System

Abstract

The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.SOLUTION: A system according to an embodiment includes a generation AI, a prompt inputter, and an object generator. The generation AI inputs prompts from the user. The prompt input unit inputs a prompt from a user. The object generation unit generates an object based on the prompt input by the prompt input unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, game development was subject to resource limitations, which placed certain restrictions on expression and design.

[0005] The system according to the embodiment aims to generate objects in a game without being subject to resource limitations. [Means for solving the problem]

[0006] A system according to an embodiment includes a generation AI, a prompt input unit, and an object generation unit. The generation AI inputs a prompt from a user. The prompt input unit inputs a prompt from the user. The object generation unit generates an object based on the prompt input by the prompt input unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate objects in a game without being subject to resource limitations. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A game system according to an embodiment of the present invention uses a generation AI to generate objects in real time during a game. In this system, the generation AI generates objects in real time based on prompts input by a user. This allows the game system to overcome resource limitations in conventional games and provide a new gaming experience.

[0029] A game system according to an embodiment includes a generation AI, a prompt input unit, and an object generation unit. The generation AI generates an object based on a prompt from a user. For example, if the user inputs a prompt such as "build a castle shaped like a dragon," the generation AI analyzes the instruction and generates a 3D model of a dragon-shaped castle. Alternatively, if the user inputs a prompt such as "a giant tree floating in space," the generation AI can generate an object based on the instruction and place it in the game. The prompt input unit inputs a prompt from the user. For example, the prompt input unit can include a keyboard for inputting a text prompt or a microphone for inputting a voice prompt. The object generation unit generates an object based on the prompt input by the prompt input unit. For example, the object generation unit places the object generated by the generation AI in the game. The object generation unit can also fine-tune the design of the object generated by the generation AI. This allows the game system according to an embodiment to generate objects in real time based on a user's prompt. For example, if the user inputs a prompt such as "build an underwater city," the generation AI generates an underwater city object based on the instruction, allowing the user to explore the city.

[0030] The object generation unit can refer to the user's past prompt history and customize the object to suit the user's preferences. For example, the object generation unit uses a generation AI to analyze the user's past prompt history and generate an object that suits the user's preferences. For example, the object generation unit reflects the user's preferred design based on the trends in prompts entered in the past. The object generation unit also has a database that stores the user's prompt history and customizes the object based on that history. For example, the object generation unit references past prompts stored in the database and generates an object that suits the user's preferences. This allows the object to be customized based on the user's past prompt history.

[0031] The object generation unit can have a function that reflects environmental changes in real time. For example, the object generation unit uses a generation AI to obtain weather data in real time and reflect it in the design of the object. For example, if it is raining, a water droplet effect is added to the object. The object generation unit can also change the design of the object according to the time of day. For example, it generates bright-colored objects during the day and dark-colored objects at night. This makes it possible to reflect environmental changes in real time.

[0032] The prompt input unit can have a function that allows a prompt to be input using voice input. For example, the prompt input unit allows a user to input a prompt by voice, and the generation AI analyzes the voice to generate an object. For example, if a user gives a voice command such as "Make a red dragon," the generation AI generates a red dragon object. The prompt input unit can also convert voice prompts into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. This allows a prompt to be input using voice input.

[0033] The prompt input unit can have a function for inputting a prompt collaboratively with other users and generating an object. For example, the prompt input unit allows multiple users to input a prompt collaboratively, and the generation AI generates an object based on the prompt. For example, if user A inputs "Make a castle in the shape of a dragon" and user B inputs "Make it red," the generation AI generates a castle in the shape of a red dragon. The prompt input unit also has an interface for collaborative work. For example, it provides a chat function that allows multiple users to input prompts simultaneously, and a collaborative editing function. This allows users to input prompts collaboratively with other users and generate objects.

[0034] The object generation unit can learn the user's play style and optimize the game design accordingly. For example, the object generation unit uses a generation AI to analyze the user's play style and optimize the game design accordingly. For example, a user who is good at action games can be provided with a design that enhances action elements. The object generation unit also has an algorithm for learning the user's play style. For example, the object generation unit can analyze the user's play style based on past play data and optimize the game design based on the results. This allows the game design to be optimized according to the user's play style.

[0035] The object generation unit can have a function to reflect real-time user feedback and dynamically change the game design. For example, the object generation unit uses a generation AI to dynamically change the game design based on real-time user feedback. For example, if the user provides feedback such as "add more enemies," the number of enemies is increased. The object generation unit also has a system for collecting real-time feedback and changing the game design based on that feedback. For example, the system analyzes the feedback and adjusts in-game elements based on the results. This allows the game design to be dynamically changed based on real-time user feedback.

[0036] The object generation unit can have a function for generating designs corresponding to different game genres. For example, the object generation unit generates objects corresponding to different game genres using a generation AI. For example, the object generation unit generates characters for RPGs and buildings for simulations. The object generation unit also has design templates for corresponding to different game genres. For example, the object generation unit uses templates for generating effects for action games and items for puzzle games. This makes it possible to generate designs corresponding to different game genres.

[0037] The object generation unit may have a function for allowing a user to share an object created by the user with other users and collaborate on game design. The object generation unit may, for example, have a function for allowing a user to share an object created by the user with other users and collaborate on game design. For example, a character created by user A is used by user B. The object generation unit may also have a system for managing shared objects. For example, the object may be saved using cloud storage and shared with other users. This allows a user to share an object created by the user with other users and collaborate on game design.

[0038] The object generation unit can provide an individually customized game experience by referencing the user's past game play data. For example, the object generation unit uses a generation AI to analyze the user's past game play data and provide an individually customized game experience. For example, it can enhance elements that the user has played with preference in the past. The object generation unit also includes a database that stores the user's game play data and customizes the game experience based on the data. For example, it can reference the user's past play history stored in the database and provide a game experience tailored to the user's preferences. This makes it possible to provide an individually customized game experience based on the user's past game play data.

[0039] The object generation unit can have a function to dynamically change the in-game environment and scenario by reflecting the user's real-time actions. For example, the object generation unit uses a generation AI to dynamically change the in-game environment based on the user's real-time actions. For example, when the user reaches a specific location, the environment of that location changes. The object generation unit also has a system for analyzing the user's actions in real time and changing the scenario based on the results. For example, the development of the scenario changes according to the user's selection. This allows the in-game environment and scenario to be dynamically changed based on the user's real-time actions.

[0040] The object generation unit can have a function to provide a gaming experience compatible with different platforms. For example, the object generation unit generates objects compatible with different platforms using a generation AI, thereby providing a gaming experience in VR or AR. For example, the object generation unit generates 3D models for VR and objects for AR. The object generation unit also has a system for supporting different platforms. For example, the object generation unit generates objects compatible with mobile devices and console game machines. This allows a gaming experience compatible with different platforms to be provided.

[0041] The object generation unit may have a function for sharing a game experience created by a user with other users and collaboratively creating a new game experience. The object generation unit, for example, has a function for sharing a game experience created by a user with other users and collaboratively creating a new game experience. For example, user B plays a quest created by user A. The object generation unit also has a system for managing the shared game experience. For example, the game experience is saved using cloud storage and shared with other users. This allows the game experience created by a user to be shared with other users and collaboratively create a new game experience.

[0042] The object generation unit customizes objects according to the user's region and culture, making it possible to respond to global markets. For example, the object generation unit uses a generation AI to analyze the user's region and culture and customize the object based on that data. For example, it may reflect the traditional design of a particular region. The object generation unit also includes a system for customizing objects according to region and culture. For example, it may change the design of an object based on the user's region and culture. This allows customization according to the user's region and culture to respond to global markets.

[0043] The object generation unit can have a function that reflects real-time market trends and dynamically generates new game content. For example, the object generation unit uses a generation AI to analyze real-time market trends and generate new game content based on that data. For example, it incorporates currently popular themes. The object generation unit also has a system for reflecting market trends. For example, it generates objects based on the latest game genres and popular themes. This allows new game content to be dynamically generated, reflecting real-time market trends.

[0044] The object generation unit can have a function for generating game content that can be applied to different industries. For example, the object generation unit generates game content that can be applied to different industries using a generation AI. For example, the object generation unit generates interactive educational materials. The object generation unit also has a system for application to different industries. For example, the object generation unit generates medical simulation content or interactive stories for entertainment. This makes it possible to generate game content that can be applied to different industries.

[0045] The object generation unit may have a function for sharing game content created by a user with other users and collaboratively developing new markets. The object generation unit may, for example, have a function for sharing game content created by a user with other users and collaboratively developing new markets. For example, educational content created by user A is used by user B. The object generation unit may also have a system for managing shared game content. For example, the game content may be stored using cloud storage and shared with other users. This allows the game content created by a user to be shared with other users and collaboratively developing new markets.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The object generation unit can acquire the user's health data and dynamically adjust the in-game difficulty level based on that data. For example, if the user's heart rate is high, the difficulty level of the game can be lowered to reduce stress. Also, if the user is highly fatigued, the game progress can be slowed down. Furthermore, it is possible to change in-game activities according to the user's health state. This makes it possible to provide a gaming experience that is tailored to the user's health state.

[0048] The object generation unit can acquire the user's geographical location information and customize the in-game environment based on that information. For example, if the user is in a mountainous area, the in-game environment can be changed to a mountainous landscape. Alternatively, if the user is in an urban area, the in-game environment can be changed to reflect an urban landscape. Furthermore, it is possible to provide specific events or quests based on the user's location information. This makes it possible to provide a game experience tailored to the user's geographical location.

[0049] The object generation unit can analyze the user's past game play data and suggest new game modes based on that data. For example, if the user has played many action games in the past, a mode with enhanced action elements can be suggested. Also, if the user likes puzzle games, a mode incorporating puzzle elements can be suggested. Furthermore, it is possible to provide modes customized according to the user's play style. This makes it possible to suggest new game modes based on the user's past play data.

[0050] The object generation unit can analyze the user's real-time behavioral data and dynamically adjust the AI ​​of enemies in the game based on that data. For example, if the user frequently uses a particular tactic, the enemy AI will take actions that counter that tactic. Alternatively, if the user has a cautious playing style, the enemy AI can become more aggressive. Furthermore, it is possible to adjust the placement and number of enemies based on the user's behavioral data. This makes it possible to provide enemy AI that responds to the user's actions.

[0051] The object generation unit can dynamically adjust the difficulty of puzzles and quests in the game based on real-time user feedback. For example, if the user provides feedback such as "this puzzle is too difficult," the difficulty of the puzzle can be lowered. Alternatively, if the user provides feedback such as "I want a more challenging quest," the difficulty of the quest can be increased. Furthermore, new puzzles and quests can be added based on user feedback. This makes it possible to provide a game experience that responds to the user's real-time feedback.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The prompt input unit inputs a prompt from the user. For example, the prompt input unit may include a keyboard for inputting a prompt in text format or a microphone for inputting a prompt in audio format. Step 2: The object generation unit generates an object based on the prompt input by the prompt input unit. For example, the object generated by the generation AI can be placed in the game and the design of the object can be fine-tuned. Step 3: The generation AI generates objects based on prompts from the user. For example, if a user inputs a prompt such as "Make a castle in the shape of a dragon," the AI ​​analyzes the instruction and generates a 3D model of a castle in the shape of a dragon. Similarly, if a user inputs a prompt such as "A giant tree floating in space," the AI ​​can generate an object based on the instruction and place it in the game.

[0054] (Example 2) A game system according to an embodiment of the present invention uses a generation AI to generate objects in real time during a game. In this system, the generation AI generates objects in real time based on prompts input by a user. This allows the game system to overcome resource limitations in conventional games and provide a new gaming experience.

[0055] A game system according to an embodiment includes a generation AI, a prompt input unit, and an object generation unit. The generation AI generates an object based on a prompt from a user. For example, if the user inputs a prompt such as "build a castle shaped like a dragon," the generation AI analyzes the instruction and generates a 3D model of a dragon-shaped castle. Alternatively, if the user inputs a prompt such as "a giant tree floating in space," the generation AI can generate an object based on the instruction and place it in the game. The prompt input unit inputs a prompt from the user. For example, the prompt input unit can include a keyboard for inputting a text prompt or a microphone for inputting a voice prompt. The object generation unit generates an object based on the prompt input by the prompt input unit. For example, the object generation unit places the object generated by the generation AI in the game. The object generation unit can also fine-tune the design of the object generated by the generation AI. This allows the game system according to an embodiment to generate objects in real time based on a user's prompt. For example, if the user inputs a prompt such as "build an underwater city," the generation AI generates an underwater city object based on the instruction, allowing the user to explore the city.

[0056] The object generation unit can estimate the user's emotions and fine-tune the object design based on those emotions. For example, the object generation unit uses a generation AI to analyze the user's emotions in real time and fine-tune the color and shape of the object according to the emotion. For example, if the user expresses surprise, the color of the object is changed to a more vivid color. The object generation unit also includes an algorithm for changing the object design based on the user's emotions. For example, the object generation unit uses an emotion estimation algorithm to analyze the user's emotions and changes the object design based on the results. This allows the object design to be dynamically changed according to the user's emotions.

[0057] The object generation unit can refer to the user's past prompt history and customize the object to suit the user's preferences. For example, the object generation unit uses a generation AI to analyze the user's past prompt history and generate an object that suits the user's preferences. For example, the object generation unit reflects the user's preferred design based on the trends in prompts entered in the past. The object generation unit also has a database that stores the user's prompt history and customizes the object based on that history. For example, the object generation unit references past prompts stored in the database and generates an object that suits the user's preferences. This allows the object to be customized based on the user's past prompt history.

[0058] The object generation unit can have a function that reflects environmental changes in real time. For example, the object generation unit uses a generation AI to obtain weather data in real time and reflect it in the design of the object. For example, if it is raining, a water droplet effect is added to the object. The object generation unit can also change the design of the object according to the time of day. For example, it generates bright-colored objects during the day and dark-colored objects at night. This makes it possible to reflect environmental changes in real time.

[0059] The prompt input unit can have a function that allows a prompt to be input using voice input. For example, the prompt input unit allows a user to input a prompt by voice, and the generation AI analyzes the voice to generate an object. For example, if a user gives a voice command such as "Make a red dragon," the generation AI generates a red dragon object. The prompt input unit can also convert voice prompts into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. This allows a prompt to be input using voice input.

[0060] The prompt input unit can have a function for inputting a prompt collaboratively with other users and generating an object. For example, the prompt input unit allows multiple users to input a prompt collaboratively, and the generation AI generates an object based on the prompt. For example, if user A inputs "Make a castle in the shape of a dragon" and user B inputs "Make it red," the generation AI generates a castle in the shape of a red dragon. The prompt input unit also has an interface for collaborative work. For example, it provides a chat function that allows multiple users to input prompts simultaneously, and a collaborative editing function. This allows users to input prompts collaboratively with other users and generate objects.

[0061] The prompt input unit may have a function of using an emotion estimation function to suggest a prompt according to the user's emotion. For example, the prompt input unit uses the emotion estimation function to suggest a prompt according to the user's emotion. For example, if the user expresses joy, the prompt input unit may suggest, "Why don't you try building a fun theme park?" The prompt input unit may also have a system for analyzing the user's emotion using an emotion estimation algorithm and suggesting a prompt based on the results. For example, the system may analyze the user's facial expression using facial expression recognition technology and calculate an emotion score. This allows the system to suggest a prompt according to the user's emotion.

[0062] The object generation unit can estimate the user's emotions and automatically generate in-game scenarios or events based on those emotions. For example, the object generation unit uses a generation AI to analyze the user's emotions and automatically generate in-game scenarios based on those emotions. For example, if the user expresses joy, a fun event is added. The object generation unit also includes a system for analyzing the user's emotions using an emotion estimation algorithm and generating scenarios and events based on the results. For example, the development of the scenario is determined based on the emotion score. This allows in-game scenarios and events to be dynamically generated based on the user's emotions.

[0063] The object generation unit can learn the user's play style and optimize the game design accordingly. For example, the object generation unit uses a generation AI to analyze the user's play style and optimize the game design accordingly. For example, a user who is good at action games can be provided with a design that enhances action elements. The object generation unit also has an algorithm for learning the user's play style. For example, the object generation unit can analyze the user's play style based on past play data and optimize the game design based on the results. This allows the game design to be optimized according to the user's play style.

[0064] The object generation unit can have a function to reflect real-time user feedback and dynamically change the game design. For example, the object generation unit uses a generation AI to dynamically change the game design based on real-time user feedback. For example, if the user provides feedback such as "add more enemies," the number of enemies is increased. The object generation unit also has a system for collecting real-time feedback and changing the game design based on that feedback. For example, the system analyzes the feedback and adjusts in-game elements based on the results. This allows the game design to be dynamically changed based on real-time user feedback.

[0065] The object generation unit can have a function for generating designs corresponding to different game genres. For example, the object generation unit generates objects corresponding to different game genres using a generation AI. For example, the object generation unit generates characters for RPGs and buildings for simulations. The object generation unit also has design templates for corresponding to different game genres. For example, the object generation unit uses templates for generating effects for action games and items for puzzle games. This makes it possible to generate designs corresponding to different game genres.

[0066] The object generation unit may have a function for allowing a user to share an object created by the user with other users and collaborate on game design. The object generation unit may, for example, have a function for allowing a user to share an object created by the user with other users and collaborate on game design. For example, a character created by user A is used by user B. The object generation unit may also have a system for managing shared objects. For example, the object may be saved using cloud storage and shared with other users. This allows a user to share an object created by the user with other users and collaborate on game design.

[0067] The object generation unit can be equipped with a function for proposing a game design according to the user's emotions using an emotion estimation function. The object generation unit, for example, uses the emotion estimation function to propose a game design according to the user's emotions. For example, if the user expresses joy, a fun design is proposed. The object generation unit also includes a system for analyzing the user's emotions using an emotion estimation algorithm and proposing a game design based on the results. For example, a design is proposed based on an emotion score. This makes it possible to propose a game design according to the user's emotions.

[0068] The object generation unit can estimate the user's emotions and automatically generate the behavior or dialogue of an in-game character based on those emotions. For example, the object generation unit uses a generation AI to analyze the user's emotions and automatically generate the behavior of an in-game character based on those emotions. For example, if the user expresses joy, the character will perform a celebratory dance. The object generation unit also includes a system for analyzing the user's emotions using an emotion estimation algorithm and generating character dialogue based on the results. For example, the character's lines are determined based on the emotion score. This allows the behavior and dialogue of an in-game character to be dynamically generated based on the user's emotions.

[0069] The object generation unit can provide an individually customized game experience by referencing the user's past game play data. For example, the object generation unit uses a generation AI to analyze the user's past game play data and provide an individually customized game experience. For example, it can enhance elements that the user has played with preference in the past. The object generation unit also includes a database that stores the user's game play data and customizes the game experience based on the data. For example, it can reference the user's past play history stored in the database and provide a game experience tailored to the user's preferences. This makes it possible to provide an individually customized game experience based on the user's past game play data.

[0070] The object generation unit can have a function to dynamically change the in-game environment and scenario by reflecting the user's real-time actions. For example, the object generation unit uses a generation AI to dynamically change the in-game environment based on the user's real-time actions. For example, when the user reaches a specific location, the environment of that location changes. The object generation unit also has a system for analyzing the user's actions in real time and changing the scenario based on the results. For example, the development of the scenario changes according to the user's selection. This allows the in-game environment and scenario to be dynamically changed based on the user's real-time actions.

[0071] The object generation unit can have a function to provide a gaming experience compatible with different platforms. For example, the object generation unit generates objects compatible with different platforms using a generation AI, thereby providing a gaming experience in VR or AR. For example, the object generation unit generates 3D models for VR and objects for AR. The object generation unit also has a system for supporting different platforms. For example, the object generation unit generates objects compatible with mobile devices and console game machines. This allows a gaming experience compatible with different platforms to be provided.

[0072] The object generation unit may have a function for sharing a game experience created by a user with other users and collaboratively creating a new game experience. The object generation unit, for example, has a function for sharing a game experience created by a user with other users and collaboratively creating a new game experience. For example, user B plays a quest created by user A. The object generation unit also has a system for managing the shared game experience. For example, the game experience is saved using cloud storage and shared with other users. This allows the game experience created by a user to be shared with other users and collaboratively create a new game experience.

[0073] The object generation unit may have a function of using an emotion estimation function to suggest a game experience according to the user's emotion. The object generation unit, for example, uses the emotion estimation function to suggest a game experience according to the user's emotion. For example, if the user expresses joy, the object generation unit suggests a fun game experience. The object generation unit also has a system for analyzing the user's emotion using an emotion estimation algorithm and suggesting a game experience based on the results. For example, the object generation unit suggests a game experience based on an emotion score. This makes it possible to suggest a game experience according to the user's emotion.

[0074] The object generation unit can estimate the user's emotions and automatically generate new game genres and concepts based on those emotions. For example, the object generation unit uses a generation AI to analyze the user's emotions and automatically generate a new game genre based on those emotions. For example, if the user expresses joy, the object generation unit suggests a fun genre. The object generation unit also includes a system that analyzes the user's emotions using an emotion estimation algorithm and generates a new game concept based on the results. For example, the system determines the theme and storyline of a new game based on the emotion score. This allows new game genres and concepts to be automatically generated based on the user's emotions.

[0075] The object generation unit customizes objects according to the user's region and culture, making it possible to respond to global markets. For example, the object generation unit uses a generation AI to analyze the user's region and culture and customize the object based on that data. For example, it may reflect the traditional design of a particular region. The object generation unit also includes a system for customizing objects according to region and culture. For example, it may change the design of an object based on the user's region and culture. This allows customization according to the user's region and culture to respond to global markets.

[0076] The object generation unit can have a function that reflects real-time market trends and dynamically generates new game content. For example, the object generation unit uses a generation AI to analyze real-time market trends and generate new game content based on that data. For example, it incorporates currently popular themes. The object generation unit also has a system for reflecting market trends. For example, it generates objects based on the latest game genres and popular themes. This allows new game content to be dynamically generated, reflecting real-time market trends.

[0077] The object generation unit can have a function for generating game content that can be applied to different industries. For example, the object generation unit generates game content that can be applied to different industries using a generation AI. For example, the object generation unit generates interactive educational materials. The object generation unit also has a system for application to different industries. For example, the object generation unit generates medical simulation content or interactive stories for entertainment. This makes it possible to generate game content that can be applied to different industries.

[0078] The object generation unit may have a function for sharing game content created by a user with other users and collaboratively developing new markets. The object generation unit may, for example, have a function for sharing game content created by a user with other users and collaboratively developing new markets. For example, educational content created by user A is used by user B. The object generation unit may also have a system for managing shared game content. For example, the game content may be stored using cloud storage and shared with other users. This allows the game content created by a user to be shared with other users and collaboratively developing new markets.

[0079] The object generation unit can be provided with a function for proposing new markets in accordance with the user's emotions using an emotion estimation function. The object generation unit, for example, uses the emotion estimation function to propose new markets in accordance with the user's emotions. For example, if the user expresses joy, a fun market is proposed. The object generation unit also includes a system for analyzing the user's emotions using an emotion estimation algorithm and proposing new markets based on the results. For example, a market is proposed based on an emotion score. This makes it possible to propose new markets in accordance with the user's emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The object generation unit can estimate the user's emotions and dynamically change the music and sound effects in the game based on those emotions. For example, if the user expresses nervousness, music that heightens the sense of tension can be played. Alternatively, if the user expresses joy, bright and cheerful music can be played. Furthermore, it is possible to adjust the volume and tone of sound effects according to the user's emotions. This makes it possible to provide an audio experience that matches the user's emotions.

[0082] The object generation unit can acquire the user's health data and dynamically adjust the in-game difficulty level based on that data. For example, if the user's heart rate is high, the difficulty level of the game can be lowered to reduce stress. Also, if the user is highly fatigued, the game progress can be slowed down. Furthermore, it is possible to change in-game activities according to the user's health state. This makes it possible to provide a gaming experience that is tailored to the user's health state.

[0083] The object generation unit can estimate the user's emotions and dynamically change the facial expressions and behavior of the in-game characters based on those emotions. For example, if the user expresses sadness, the character can show a comforting expression. Alternatively, if the user expresses surprise, the character can show a surprised expression. Furthermore, it is also possible to change the behavior of the character according to the user's emotions. This makes it possible to provide the character's reaction according to the user's emotions.

[0084] The object generation unit can acquire the user's geographical location information and customize the in-game environment based on that information. For example, if the user is in a mountainous area, the in-game environment can be changed to a mountainous landscape. Alternatively, if the user is in an urban area, the in-game environment can be changed to reflect an urban landscape. Furthermore, it is possible to provide specific events or quests based on the user's location information. This makes it possible to provide a game experience tailored to the user's geographical location.

[0085] The object generation unit can estimate the user's emotions and dynamically change the weather and time of day in the game based on those emotions. For example, if the user expresses a relaxed emotion, it can set the weather to a sunny daytime. If the user expresses an excited emotion, it can set the weather to a stormy night. Furthermore, it can adjust the weather effects and light intensity according to the user's emotions. This makes it possible to provide an environmental setting that corresponds to the user's emotions.

[0086] The object generation unit can analyze the user's past game play data and suggest new game modes based on that data. For example, if the user has played many action games in the past, a mode with enhanced action elements can be suggested. Also, if the user likes puzzle games, a mode incorporating puzzle elements can be suggested. Furthermore, it is possible to provide modes customized according to the user's play style. This makes it possible to suggest new game modes based on the user's past play data.

[0087] The object generation unit can estimate the user's emotions and dynamically change the in-game reward system based on the emotions. For example, if the user expresses a sense of accomplishment, a special reward can be provided. If the user expresses a sense of frustration, an additional reward can be provided along with an encouraging message. Furthermore, it is possible to adjust the type and amount of reward according to the user's emotions. This makes it possible to provide a reward system that responds to the user's emotions.

[0088] The object generation unit can analyze the user's real-time behavioral data and dynamically adjust the AI ​​of enemies in the game based on that data. For example, if the user frequently uses a particular tactic, the enemy AI will take actions that counter that tactic. Alternatively, if the user has a cautious playing style, the enemy AI can become more aggressive. Furthermore, it is possible to adjust the placement and number of enemies based on the user's behavioral data. This makes it possible to provide enemy AI that responds to the user's actions.

[0089] The object generation unit can estimate the user's emotions and dynamically change the in-game story development based on those emotions. For example, if the user expresses excitement, an action scene can be added. If the user expresses sadness, an emotional scene can be added. Furthermore, it is possible to change the branching and ending of the story depending on the user's emotions. This makes it possible to provide a story development that matches the user's emotions.

[0090] The object generation unit can dynamically adjust the difficulty of puzzles and quests in the game based on real-time user feedback. For example, if the user provides feedback such as "this puzzle is too difficult," the difficulty of the puzzle can be lowered. Alternatively, if the user provides feedback such as "I want a more challenging quest," the difficulty of the quest can be increased. Furthermore, new puzzles and quests can be added based on user feedback. This makes it possible to provide a game experience that responds to the user's real-time feedback.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The prompt input unit inputs a prompt from the user. For example, the prompt input unit may include a keyboard for inputting a prompt in text format or a microphone for inputting a prompt in audio format. Step 2: The object generation unit generates an object based on the prompt input by the prompt input unit. For example, the object generated by the generation AI can be placed in the game and the design of the object can be fine-tuned. Step 3: The generation AI generates objects based on prompts from the user. For example, if a user inputs a prompt such as "Make a castle in the shape of a dragon," the AI ​​analyzes the instruction and generates a 3D model of a castle in the shape of a dragon. Similarly, if a user inputs a prompt such as "A giant tree floating in space," the AI ​​can generate an object based on the instruction and place it in the game.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 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. Generative AI and a prompt input unit for inputting a prompt from a user; an object generation unit that generates an object based on the prompt input by the prompt input unit; A system characterized by:

2. The object generation unit Estimating the user's emotion and fine-tuning the design of the object based on the emotion.

2. The system of claim 1.

3. The prompt input unit Provides the ability to enter prompts using voice input 2. The system of claim 1.

4. The object generation unit Estimating the user's emotions and automatically generating in-game scenarios or events based on the emotions.

2. The system of claim 1.

5. The object generation unit Estimating the user's emotions and automatically generating the behavior or dialogue of a character in the game based on the emotions.

2. The system of claim 1.

6. The object generation unit The emotions of the user are estimated, and new game genres and concepts are automatically generated based on those emotions.

2. The system of claim 1.

7. The prompt input unit It has the ability to suggest prompts based on the user's emotions.

2. The system of claim 1.

8. The object generation unit The system has a function to propose game designs according to the user's emotions.

2. The system of claim 1.

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