploys the game generated by the generation unit on a platform.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technologies have not adequately created original games that meet users' preferences and deployed them on platforms, so there is room for improvement.

[0005] The system according to the embodiment aims to generate original games according to the preferences of users and deploy them on a platform. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a play unit, and a deployment unit. The reception unit receives preferred settings from a user. The generation unit generates a game based on the settings received by the reception unit. The play unit allows the user to play the game generated by the generation unit. The deployment unit deploys the game generated by the generation unit on a platform. [Effects of the Invention]

[0007] The system according to the embodiment can generate original games according to the preferences of users and deploy them on a platform. [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 1) A game generation system according to an embodiment of the present invention allows a user to set their preferred game type, game system, story, character design, UI, and so on, and then an AI generates an original game that the user can enjoy. In this game generation system, a user inputs their preferred settings, an AI generates a game based on those settings, and the generated game can be played by the user. The generated game is then deployed on a platform, where it can be ranked and experienced by other users. For example, when a user inputs their preferred settings, they can set detailed settings for the game type, game system, story, character design, UI, and so on. They can select a game type, such as an action game, RPG, or puzzle game, and further set detailed settings for the story and character design. The AI ​​then generates a game based on the input settings. The AI ​​analyzes the user's settings and generates the game's scenario, characters, graphics, UI, and so on based on those settings. For example, the AI ​​generates a scenario and designs characters based on a story selected by the user. The game system and UI are also customized based on the user's settings. The generated game can then be played by the user. Users can enjoy an original game generated based on their preferences. For example, they can play a game generated by the AI ​​based on the story and character design set by the user. The generated game is then deployed on a platform. Users can share games they have created with other users. They can also experience games created by other users. This allows games to be shared and ranked among users. For example, games created by users can be ranked on the platform, and other users can play and rate those games. This system allows users to create and enjoy original games tailored to their preferences. Furthermore, by sharing games with other users and ranking them, more users can enjoy them.This allows the game generation system to generate original games based on the user's preferences, which can then be played and shared.

[0029] A game generation system according to an embodiment includes a reception unit, a generation unit, a play unit, and a development unit. The reception unit receives user preferences. The user preferences include, but are not limited to, game type, game system, story, character design, and UI. The reception unit allows the user to select a game type, such as an action game, RPG, or puzzle game, and further set details of the story and character design. The reception unit can also provide an interface for the user to enter detailed settings. For example, the reception unit provides a form or drop-down menu for the user to input settings. The generation unit generates a game based on the settings received by the reception unit. The generation unit analyzes the user's settings, for example, using AI, and generates a game scenario, characters, graphics, UI, and the like based on the analysis. For example, the generation unit generates a scenario and character design based on a story selected by the user using AI. The generation unit can also customize the game system and UI based on the user's settings. The play unit allows the user to play the game generated by the generation unit. For example, the play unit provides an interface for the user to play the generated game. For example, the play unit provides an environment for playing a game on a platform such as a PC, console, or mobile device. Furthermore, the play unit may provide a user with operation instructions or tutorials for playing the generated game. The development unit develops the game generated by the generation unit on the platform. For example, the development unit may rank the generated game on the platform, allowing other users to experience the game. For example, the development unit may publish the generated game on the platform, allowing other users to play and rate the game. The development unit may also display the ranking of the generated game and provide an interface for users to experience other users' games. In this way, the game generation system according to the embodiment generates original games based on user preferences, allowing them to be played and shared.

[0030] The reception unit can accept detailed settings for the game type, game system, story, character design, and UI. Detailed settings include, but are not limited to, the game type, game system, story, character design, and UI. The reception unit allows the user to select a game type, such as an action game, RPG, or puzzle game, and then set details of the story and character design. The reception unit can also provide an interface for the user to enter detailed settings. For example, the reception unit may provide a form or drop-down menu for the user to input settings. This allows the user to enter detailed settings to generate a more customized game. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the setting data entered by the user to a generation AI, which may analyze the setting data and propose detailed settings.

[0031] The generation unit can generate a game scenario, characters, graphics, and UI based on user settings. The generation unit, for example, uses AI to analyze the user settings and generate the game scenario, characters, graphics, UI, etc. based on the user settings. For example, the generation unit generates a scenario and designs characters using AI based on a story selected by the user. The generation unit can also customize the game system and UI based on the user settings. For example, the generation unit can use techniques such as storyboarding, plot generation, and event sequencing to generate a scenario. For character generation, techniques such as character design, personality setting, and animation can be used. For graphic generation, techniques such as 2D graphics, 3D modeling, and texturing can be used. For UI generation, techniques such as interface design, usability testing, and responsive design can be used. This allows detailed game elements to be generated based on the user settings. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input setting data entered by the user into a generation AI, which can analyze the setting data and generate a game scenario, characters, graphics, and UI.

[0032] The play unit may enable a user to play the generated game. For example, the play unit may provide an interface for the user to play the generated game. For example, the play unit may provide an environment for playing the game on a platform such as a PC, console, or mobile device. Furthermore, the play unit may provide operation instructions and tutorials for the user when playing the generated game. For example, the play unit may display a tutorial explaining how to operate the game to support the user in smoothly playing the game. The play unit may also provide a function that saves the game progress and allows the user to resume from where they left off the previous time they play. For example, the play unit may automatically save the game progress and allow the user to resume from where they left off the previous time the next time they play. This allows the user to actually play the generated game. Some or all of the above-described processing in the play unit may be performed using, for example, AI, or may be performed without AI. For example, the play unit may input operation data used by the user when playing to a generation AI, which may analyze the operation data and provide support for the user's play.

[0033] The development unit can rank the generated games on the platform, allowing other users to experience the games. For example, the development unit can rank the generated games on the platform, allowing other users to experience the games. For example, the development unit can publish the generated games on the platform, allowing other users to play and rate the games. The development unit can also display rankings of the generated games and provide an interface for users to experience other users' games. For example, the development unit can clarify specific ranking methods and criteria. For example, the rankings can be determined using criteria such as a scoring system, user ratings, and number of plays. The development unit can also provide specific methods and environments for other users to experience the games. For example, demo play, trial versions, review functions, etc. can be provided. This allows the generated games to be shared with other users and ranked. Some or all of the above-described processing in the development unit can be performed using, for example, AI, or without AI. For example, the development unit can input evaluation data of the generated games to a generation AI, which can analyze the evaluation data and determine the rankings.

[0034] The reception unit can analyze the user's past setting history and present optimal setting options. The reception unit, for example, analyzes the user's past setting history and presents optimal setting options. For example, the reception unit can suggest similar settings based on game types that the user has previously preferred. The reception unit can also suggest new designs based on character designs that the user has previously selected. Furthermore, the reception unit can also suggest related stories by referring to stories of games the user has previously played. This makes it possible to present optimal setting options based on the past setting history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past setting data into a generation AI, which can analyze the setting data and present optimal setting options.

[0035] The reception unit may perform filtering based on the user's current gameplay status or preferences when receiving the settings. For example, the reception unit may perform filtering based on the user's current gameplay status or preferences when receiving the settings. For example, the reception unit may suggest relevant settings based on the genre of the game the user is currently playing. The reception unit may also suggest settings that can be enjoyed in a short amount of time based on the user's current gameplay time. Furthermore, the reception unit may also suggest a next setting based on the user's current in-game progress. This allows appropriate settings to be suggested based on the user's current gameplay status and preferences. Some or all of the above-described processing by the reception unit may be performed using, or without, an AI. For example, the reception unit may input the user's current gameplay data into a generation AI, which may analyze the play data and perform filtering.

[0036] When accepting settings, the reception unit can prioritize accepting highly relevant settings based on the user's geographical location information. For example, when accepting settings, the reception unit prioritizes accepting highly relevant settings by taking the user's geographical location information into consideration. For example, when accepting settings, the reception unit can suggest a game setting set in an urban area if the user is in an area rich in nature, or a game setting with a nature theme if the user is in a location rich in nature. Furthermore, when the user is at a specific event venue, the reception unit can also suggest a game setting related to the event. This allows highly relevant settings to be preferentially accepted based on the geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to a generation AI, which can analyze the location data and suggest highly relevant settings.

[0037] The reception unit can analyze the user's social media activity when receiving the settings and suggest related settings. For example, the reception unit can analyze the user's social media activity when receiving the settings and suggest related settings. For example, the reception unit can suggest similar settings based on games the user has shared on social media. The reception unit can also suggest related settings based on the work of game designers the user follows. Furthermore, the reception unit can suggest popular settings based on trends in game communities in which the user participates. This allows related settings to be suggested based on social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI, which can analyze the data and suggest related settings.

[0038] The generation unit can adjust the difficulty level or progression speed of the game based on the user's settings at the time of generation. The generation unit, for example, adjusts the difficulty level or progression speed of the game based on the user's settings at the time of generation. For example, the generation unit can set the difficulty level of the game low if the user is a beginner. Furthermore, the generation unit can set the difficulty level of the game high if the user is an advanced player. Furthermore, the generation unit can set the progression speed high if the user wants to play in a short amount of time. In this way, the difficulty level and progression speed of the game can be adjusted based on the user's settings. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the user's setting data into the generation AI, and the generation AI can analyze the setting data and adjust the difficulty level and progression speed of the game.

[0039] The generation unit can apply different generation algorithms depending on the user's settings during generation. For example, the generation unit can apply different generation algorithms depending on the user's settings during generation. For example, if the user selects an action game, the generation unit can apply a generation algorithm specialized for action. Furthermore, if the user selects an RPG, the generation unit can apply a generation algorithm that emphasizes the story. Furthermore, if the user selects a puzzle game, the generation unit can also apply a generation algorithm that enhances puzzle elements. This makes it possible to apply an appropriate generation algorithm depending on the user's settings. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's setting data into a generation AI, which can analyze the setting data and apply an appropriate generation algorithm.

[0040] The generation unit can determine the order of generation based on the user's past game play data at the time of generation. The generation unit, for example, determines the priority of generation based on the user's past game play data at the time of generation. For example, the generation unit prioritizes the generation of game types that the user has previously preferred. The generation unit can also generate an appropriate game based on the time period in which the user has previously played. Furthermore, the generation unit can generate a game by incorporating elements of games that the user has previously given high ratings. In this way, the priority of generation can be determined based on the past game play data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past play data into the generation AI, which can analyze the play data and determine the priority of generation.

[0041] The generation unit can customize the game's music and sound effects based on the user's settings at the time of generation. The generation unit, for example, customizes the game's music and sound effects based on the user's settings at the time of generation. For example, the generation unit can provide calm music if the user wants to relax. The generation unit can also provide fast-paced music if the user wants to get excited. Furthermore, the generation unit can also provide soothing sound effects if the user is feeling stressed. This allows the game's music and sound effects to be customized based on the user's settings. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's setting data into the generation AI, which can analyze the setting data to customize the music and sound effects.

[0042] The play unit can provide an appropriate gameplay experience by referring to the user's past play history during play. The play unit can, for example, provide an optimal gameplay experience by referring to the user's past play history during play. For example, the play unit can provide an optimal experience based on the user's previously preferred gameplay style. The play unit can also provide a play experience that avoids elements that the user has previously struggled with. Furthermore, the play unit can also provide a play experience that incorporates elements that the user has previously rated highly. This allows for an optimal gameplay experience to be provided based on the past play history. Some or all of the above-described processing by the play unit may be performed using, for example, AI, or may be performed without using AI. For example, the play unit can input the user's past play data into a generation AI, which can analyze the play data to provide an optimal gameplay experience.

[0043] The play unit can customize the progress of the game based on the user's current playing status during play. The play unit customizes the progress of the game based on the user's current playing status during play, for example. For example, if the user is progressing smoothly, the play unit can provide the next stage early. The play unit can also provide hints or assistance if the user is struggling. Furthermore, if the user has been playing a particular stage for a long time, the play unit can make adjustments to smooth the progress. This allows the progress of the game to be customized based on the current playing status. Some or all of the above-described processing in the play unit may be performed using, or without, AI, for example. For example, the play unit can input the user's current playing data into a generation AI, which can analyze the play data and customize the progress of the game.

[0044] The play unit can provide highly relevant game events based on the user's geographical location information during play. For example, the play unit can provide highly relevant game events taking the user's geographical location information into consideration during play. For example, when the user is in a specific area, the play unit can provide game events related to that area. Furthermore, when the user is traveling, the play unit can provide game events related to the user's travel destination. Furthermore, when the user is at a specific event venue, the play unit can provide game events related to that event. In this way, highly relevant game events can be provided based on geographical location information. Some or all of the above-described processing in the play unit may be performed using, for example, AI, or may be performed without using AI. For example, the play unit can input the user's geographical location data to a generation AI, which can analyze the location data and provide highly relevant game events.

[0045] The play unit can analyze the user's social media activity during play and provide relevant game content. For example, the play unit can analyze the user's social media activity during play and provide relevant game content. For example, the play unit can provide relevant game content based on games shared by the user on social media. The play unit can also provide relevant game content based on the work of game designers the user follows. Furthermore, the play unit can provide popular game content based on trends in game communities in which the user participates. This makes it possible to provide relevant game content based on social media activity. Some or all of the above-described processing in the play unit may be performed using, or without, AI. For example, the play unit can input the user's social media data into a generation AI, which can analyze the data and provide relevant game content.

[0046] The development unit can estimate the current rankings by referring to past ranking data during development. The development unit, for example, estimates the current rankings by referring to past ranking data during development. For example, the development unit predicts the current rankings based on past ranking data. The development unit can also predict the current rankings by analyzing trends from the past ranking data. Furthermore, the development unit can also update the current rankings in real time by referring to past ranking data. This makes it possible to predict the current rankings based on the past ranking data. Some or all of the above-described processing in the development unit may be performed using, for example, AI, or may be performed without using AI. For example, the development unit can input past ranking data into a generation AI, which analyzes the data and estimates the current rankings.

[0047] The development unit can apply different ranking algorithms to each game category during development. For example, the development unit can apply different ranking algorithms to each game category during development. For example, the development unit can apply a ranking algorithm that emphasizes score or clear time to the action game category. The development unit can also apply a ranking algorithm that emphasizes story progression or character development to the RPG category. Furthermore, the development unit can also apply a ranking algorithm that emphasizes solution speed or correct answer rate to the puzzle game category. This allows an appropriate ranking algorithm to be applied to each game category. Some or all of the above-mentioned processing in the development unit can be performed using, or without, AI, for example. For example, the development unit can input game category data to a generation AI, which can analyze the data and apply an appropriate ranking algorithm.

[0048] The expansion unit can display rankings based on the user's geographical location information during expansion. For example, the expansion unit displays rankings taking the user's geographical location information into consideration during expansion. For example, when the user is in a specific area, the expansion unit can prioritize displaying rankings for that area. Furthermore, when the user is traveling, the expansion unit can display rankings for travel destinations. Furthermore, when the user is at a specific event venue, the expansion unit can also display rankings related to that event. This makes it possible to display highly relevant rankings based on the geographical location information. Some or all of the above-described processing in the expansion unit may be performed using, for example, AI, or may be performed without using AI. For example, the expansion unit can input the user's geographical location data to a generation AI, which can analyze the location data and display the rankings.

[0049] The development unit can analyze the user's social media activity during development and provide related ranking information. For example, the development unit can analyze the user's social media activity during development and provide related ranking information. For example, the development unit can provide related ranking information based on games shared by the user on social media. The development unit can also provide related ranking information based on the work of game designers the user follows. Furthermore, the development unit can provide popular ranking information based on trends in game communities in which the user participates. This allows related ranking information to be provided based on social media activity. Some or all of the above-described processing in the development unit can be performed using, or without, AI. For example, the development unit can input the user's social media data into a generation AI, which can analyze the data and provide related ranking information.

[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 accept settings based on the voice command. For example, if the user gives a voice command such as "I want to create an action game," the reception unit analyzes the voice and displays a setting screen for the action game. Also, if the user gives a voice command such as "Make the characters cuter," the reception unit can suggest character design options based on the command. Furthermore, if the user gives a voice command such as "Make the story an adventure," the reception unit can suggest an adventure story template. This allows intuitive configuration using voice input.

[0052] The generator can customize the game's physics engine based on the user's settings. For example, if the user desires realistic physics behavior, the generator can apply an advanced physics engine. Alternatively, if the user emphasizes fantasy elements, the generator can apply an engine with unrealistic physics behavior. Furthermore, if the user prefers simple operation, the generator can reduce the complexity of the physics engine to achieve intuitive operation. This allows the application of a physics engine according to the user's settings.

[0053] The play unit can customize the game operation method based on the user's play style. For example, if the user prefers a keyboard and mouse, the play unit can provide an interface optimized for that operation method. Also, if the user uses a gamepad, the play unit can provide an operation method compatible with the gamepad. Furthermore, if the user uses a touchscreen, the play unit can also provide an interface optimized for touch operation. This makes it possible to provide an operation method that suits the user's play style.

[0054] The development unit can collect play data of the generated games and analyze users' play trends to adjust rankings. For example, if a particular game is played by many users, the ranking of that game can be raised. The development unit can also adjust rankings based on users' play time and completion rate. Furthermore, the development unit can collect user feedback and adjust rankings based on that feedback. This allows dynamic adjustment of rankings based on actual play data.

[0055] The generator can customize the game ecosystem based on the user's settings. For example, if the user places importance on natural environments, the generator can generate a game world with a rich natural environment. If the user prefers urban environments, the generator can generate a game world set in an urban setting. Furthermore, if the user desires fantasy elements, the generator can generate a game world incorporating elements of magic or another world. This makes it possible to generate a game with an ecosystem that matches the user's settings.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit receives preference settings from the user. The user's preference settings include the game type, game system, story, character design, UI, etc. For example, the user can select a game type such as an action game, RPG, or puzzle game, and then set details of the story and character design. The reception unit also provides an interface for the user to enter detailed settings. For example, it provides a form or drop-down menu for the user to enter settings. Step 2: The generation unit generates a game based on the settings received by the reception unit. The generation unit, for example, uses AI to analyze the user's settings and generate the game's scenario, characters, graphics, UI, etc. based on the analysis. For example, the AI ​​generates a scenario and designs characters based on a story selected by the user. The generation unit can also customize the game system and UI based on the user's settings. Step 3: The play unit enables the user to play the game generated by the generation unit. The play unit, for example, provides an interface for the user to play the generated game. For example, the play unit provides an environment for playing the game on a platform such as a PC, console, or mobile device. Furthermore, the play unit may provide operation instructions and tutorials for the user when playing the generated game. Step 4: The deployment unit deploys the game generated by the generation unit on the platform. The deployment unit, for example, ranks the generated game on the platform so that other users can experience the game. For example, the deployment unit publishes the generated game on the platform so that other users can play and rate the game. The deployment unit can also display the ranking of the generated game and provide an interface for users to experience other users' games.

[0058] (Example 2) A game generation system according to an embodiment of the present invention allows a user to set their preferred game type, game system, story, character design, UI, and so on, and then an AI generates an original game that the user can enjoy. In this game generation system, a user inputs their preferred settings, an AI generates a game based on those settings, and the generated game can be played by the user. The generated game is then deployed on a platform, where it can be ranked and experienced by other users. For example, when a user inputs their preferred settings, they can set detailed settings for the game type, game system, story, character design, UI, and so on. They can select a game type, such as an action game, RPG, or puzzle game, and further set detailed settings for the story and character design. The AI ​​then generates a game based on the input settings. The AI ​​analyzes the user's settings and generates the game's scenario, characters, graphics, UI, and so on based on those settings. For example, the AI ​​generates a scenario and designs characters based on a story selected by the user. The game system and UI are also customized based on the user's settings. The generated game can then be played by the user. Users can enjoy an original game generated based on their preferences. For example, they can play a game generated by the AI ​​based on the story and character design set by the user. The generated game is then deployed on a platform. Users can share games they have created with other users. They can also experience games created by other users. This allows games to be shared and ranked among users. For example, games created by users can be ranked on the platform, and other users can play and rate those games. This system allows users to create and enjoy original games tailored to their preferences. Furthermore, by sharing games with other users and ranking them, more users can enjoy them.This allows the game generation system to generate original games based on the user's preferences, which can then be played and shared.

[0059] A game generation system according to an embodiment includes a reception unit, a generation unit, a play unit, and a development unit. The reception unit receives user preferences. The user preferences include, but are not limited to, game type, game system, story, character design, and UI. The reception unit allows the user to select a game type, such as an action game, RPG, or puzzle game, and further set details of the story and character design. The reception unit can also provide an interface for the user to enter detailed settings. For example, the reception unit provides a form or drop-down menu for the user to input settings. The generation unit generates a game based on the settings received by the reception unit. The generation unit analyzes the user's settings, for example, using AI, and generates a game scenario, characters, graphics, UI, and the like based on the analysis. For example, the generation unit generates a scenario and character design based on a story selected by the user using AI. The generation unit can also customize the game system and UI based on the user's settings. The play unit allows the user to play the game generated by the generation unit. For example, the play unit provides an interface for the user to play the generated game. For example, the play unit provides an environment for playing a game on a platform such as a PC, console, or mobile device. Furthermore, the play unit may provide a user with operation instructions or tutorials for playing the generated game. The development unit develops the game generated by the generation unit on the platform. For example, the development unit may rank the generated game on the platform, allowing other users to experience the game. For example, the development unit may publish the generated game on the platform, allowing other users to play and rate the game. The development unit may also display the ranking of the generated game and provide an interface for users to experience other users' games. In this way, the game generation system according to the embodiment generates original games based on user preferences, allowing them to be played and shared.

[0060] The reception unit can accept detailed settings for the game type, game system, story, character design, and UI. Detailed settings include, but are not limited to, the game type, game system, story, character design, and UI. The reception unit allows the user to select a game type, such as an action game, RPG, or puzzle game, and then set details of the story and character design. The reception unit can also provide an interface for the user to enter detailed settings. For example, the reception unit may provide a form or drop-down menu for the user to input settings. This allows the user to enter detailed settings to generate a more customized game. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the setting data entered by the user to a generation AI, which may analyze the setting data and propose detailed settings.

[0061] The generation unit can generate a game scenario, characters, graphics, and UI based on user settings. The generation unit, for example, uses AI to analyze the user settings and generate the game scenario, characters, graphics, UI, etc. based on the user settings. For example, the generation unit generates a scenario and designs characters using AI based on a story selected by the user. The generation unit can also customize the game system and UI based on the user settings. For example, the generation unit can use techniques such as storyboarding, plot generation, and event sequencing to generate a scenario. For character generation, techniques such as character design, personality setting, and animation can be used. For graphic generation, techniques such as 2D graphics, 3D modeling, and texturing can be used. For UI generation, techniques such as interface design, usability testing, and responsive design can be used. This allows detailed game elements to be generated based on the user settings. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input setting data entered by the user into a generation AI, which can analyze the setting data and generate a game scenario, characters, graphics, and UI.

[0062] The play unit may enable a user to play the generated game. For example, the play unit may provide an interface for the user to play the generated game. For example, the play unit may provide an environment for playing the game on a platform such as a PC, console, or mobile device. Furthermore, the play unit may provide operation instructions and tutorials for the user when playing the generated game. For example, the play unit may display a tutorial explaining how to operate the game to support the user in smoothly playing the game. The play unit may also provide a function that saves the game progress and allows the user to resume from where they left off the previous time they play. For example, the play unit may automatically save the game progress and allow the user to resume from where they left off the previous time the next time they play. This allows the user to actually play the generated game. Some or all of the above-described processing in the play unit may be performed using, for example, AI, or may be performed without AI. For example, the play unit may input operation data used by the user when playing to a generation AI, which may analyze the operation data and provide support for the user's play.

[0063] The development unit can rank the generated games on the platform, allowing other users to experience the games. For example, the development unit can rank the generated games on the platform, allowing other users to experience the games. For example, the development unit can publish the generated games on the platform, allowing other users to play and rate the games. The development unit can also display rankings of the generated games and provide an interface for users to experience other users' games. For example, the development unit can clarify specific ranking methods and criteria. For example, the rankings can be determined using criteria such as a scoring system, user ratings, and number of plays. The development unit can also provide specific methods and environments for other users to experience the games. For example, demo play, trial versions, review functions, etc. can be provided. This allows the generated games to be shared with other users and ranked. Some or all of the above-described processing in the development unit can be performed using, for example, AI, or without AI. For example, the development unit can input evaluation data of the generated games to a generation AI, which can analyze the evaluation data and determine the rankings.

[0064] The reception unit can estimate the user's emotions and suggest settings based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and suggest settings based on the estimated user emotions. For example, if the user is excited, the reception unit can suggest an action game or a thrilling story. Furthermore, if the user is relaxed, the reception unit can suggest a puzzle game or a calming story. Furthermore, if the user is stressed, the reception unit can suggest a soothing character design or UI. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This enables settings to be suggested based on the user's emotions. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI, which can analyze the emotion data and suggest settings.

[0065] The reception unit can analyze the user's past setting history and present optimal setting options. The reception unit, for example, analyzes the user's past setting history and presents optimal setting options. For example, the reception unit can suggest similar settings based on game types that the user has previously preferred. The reception unit can also suggest new designs based on character designs that the user has previously selected. Furthermore, the reception unit can also suggest related stories by referring to stories of games the user has previously played. This makes it possible to present optimal setting options based on the past setting history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past setting data into a generation AI, which can analyze the setting data and present optimal setting options.

[0066] The reception unit may perform filtering based on the user's current gameplay status or preferences when receiving the settings. For example, the reception unit may perform filtering based on the user's current gameplay status or preferences when receiving the settings. For example, the reception unit may suggest relevant settings based on the genre of the game the user is currently playing. The reception unit may also suggest settings that can be enjoyed in a short amount of time based on the user's current gameplay time. Furthermore, the reception unit may also suggest a next setting based on the user's current in-game progress. This allows appropriate settings to be suggested based on the user's current gameplay status and preferences. Some or all of the above-described processing by the reception unit may be performed using, or without, an AI. For example, the reception unit may input the user's current gameplay data into a generation AI, which may analyze the play data and perform filtering.

[0067] The reception unit can estimate the user's emotions and determine the priority of settings based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of settings based on the estimated user emotions. For example, if the user is excited, the reception unit can preferentially display settings for an action game. Furthermore, if the user is relaxed, the reception unit can preferentially display settings for a puzzle game. Furthermore, if the user is stressed, the reception unit can preferentially display settings for a soothing theme. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the priority of settings to be determined according to the user's emotions. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI, which can analyze the emotion data and determine the priority of settings.

[0068] When accepting settings, the reception unit can prioritize accepting highly relevant settings based on the user's geographical location information. For example, when accepting settings, the reception unit prioritizes accepting highly relevant settings by taking the user's geographical location information into consideration. For example, when accepting settings, the reception unit can suggest a game setting set in an urban area if the user is in an area rich in nature, or a game setting with a nature theme if the user is in a location rich in nature. Furthermore, when the user is at a specific event venue, the reception unit can also suggest a game setting related to the event. This allows highly relevant settings to be preferentially accepted based on the geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to a generation AI, which can analyze the location data and suggest highly relevant settings.

[0069] The reception unit can analyze the user's social media activity when receiving the settings and suggest related settings. For example, the reception unit can analyze the user's social media activity when receiving the settings and suggest related settings. For example, the reception unit can suggest similar settings based on games the user has shared on social media. The reception unit can also suggest related settings based on the work of game designers the user follows. Furthermore, the reception unit can suggest popular settings based on trends in game communities in which the user participates. This allows related settings to be suggested based on social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI, which can analyze the data and suggest related settings.

[0070] The generation unit can estimate the user's emotions and adjust the game scenario and character expression methods based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the game scenario and character expression methods based on the estimated user emotions. For example, if the user is excited, the generation unit can add a tense development to the scenario. Also, if the user is relaxed, the generation unit can add a calm development to the scenario. Furthermore, if the user is stressed, the generation unit can adjust the character's expression to a soothing one. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the game scenario and character expression methods to be adjusted according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI, which can analyze the emotion data and adjust the scenario and character expression methods.

[0071] The generation unit can adjust the difficulty level or progression speed of the game based on the user's settings at the time of generation. The generation unit, for example, adjusts the difficulty level or progression speed of the game based on the user's settings at the time of generation. For example, the generation unit can set the difficulty level of the game low if the user is a beginner. Furthermore, the generation unit can set the difficulty level of the game high if the user is an advanced player. Furthermore, the generation unit can set the progression speed high if the user wants to play in a short amount of time. In this way, the difficulty level and progression speed of the game can be adjusted based on the user's settings. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the user's setting data into the generation AI, and the generation AI can analyze the setting data and adjust the difficulty level and progression speed of the game.

[0072] The generation unit can apply different generation algorithms depending on the user's settings during generation. For example, the generation unit can apply different generation algorithms depending on the user's settings during generation. For example, if the user selects an action game, the generation unit can apply a generation algorithm specialized for action. Furthermore, if the user selects an RPG, the generation unit can apply a generation algorithm that emphasizes the story. Furthermore, if the user selects a puzzle game, the generation unit can also apply a generation algorithm that enhances puzzle elements. This makes it possible to apply an appropriate generation algorithm depending on the user's settings. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's setting data into a generation AI, which can analyze the setting data and apply an appropriate generation algorithm.

[0073] The generation unit can estimate a user's emotion and adjust the game graphics and UI design based on the estimated user emotion. The generation unit, for example, estimates a user's emotion and adjusts the game graphics and UI design based on the estimated user emotion. For example, the generation unit can provide graphics with vivid colors when the user is excited. The generation unit can also provide graphics with calm colors when the user is relaxed. Furthermore, the generation unit can provide a simple, highly visible UI when the user is stressed. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the game graphics and UI design to be adjusted according to the user's emotion. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input user emotion data into the generation AI, which can analyze the emotion data and adjust the graphic and UI design.

[0074] The generation unit can determine the order of generation based on the user's past game play data at the time of generation. The generation unit, for example, determines the priority of generation based on the user's past game play data at the time of generation. For example, the generation unit prioritizes the generation of game types that the user has previously preferred. The generation unit can also generate an appropriate game based on the time period in which the user has previously played. Furthermore, the generation unit can generate a game by incorporating elements of games that the user has previously given high ratings. In this way, the priority of generation can be determined based on the past game play data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past play data into the generation AI, which can analyze the play data and determine the priority of generation.

[0075] The generation unit can customize the game's music and sound effects based on the user's settings at the time of generation. The generation unit, for example, customizes the game's music and sound effects based on the user's settings at the time of generation. For example, the generation unit can provide calm music if the user wants to relax. The generation unit can also provide fast-paced music if the user wants to get excited. Furthermore, the generation unit can also provide soothing sound effects if the user is feeling stressed. This allows the game's music and sound effects to be customized based on the user's settings. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's setting data into the generation AI, which can analyze the setting data to customize the music and sound effects.

[0076] The play unit can estimate the user's emotions and adjust the game difficulty in real time based on the estimated user emotions. The play unit, for example, estimates the user's emotions and adjusts the game difficulty in real time based on the estimated user emotions. For example, if the user is excited, the play unit can increase the difficulty to provide a more challenging game. Also, if the user is relaxed, the play unit can lower the difficulty to provide a more gentle game. Furthermore, if the user is stressed, the play unit can lower the difficulty to provide a more relaxing game. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the game difficulty to be adjusted in real time according to the user's emotions. Some or all of the above-described processing in the play unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the play unit can input the user's emotion data into the generation AI, which can analyze the emotion data and adjust the difficulty in real time.

[0077] The play unit can provide an appropriate gameplay experience by referring to the user's past play history during play. The play unit can, for example, provide an optimal gameplay experience by referring to the user's past play history during play. For example, the play unit can provide an optimal experience based on the user's previously preferred gameplay style. The play unit can also provide a play experience that avoids elements that the user has previously struggled with. Furthermore, the play unit can also provide a play experience that incorporates elements that the user has previously rated highly. This allows for an optimal gameplay experience to be provided based on the past play history. Some or all of the above-described processing by the play unit may be performed using, for example, AI, or may be performed without using AI. For example, the play unit can input the user's past play data into a generation AI, which can analyze the play data to provide an optimal gameplay experience.

[0078] The play unit can customize the progress of the game based on the user's current playing status during play. The play unit customizes the progress of the game based on the user's current playing status during play, for example. For example, if the user is progressing smoothly, the play unit can provide the next stage early. The play unit can also provide hints or assistance if the user is struggling. Furthermore, if the user has been playing a particular stage for a long time, the play unit can make adjustments to smooth the progress. This allows the progress of the game to be customized based on the current playing status. Some or all of the above-described processing in the play unit may be performed using, or without, AI, for example. For example, the play unit can input the user's current playing data into a generation AI, which can analyze the play data and customize the progress of the game.

[0079] The play unit can estimate the user's emotions and provide game hints and advice based on the estimated user emotions. For example, the play unit can estimate the user's emotions and provide game hints and advice based on the estimated user emotions. For example, if the user is excited, the play unit can provide challenging hints. Furthermore, if the user is relaxed, the play unit can provide gentle advice. Furthermore, if the user is stressed, the play unit can provide relaxing hints. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows appropriate hints and advice to be provided based on the user's emotions. Some or all of the above-described processing in the play unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the play unit can input the user's emotion data into the generation AI, which can analyze the emotion data and provide hints and advice.

[0080] The play unit can provide highly relevant game events based on the user's geographical location information during play. For example, the play unit can provide highly relevant game events taking the user's geographical location information into consideration during play. For example, when the user is in a specific area, the play unit can provide game events related to that area. Furthermore, when the user is traveling, the play unit can provide game events related to the user's travel destination. Furthermore, when the user is at a specific event venue, the play unit can provide game events related to that event. In this way, highly relevant game events can be provided based on geographical location information. Some or all of the above-described processing in the play unit may be performed using, for example, AI, or may be performed without using AI. For example, the play unit can input the user's geographical location data to a generation AI, which can analyze the location data and provide highly relevant game events.

[0081] The play unit can analyze the user's social media activity during play and provide relevant game content. For example, the play unit can analyze the user's social media activity during play and provide relevant game content. For example, the play unit can provide relevant game content based on games shared by the user on social media. The play unit can also provide relevant game content based on the work of game designers the user follows. Furthermore, the play unit can provide popular game content based on trends in game communities in which the user participates. This makes it possible to provide relevant game content based on social media activity. Some or all of the above-described processing in the play unit may be performed using, or without, AI. For example, the play unit can input the user's social media data into a generation AI, which can analyze the data and provide relevant game content.

[0082] The development unit can estimate the user's emotions and adjust the display method of the rankings based on the estimated user emotions. The development unit, for example, estimates the user's emotions and adjusts the display method of the rankings based on the estimated user emotions. For example, if the user is excited, the development unit can display the rankings in a visually stimulating design. Furthermore, if the user is relaxed, the development unit can display the rankings in a calming design. Furthermore, if the user is stressed, the development unit can display a simple, highly visible ranking. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the display method of the rankings to be adjusted according to the user's emotions. Some or all of the above-described processing in the development unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the development unit can input the user's emotion data into the generation AI, which can analyze the emotion data and adjust the display method of the rankings.

[0083] The development unit can estimate the current rankings by referring to past ranking data during development. The development unit, for example, estimates the current rankings by referring to past ranking data during development. For example, the development unit predicts the current rankings based on past ranking data. The development unit can also predict the current rankings by analyzing trends from the past ranking data. Furthermore, the development unit can also update the current rankings in real time by referring to past ranking data. This makes it possible to predict the current rankings based on the past ranking data. Some or all of the above-described processing in the development unit may be performed using, for example, AI, or may be performed without using AI. For example, the development unit can input past ranking data into a generation AI, which analyzes the data and estimates the current rankings.

[0084] The development unit can apply different ranking algorithms to each game category during development. For example, the development unit can apply different ranking algorithms to each game category during development. For example, the development unit can apply a ranking algorithm that emphasizes score or clear time to the action game category. The development unit can also apply a ranking algorithm that emphasizes story progression or character development to the RPG category. Furthermore, the development unit can also apply a ranking algorithm that emphasizes solution speed or correct answer rate to the puzzle game category. This allows an appropriate ranking algorithm to be applied to each game category. Some or all of the above-mentioned processing in the development unit can be performed using, or without, AI, for example. For example, the development unit can input game category data to a generation AI, which can analyze the data and apply an appropriate ranking algorithm.

[0085] The development unit can estimate the user's emotions and adjust the importance of the rankings based on the estimated user emotions. The development unit, for example, estimates the user's emotions and adjusts the importance of the rankings based on the estimated user emotions. For example, if the user is excited, the development unit can set the importance of the rankings high and display them prominently. If the user is relaxed, the development unit can set the importance of the rankings low and display them modestly. Furthermore, if the user is stressed, the development unit can set the importance of the rankings to medium and display them in a balanced manner. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the importance of the rankings to be adjusted according to the user's emotions. Some or all of the above-described processing in the development unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the development unit can input the user's emotional data into the generation AI, which can then analyze the emotional data and adjust the importance of the ranking.

[0086] The expansion unit can display rankings based on the user's geographical location information during expansion. For example, the expansion unit displays rankings taking the user's geographical location information into consideration during expansion. For example, when the user is in a specific area, the expansion unit can prioritize displaying rankings for that area. Furthermore, when the user is traveling, the expansion unit can display rankings for travel destinations. Furthermore, when the user is at a specific event venue, the expansion unit can also display rankings related to that event. This makes it possible to display highly relevant rankings based on the geographical location information. Some or all of the above-described processing in the expansion unit may be performed using, for example, AI, or may be performed without using AI. For example, the expansion unit can input the user's geographical location data to a generation AI, which can analyze the location data and display the rankings.

[0087] The development unit can analyze the user's social media activity during development and provide related ranking information. For example, the development unit can analyze the user's social media activity during development and provide related ranking information. For example, the development unit can provide related ranking information based on games shared by the user on social media. The development unit can also provide related ranking information based on the work of game designers the user follows. Furthermore, the development unit can provide popular ranking information based on trends in game communities in which the user participates. This allows related ranking information to be provided based on social media activity. Some or all of the above-described processing in the development unit can be performed using, or without, AI. For example, the development unit can input the user's social media data into a generation AI, which can analyze the data and provide related ranking information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, play unit, and development 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 is realized by the control unit 46A of the smart device 14 and provides an interface for the user to input preferred settings. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a game based on the user's settings using AI. The play unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to play the generated game. The development unit is realized by the specific processing unit 290 of the data processing device 12 and publishes the generated game on a platform so that other users can experience the game. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, play unit, and development 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 is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to input preferred settings. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a game based on the user's settings using AI. The play unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides an interface for the user to play the generated game. The development unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and publishes the generated game on a platform so that other users can experience the game. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, play unit, and development unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for the user to input preferred settings. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a game based on the user's settings using AI. The play unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for the user to play the generated game. The development unit is realized by the specific processing unit 290 of the data processing device 12 and publishes the generated game on a platform so that other users can experience the game. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, play unit, and development unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to input preferred settings. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a game based on the user's settings using AI. The play unit is realized, for example, by the control unit 46A of the robot 414 and provides an interface for the user to play the generated game. The development unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and publishes the generated game on a platform so that other users can experience the game.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The reception unit can analyze the user's voice input and accept settings based on the voice command. For example, if the user gives a voice command such as "I want to create an action game," the reception unit analyzes the voice and displays a setting screen for the action game. Also, if the user gives a voice command such as "Make the characters cuter," the reception unit can suggest character design options based on the command. Furthermore, if the user gives a voice command such as "Make the story an adventure," the reception unit can suggest an adventure story template. This allows intuitive configuration using voice input.

[0090] The generator can customize the game's physics engine based on the user's settings. For example, if the user desires realistic physics behavior, the generator can apply an advanced physics engine. Alternatively, if the user emphasizes fantasy elements, the generator can apply an engine with unrealistic physics behavior. Furthermore, if the user prefers simple operation, the generator can reduce the complexity of the physics engine to achieve intuitive operation. This allows the application of a physics engine according to the user's settings.

[0091] The play unit can customize the game operation method based on the user's play style. For example, if the user prefers a keyboard and mouse, the play unit can provide an interface optimized for that operation method. Also, if the user uses a gamepad, the play unit can provide an operation method compatible with the gamepad. Furthermore, if the user uses a touchscreen, the play unit can also provide an interface optimized for touch operation. This makes it possible to provide an operation method that suits the user's play style.

[0092] The development unit can collect play data of the generated games and analyze users' play trends to adjust rankings. For example, if a particular game is played by many users, the ranking of that game can be raised. The development unit can also adjust rankings based on users' play time and completion rate. Furthermore, the development unit can collect user feedback and adjust rankings based on that feedback. This allows dynamic adjustment of rankings based on actual play data.

[0093] The reception unit can estimate the user's emotions and suggest game themes based on the estimated user emotions. For example, if the user is excited, an action or adventure-themed game can be suggested. If the user is relaxed, a puzzle or simulation-themed game can be suggested. Furthermore, if the user is stressed, a soothing theme can be suggested. In this way, themes can be suggested according to the user's emotions.

[0094] The generation unit can estimate the user's emotions and adjust the game's music and sound effects based on the estimated user's emotions. For example, if the user is excited, fast-paced music and intense sound effects can be provided. If the user is relaxed, calm music and quiet sound effects can be provided. Furthermore, if the user is stressed, soothing music and sound effects can be provided. In this way, music and sound effects can be provided according to the user's emotions.

[0095] The play unit can estimate the user's emotions and adjust the game progress speed in real time based on the estimated user's emotions. For example, if the user is excited, the game progress speed can be increased. Alternatively, if the user is relaxed, the game progress speed can be decreased. Furthermore, if the user is feeling stressed, the game progress speed can be adjusted to provide a relaxing experience. In this way, the game progress speed can be adjusted in real time according to the user's emotions.

[0096] The development unit can estimate the user's emotions and filter game reviews and comments based on the estimated user emotions. For example, if the user is excited, positive reviews and comments can be preferentially displayed. If the user is relaxed, balanced reviews and comments can be displayed. Furthermore, if the user is stressed, negative reviews and comments can be filtered out and not displayed. This allows reviews and comments to be displayed according to the user's emotions.

[0097] The reception unit can estimate the user's emotions and suggest a game difficulty level based on the estimated user's emotions. For example, if the user is excited, a game with a high level of difficulty can be suggested. If the user is relaxed, a game with a low level of difficulty can be suggested. Furthermore, if the user is feeling stressed, a game with an adjustable level of difficulty that allows the user to relax can be suggested. In this way, it is possible to suggest games with a level of difficulty that corresponds to the user's emotions.

[0098] The generator can customize the game ecosystem based on the user's settings. For example, if the user places importance on natural environments, the generator can generate a game world with a rich natural environment. If the user prefers urban environments, the generator can generate a game world set in an urban setting. Furthermore, if the user desires fantasy elements, the generator can generate a game world incorporating elements of magic or another world. This makes it possible to generate a game with an ecosystem that matches the user's settings.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit receives preference settings from the user. The user's preference settings include the game type, game system, story, character design, UI, etc. For example, the user can select a game type such as an action game, RPG, or puzzle game, and then set details of the story and character design. The reception unit also provides an interface for the user to enter detailed settings. For example, it provides a form or drop-down menu for the user to enter settings. Step 2: The generation unit generates a game based on the settings received by the reception unit. The generation unit, for example, uses AI to analyze the user's settings and generate the game's scenario, characters, graphics, UI, etc. based on the analysis. For example, the AI ​​generates a scenario and designs characters based on a story selected by the user. The generation unit can also customize the game system and UI based on the user's settings. Step 3: The play unit enables the user to play the game generated by the generation unit. The play unit, for example, provides an interface for the user to play the generated game. For example, the play unit provides an environment for playing the game on a platform such as a PC, console, or mobile device. Furthermore, the play unit may provide operation instructions and tutorials for the user when playing the generated game. Step 4: The deployment unit deploys the game generated by the generation unit on the platform. The deployment unit, for example, ranks the generated game on the platform so that other users can experience the game. For example, the deployment unit publishes the generated game on the platform so that other users can play and rate the game. The deployment unit can also display the ranking of the generated game and provide an interface for users to experience other users' games.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 unit that receives preference settings from a user; a generation unit that generates a game based on the settings accepted by the acceptance unit; a play unit through which a user plays the game generated by the generation unit; a deployment unit that deploys the game generated by the generation unit on a platform; Equipped with A system characterized by:

2. The reception unit Accepts detailed settings for game type, game system, story, character design, and UI.

2. The system of claim 1.

3. The generation unit Generate game scenarios, characters, graphics, and UI based on user settings 2. The system of claim 1.

4. The play section includes: Allowing users to play generated games 2. The system of claim 1.

5. The expansion section The games created will be ranked on the platform, allowing other users to experience them.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and suggest settings based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past configuration history and present optimal configuration options 2. The system of claim 1.

8. The reception unit When accepting settings, filter based on the user's current gameplay or preferences 2. The system of claim 1.

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