System

Abstract

An object of the system according to the embodiment is to make the work of game development efficient and to generate a story according to the action of a player.SOLUTION: A system includes an operation efficiency improvement part, a content generation part, and a story generation part. The work efficiency improvement unit improves the efficiency of game development work. The content generation unit generates content in a game. The story generation unit generates a story according to the action of the player.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately improve the efficiency of game development or generate stories based on player behavior, so there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of game development work and generate a story that corresponds to the actions of the player. [Means for solving the problem]

[0006] The system according to the embodiment includes a business efficiency improvement unit, a content generation unit, and a story generation unit. The business efficiency improvement unit improves the efficiency of game development work. The content generation unit generates in-game content. The story generation unit generates a story based on the player's actions. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of game development work and generate a story that corresponds to the actions of the player. [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 creation system according to an embodiment of the present invention is a system for creating immersive games by combining AI technology and generative AI technology to improve business efficiency and creativity. As a result, the game creation system utilizes AI technology and generative AI technology to improve business efficiency and creativity, and to provide players with a highly immersive experience.

[0029] A game development system according to an embodiment includes a business efficiency improvement unit, a content generation unit, and a story generation unit. The business efficiency improvement unit improves the efficiency of game development operations. For example, the business efficiency improvement unit automatically generates program code using AI. The business efficiency improvement unit can also detect bugs using AI. The business efficiency improvement unit can also perform data analysis using AI to analyze player behavior patterns. For example, the business efficiency improvement unit can analyze player behavior patterns using AI to optimize game balance adjustments and difficulty settings. The content generation unit generates in-game content. For example, the content generation unit generates characters using a generation AI. The content generation unit can also generate quests using the generation AI. The content generation unit can also generate dialogue using the generation AI. For example, the content generation unit receives a prompt such as "Adventure in a medieval fantasy world" and generates characters, quests, and dialogue based on the prompt. The story generation unit generates a story based on the player's actions. For example, the story generation unit uses the generation AI to branch the story based on the player's choices. The story generation unit can also use the generation AI to generate different endings based on the player's choices. The story generation unit can also use generation AI to generate a new story based on a player's selection. For example, if the player makes a specific selection, the story generation unit can branch the story and lead to a different ending depending on the selection. This allows the game creation system according to the embodiment to utilize AI technology and generation AI technology to improve operational efficiency and creativity, and provide a highly immersive experience for the player.

[0030] The business efficiency improvement unit can learn a developer's coding style and provide them with optimized code completion. For example, the business efficiency improvement unit uses AI to analyze a developer's past code and learn specific coding patterns and styles. For example, it learns specific variable naming conventions and function writing methods, and automatically completes them when the developer writes code. The business efficiency improvement unit also uses AI to suggest code completion in real time as the developer writes code. For example, it automatically completes function arguments and return value types, reducing the developer's workload. The business efficiency improvement unit also uses AI to provide optimal code completion based on the developer's coding style. For example, it suggests appropriate methods and classes when using specific libraries or frameworks. This can improve the developer's coding efficiency.

[0031] The Business Efficiency Department automates playtesting of games under development, enabling the detection and correction of bugs and defects in real time. For example, the Business Efficiency Department uses AI to automatically playtest games and detect bugs and defects. For example, AI simulates player behavior and identifies bugs that occur under specific conditions. The Business Efficiency Department also uses AI to playtest games in real time and immediately fix any bugs or defects it detects. For example, it automatically fixes bugs that occur in specific scenarios and notifies the developer. The Business Efficiency Department also uses AI to analyze the results of playtests and identify the causes of bugs and defects. For example, it identifies bugs that occur due to specific code blocks or functions and proposes fixes. This enables the rapid detection and correction of bugs and defects during development.

[0032] The content generation unit can learn the player's past play history and generate content optimized for the player. For example, the content generation unit uses a generation AI to analyze the player's past play history and generate optimal content based on the player's preferences and play style. For example, it generates specific quests and characters to suit the player's preferences. The content generation unit also learns the player's past play history and automatically generates content that the player can enjoy. For example, it generates new content based on scenarios and events that the player liked to play in the past. The content generation unit also uses a generation AI to generate an optimal story development for the player based on the player's play history. For example, it generates a new story based on the choices and actions the player made in the past. This makes it possible to provide content optimized for the player.

[0033] The content generation unit can analyze the player's actions in real time and generate new quests and events on the spot. In the content generation unit, for example, the generation AI analyzes the player's actions in real time and generates new quests on the spot. For example, when the player reaches a specific location, a new quest is automatically generated. The content generation unit also analyzes the player's actions and generates new events on the spot. For example, when the player obtains a specific item, a new event is automatically generated. In addition, the content generation unit analyzes the player's actions in real time and generates new story developments on the spot. For example, when the player makes a specific choice, a new story branch is automatically generated. This makes it possible to generate new quests and events in real time in response to the player's actions.

[0034] The story generation unit can analyze the player's selection history and predict and present the next choice. In the story generation unit, for example, the generation AI analyzes the player's past selection history and predicts and presents the choice that is likely to be selected next. For example, the generation AI learns the patterns of choices the player has made in the past and suggests the next choice. In addition, the story generation unit predicts the next choice based on the player's selection history and presents it to the player. For example, it prioritizes presenting choices that the player has preferred in the past. In addition, the story generation unit builds a system in which the generation AI analyzes the player's selection history and predicts and presents the next choice. For example, it learns the player's selection patterns and automatically generates the next choice. This makes it possible to predict and present the next choice based on the player's selection history.

[0035] The story generation unit can learn the player's behavioral patterns and generate a story in advance based on predicted behavior. For example, the generation AI of the story generation unit learns the player's behavioral patterns and generates a story in advance based on predicted behavior. For example, before the player takes a specific action, a story development corresponding to that action is generated. The story generation unit also builds a system that learns the player's behavioral patterns and generates a story in advance based on predicted behavior. For example, before the player heads to a specific location, it generates an event at that location. The story generation unit also analyzes the player's behavioral patterns and generates a story in advance based on predicted behavior. For example, before the player obtains a specific item, it generates a story related to that item. This makes it possible to generate a story in advance based on the player's behavioral patterns.

[0036] The story generation unit can dynamically change the in-game environment and character behavior in response to the player's choices. In the story generation unit, for example, the generation AI dynamically changes the in-game environment in response to the player's choices. For example, if the player makes a specific choice, the weather and time of day change in response to that choice. In addition, the story generation unit dynamically changes the character behavior in response to the player's choices. For example, if the player builds a friendly relationship with a specific character, that character will take an action to help the player. In addition, the story generation unit constructs a system in which the generation AI dynamically changes the in-game environment and character behavior based on the player's choices. For example, the placement and behavior patterns of enemy characters change in response to the player's choices. This makes it possible to dynamically change the in-game environment and character behavior in response to the player's choices.

[0037] The story generation unit can generate different endings and side quests based on the player's choices. In the story generation unit, for example, the generation AI generates different endings based on the player's choices. For example, if the player makes a specific choice, it generates an ending that corresponds to that choice. In addition, the story generation unit generates new side quests based on the player's choices. For example, if the player talks to a specific character, it generates a side quest related to that character. In addition, the story generation unit builds a system in which the generation AI generates different endings and side quests based on the player's choices. For example, it generates branching points in the story and adds new quests in response to the player's choices. This makes it possible to generate different endings and side quests based on the player's choices.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The game creation system may further include a voice recognition unit. The voice recognition unit can analyze the developer's voice commands and automatically execute specific operations. For example, if a developer issues a voice command such as "generate a character," the voice recognition unit analyzes the command and instructs the content generation unit to generate a character. Furthermore, if the developer instructs the voice recognition unit to "detect bugs," the voice recognition unit can instruct the business efficiency unit to detect bugs. Furthermore, if the developer instructs the voice recognition unit to "branch the story," the voice recognition unit can instruct the story generation unit to branch the story. This allows developers to efficiently develop games using voice commands.

[0040] The business efficiency improvement department can further include a project management department. The project management department can monitor the progress of development projects in real time and automatically assign tasks and report progress. For example, the project management department can analyze the task progress of each developer and identify tasks that are delayed. The project management department can also send reminders to developers based on the task progress. Furthermore, the project management department can visualize the overall progress of development projects and report it to the project manager. This makes progress management of development projects more efficient and enables smooth project operation.

[0041] The business efficiency improvement department can further include a resource optimization department. The resource optimization department can analyze the resource usage status in a development project and propose optimal resource allocation. For example, the resource optimization department can analyze the workload of developers and propose task reallocation for overloaded developers. The resource optimization department can also analyze the usage status of hardware and software in a development project and propose optimal resource allocation. Furthermore, the resource optimization department can propose adding or reducing resources based on the progress of the development project. This realizes resource optimization in a development project and enables efficient project management.

[0042] The content generation unit may further include an environment generation unit. The environment generation unit may dynamically generate an in-game environment in response to the player's actions and selections. For example, when the player reaches a specific location, the environment generation unit may automatically generate the environment of that location. The environment generation unit may also dynamically change the weather and time of day in response to the player's selections. Furthermore, the environment generation unit may change the in-game environment in real time based on the player's actions. This allows for dynamic environment generation in response to the player's actions and selections, providing a more immersive gaming experience.

[0043] The story generation unit may further include a scenario prediction unit. The scenario prediction unit may analyze the player's selection history and behavioral patterns, and predict and present a scenario that is likely to be selected next. For example, the scenario prediction unit may learn patterns of options previously selected by the player, and suggest a scenario that is likely to be selected next. The scenario prediction unit may also predict a scenario that is likely to be selected next based on the player's behavioral patterns, and present this to the player. Furthermore, the scenario prediction unit may analyze the player's selection history, and automatically generate a scenario that is likely to be selected next. This allows for scenario prediction based on the player's selection history and behavioral patterns, providing a smoother game progression.

[0044] The story generation unit may further include a multiple ending generation unit. The multiple ending generation unit can dynamically generate multiple endings according to the player's choices and actions. For example, if the player makes a specific choice, the multiple ending generation unit automatically generates an ending according to that choice. The multiple ending generation unit can also generate different endings based on the player's behavioral patterns. Furthermore, the multiple ending generation unit can change the content of the ending in real time according to the player's choices and actions. This provides multiple endings according to the player's choices and actions, resulting in a more diverse game experience.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The Business Efficiency Department improves the efficiency of game development operations. For example, the Business Efficiency Department uses AI to automatically generate program code. The Business Efficiency Department can also use AI to detect bugs. The Business Efficiency Department can also use AI to analyze data and analyze player behavior patterns. For example, the Business Efficiency Department can use AI to analyze player behavior patterns and optimize game balance adjustments and difficulty settings. Step 2: The content generation unit generates content for the game. For example, the content generation unit generates characters using a generation AI. The content generation unit can also generate quests using the generation AI. The content generation unit can also generate dialogue using the generation AI. For example, the content generation unit generates characters, quests, and dialogue based on the prompt that the generation AI receives: "Adventure in a medieval fantasy world." Step 3: The story generation unit generates a story based on the player's actions. For example, the story generation unit uses a generation AI to branch the story based on the player's choices. The story generation unit can also use the generation AI to generate different endings based on the player's choices. The story generation unit can also use the generation AI to generate a new story based on the player's choices. For example, if the player makes a specific choice, the story generation unit causes the story to branch depending on that choice, leading to a different ending.

[0047] (Example 2) A game creation system according to an embodiment of the present invention is a system for creating immersive games by combining AI technology and generative AI technology to improve business efficiency and creativity. As a result, the game creation system utilizes AI technology and generative AI technology to improve business efficiency and creativity, and to provide players with a highly immersive experience.

[0048] A game development system according to an embodiment includes a business efficiency improvement unit, a content generation unit, and a story generation unit. The business efficiency improvement unit improves the efficiency of game development operations. For example, the business efficiency improvement unit automatically generates program code using AI. The business efficiency improvement unit can also detect bugs using AI. The business efficiency improvement unit can also perform data analysis using AI to analyze player behavior patterns. For example, the business efficiency improvement unit can analyze player behavior patterns using AI to optimize game balance adjustments and difficulty settings. The content generation unit generates in-game content. For example, the content generation unit generates characters using a generation AI. The content generation unit can also generate quests using the generation AI. The content generation unit can also generate dialogue using the generation AI. For example, the content generation unit receives a prompt such as "Adventure in a medieval fantasy world" and generates characters, quests, and dialogue based on the prompt. The story generation unit generates a story based on the player's actions. For example, the story generation unit uses the generation AI to branch the story based on the player's choices. The story generation unit can also use the generation AI to generate different endings based on the player's choices. The story generation unit can also use generation AI to generate a new story based on a player's selection. For example, if the player makes a specific selection, the story generation unit can branch the story and lead to a different ending depending on the selection. This allows the game creation system according to the embodiment to utilize AI technology and generation AI technology to improve operational efficiency and creativity, and provide a highly immersive experience for the player.

[0049] The business efficiency improvement unit can learn a developer's coding style and provide them with optimized code completion. For example, the business efficiency improvement unit uses AI to analyze a developer's past code and learn specific coding patterns and styles. For example, it learns specific variable naming conventions and function writing methods, and automatically completes them when the developer writes code. The business efficiency improvement unit also uses AI to suggest code completion in real time as the developer writes code. For example, it automatically completes function arguments and return value types, reducing the developer's workload. The business efficiency improvement unit also uses AI to provide optimal code completion based on the developer's coding style. For example, it suggests appropriate methods and classes when using specific libraries or frameworks. This can improve the developer's coding efficiency.

[0050] The Business Efficiency Department automates playtesting of games under development, enabling the detection and correction of bugs and defects in real time. For example, the Business Efficiency Department uses AI to automatically playtest games and detect bugs and defects. For example, AI simulates player behavior and identifies bugs that occur under specific conditions. The Business Efficiency Department also uses AI to playtest games in real time and immediately fix any bugs or defects it detects. For example, it automatically fixes bugs that occur in specific scenarios and notifies the developer. The Business Efficiency Department also uses AI to analyze the results of playtests and identify the causes of bugs and defects. For example, it identifies bugs that occur due to specific code blocks or functions and proposes fixes. This enables the rapid detection and correction of bugs and defects during development.

[0051] The work efficiency improvement unit can use the emotion estimation function to monitor developers' stress levels and suggest breaks at appropriate times. For example, the work efficiency improvement unit uses AI to analyze developers' facial expressions and voices and monitor stress levels in real time. For example, it can analyze a developer's emotional state using a camera or microphone and suggest breaks when stress increases. The work efficiency improvement unit also uses the emotion estimation function to quantify a developer's stress level and suggest breaks at appropriate times. For example, it can notify the developer to take a break when a certain stress score is exceeded. The work efficiency improvement unit also uses AI to monitor developers' stress levels and suggest relaxation methods when stress increases. For example, it can suggest relaxation methods such as deep breathing and stretching. This reduces developers' stress and promotes efficient work.

[0052] The content generation unit can learn the player's past play history and generate content optimized for the player. For example, the content generation unit uses a generation AI to analyze the player's past play history and generate optimal content based on the player's preferences and play style. For example, it generates specific quests and characters to suit the player's preferences. The content generation unit also learns the player's past play history and automatically generates content that the player can enjoy. For example, it generates new content based on scenarios and events that the player liked to play in the past. The content generation unit also uses a generation AI to generate an optimal story development for the player based on the player's play history. For example, it generates a new story based on the choices and actions the player made in the past. This makes it possible to provide content optimized for the player.

[0053] The content generation unit can analyze the player's actions in real time and generate new quests and events on the spot. In the content generation unit, for example, the generation AI analyzes the player's actions in real time and generates new quests on the spot. For example, when the player reaches a specific location, a new quest is automatically generated. The content generation unit also analyzes the player's actions and generates new events on the spot. For example, when the player obtains a specific item, a new event is automatically generated. In addition, the content generation unit analyzes the player's actions in real time and generates new story developments on the spot. For example, when the player makes a specific choice, a new story branch is automatically generated. This makes it possible to generate new quests and events in real time in response to the player's actions.

[0054] The content generation unit can use the emotion estimation function to generate story developments and character reactions according to the player's emotional state. For example, the content generation unit uses a generation AI to analyze the player's emotional state in real time and generate story developments according to that emotion. For example, if the player is excited, the content generation unit increases the number of action scenes. The content generation unit also uses the emotion estimation function to generate character reactions according to the player's emotional state. For example, if the player is sad, the character will say comforting lines. The content generation unit also uses a generation AI to analyze the player's emotional state and generate quests and events according to that emotion. For example, if the player is bored, an exciting quest will be added. This makes it possible to generate story developments and character reactions according to the player's emotional state.

[0055] The story generation unit can analyze the player's selection history and predict and present the next choice. In the story generation unit, for example, the generation AI analyzes the player's past selection history and predicts and presents the choice that is likely to be selected next. For example, the generation AI learns the patterns of choices the player has made in the past and suggests the next choice. In addition, the story generation unit predicts the next choice based on the player's selection history and presents it to the player. For example, it prioritizes presenting choices that the player has preferred in the past. In addition, the story generation unit builds a system in which the generation AI analyzes the player's selection history and predicts and presents the next choice. For example, it learns the player's selection patterns and automatically generates the next choice. This makes it possible to predict and present the next choice based on the player's selection history.

[0056] The story generation unit can learn the player's behavioral patterns and generate a story in advance based on predicted behavior. For example, the generation AI of the story generation unit learns the player's behavioral patterns and generates a story in advance based on predicted behavior. For example, before the player takes a specific action, a story development corresponding to that action is generated. The story generation unit also builds a system that learns the player's behavioral patterns and generates a story in advance based on predicted behavior. For example, before the player heads to a specific location, it generates an event at that location. The story generation unit also analyzes the player's behavioral patterns and generates a story in advance based on predicted behavior. For example, before the player obtains a specific item, it generates a story related to that item. This makes it possible to generate a story in advance based on the player's behavioral patterns.

[0057] The story generation unit can use the emotion estimation function to generate story branches in real time according to the player's emotions. For example, the generation AI in the story generation unit analyzes the player's emotional state in real time and generates story branches according to those emotions. For example, if the player is excited, the number of action scenes is increased. The story generation unit also uses the emotion estimation function to generate story branches in real time according to the player's emotional state. For example, if the player is sad, an emotional scene is added. The story generation unit also builds a system in which the generation AI analyzes the player's emotional state and generates story branches in real time according to those emotions. For example, if the player is bored, an exciting event is added. In this way, story branches can be generated in real time according to the player's emotions.

[0058] The story generation unit can dynamically change the in-game environment and character behavior in response to the player's choices. In the story generation unit, for example, the generation AI dynamically changes the in-game environment in response to the player's choices. For example, if the player makes a specific choice, the weather and time of day change in response to that choice. In addition, the story generation unit dynamically changes the character behavior in response to the player's choices. For example, if the player builds a friendly relationship with a specific character, that character will take an action to help the player. In addition, the story generation unit constructs a system in which the generation AI dynamically changes the in-game environment and character behavior based on the player's choices. For example, the placement and behavior patterns of enemy characters change in response to the player's choices. This makes it possible to dynamically change the in-game environment and character behavior in response to the player's choices.

[0059] The story generation unit can generate different endings and side quests based on the player's choices. In the story generation unit, for example, the generation AI generates different endings based on the player's choices. For example, if the player makes a specific choice, it generates an ending that corresponds to that choice. In addition, the story generation unit generates new side quests based on the player's choices. For example, if the player talks to a specific character, it generates a side quest related to that character. In addition, the story generation unit builds a system in which the generation AI generates different endings and side quests based on the player's choices. For example, it generates branching points in the story and adds new quests in response to the player's choices. This makes it possible to generate different endings and side quests based on the player's choices.

[0060] The story generation unit can use the emotion estimation function to identify characters and scenarios with which the player can most easily empathize and generate a story development based on that. For example, the story generation unit uses the generation AI to analyze the player's emotional state and identify characters with which the player can most easily empathize. For example, if the player has strong feelings toward a particular character, the story development will center around that character. The story generation unit also uses the emotion estimation function to identify scenarios with which the player can most easily empathize and generate a story development based on those scenarios. For example, it will strengthen scenarios that move the player. The story generation unit also builds a system in which the generation AI analyzes the player's emotional state and identifies characters and scenarios with which the player can most easily empathize. For example, it generates a story development that is easy to empathize with based on the player's emotional reaction. This makes it possible to generate a story development based on characters and scenarios with which the player can most easily empathize.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The game creation system may further include a voice recognition unit. The voice recognition unit can analyze the developer's voice commands and automatically execute specific operations. For example, if a developer issues a voice command such as "generate a character," the voice recognition unit analyzes the command and instructs the content generation unit to generate a character. Furthermore, if the developer instructs the voice recognition unit to "detect bugs," the voice recognition unit can instruct the business efficiency unit to detect bugs. Furthermore, if the developer instructs the voice recognition unit to "branch the story," the voice recognition unit can instruct the story generation unit to branch the story. This allows developers to efficiently develop games using voice commands.

[0063] The business efficiency improvement department can further include a project management department. The project management department can monitor the progress of development projects in real time and automatically assign tasks and report progress. For example, the project management department can analyze the task progress of each developer and identify tasks that are delayed. The project management department can also send reminders to developers based on the task progress. Furthermore, the project management department can visualize the overall progress of development projects and report it to the project manager. This makes progress management of development projects more efficient and enables smooth project operation.

[0064] The business efficiency improvement department can further include a resource optimization department. The resource optimization department can analyze the resource usage status in a development project and propose optimal resource allocation. For example, the resource optimization department can analyze the workload of developers and propose task reallocation for overloaded developers. The resource optimization department can also analyze the usage status of hardware and software in a development project and propose optimal resource allocation. Furthermore, the resource optimization department can propose adding or reducing resources based on the progress of the development project. This realizes resource optimization in a development project and enables efficient project management.

[0065] The work efficiency improvement unit can further include a health management unit. The health management unit can monitor the health status of developers and suggest appropriate health management. For example, the health management unit can analyze the developer's heart rate and sleep patterns to monitor the health status in real time. The health management unit can also suggest appropriate breaks and exercise based on the developer's health status. Furthermore, the health management unit can analyze the developer's health status and suggest relaxation methods when stress levels rise. This makes developer health management more efficient and creates a healthy work environment.

[0066] The content generation unit may further include an environment generation unit. The environment generation unit may dynamically generate an in-game environment in response to the player's actions and selections. For example, when the player reaches a specific location, the environment generation unit may automatically generate the environment of that location. The environment generation unit may also dynamically change the weather and time of day in response to the player's selections. Furthermore, the environment generation unit may change the in-game environment in real time based on the player's actions. This allows for dynamic environment generation in response to the player's actions and selections, providing a more immersive gaming experience.

[0067] The content generation unit may further include a music generation unit. The music generation unit may dynamically generate in-game music according to the player's actions and emotional state. For example, when the player reaches a specific location, the music generation unit may automatically generate music appropriate for that location. The music generation unit may also change the tempo and atmosphere of the music according to the player's emotional state. Furthermore, the music generation unit may change the in-game music in real time based on the player's actions. This allows for dynamic music generation according to the player's actions and emotional state, providing a more immersive gaming experience.

[0068] The content generation unit may further include a character emotion analysis unit. The character emotion analysis unit can dynamically change the emotions of the in-game character according to the player's emotional state. For example, if the player is excited, the character may also have an excited expression and behavior. Also, if the player is sad, the character may say comforting words. Furthermore, the character emotion analysis unit can change the character's emotions in real time based on the player's emotional state. This allows the character's emotions to change according to the player's emotional state, providing a more empathetic gaming experience.

[0069] The story generation unit may further include a scenario prediction unit. The scenario prediction unit may analyze the player's selection history and behavioral patterns, and predict and present a scenario that is likely to be selected next. For example, the scenario prediction unit may learn patterns of options previously selected by the player, and suggest a scenario that is likely to be selected next. The scenario prediction unit may also predict a scenario that is likely to be selected next based on the player's behavioral patterns, and present this to the player. Furthermore, the scenario prediction unit may analyze the player's selection history, and automatically generate a scenario that is likely to be selected next. This allows for scenario prediction based on the player's selection history and behavioral patterns, providing a smoother game progression.

[0070] The story generation unit may further include an emotional feedback unit. The emotional feedback unit may analyze the emotional state of the player and provide feedback in real time according to the player's emotion. For example, the emotional feedback unit may provide positive feedback if the player is excited. Alternatively, the emotional feedback unit may provide comforting feedback if the player is sad. Furthermore, the emotional feedback unit may change the content of the feedback in real time based on the player's emotional state. This provides feedback according to the player's emotional state, resulting in a more empathetic gaming experience.

[0071] The story generation unit may further include a multiple ending generation unit. The multiple ending generation unit can dynamically generate multiple endings according to the player's choices and actions. For example, if the player makes a specific choice, the multiple ending generation unit automatically generates an ending according to that choice. The multiple ending generation unit can also generate different endings based on the player's behavioral patterns. Furthermore, the multiple ending generation unit can change the content of the ending in real time according to the player's choices and actions. This provides multiple endings according to the player's choices and actions, resulting in a more diverse game experience.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The Business Efficiency Department improves the efficiency of game development operations. For example, the Business Efficiency Department uses AI to automatically generate program code. The Business Efficiency Department can also use AI to detect bugs. The Business Efficiency Department can also use AI to analyze data and analyze player behavior patterns. For example, the Business Efficiency Department can use AI to analyze player behavior patterns and optimize game balance adjustments and difficulty settings. Step 2: The content generation unit generates content for the game. For example, the content generation unit generates characters using a generation AI. The content generation unit can also generate quests using the generation AI. The content generation unit can also generate dialogue using the generation AI. For example, the content generation unit generates characters, quests, and dialogue based on the prompt that the generation AI receives: "Adventure in a medieval fantasy world." Step 3: The story generation unit generates a story based on the player's actions. For example, the story generation unit uses a generation AI to branch the story based on the player's choices. The story generation unit can also use the generation AI to generate different endings based on the player's choices. The story generation unit can also use the generation AI to generate a new story based on the player's choices. For example, if the player makes a specific choice, the story generation unit causes the story to branch depending on that choice, leading to a different ending.

[0074] 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.

[0075] 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 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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 AI 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] 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.

[0101] 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.

[0102] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] 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 AI 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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, in order to avoid confusion and to 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.

[0140] 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]

[0141] 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. The Business Efficiency Department, which streamlines game development operations, a content generation unit that generates in-game content; A story generation unit that generates a story according to the player's actions. A system characterized by:

2. The business efficiency department Learns the developer's coding style and provides optimized code completion for that developer 2. The system of claim 1.

3. The business efficiency department Automate playtesting of games in development to detect and fix bugs and glitches in real time 2. The system of claim 1.

4. The business efficiency department Monitor developers' stress levels and suggest breaks at appropriate times 2. The system of claim 1.

5. The content generation unit Learning the past playing history of the player and generating the content optimized for the player 2. The system of claim 1.

6. The content generation unit Analyze the player's actions in real time and generate new quests and events on the fly 2. The system of claim 1.

7. The content generation unit Generate story developments and character reactions according to the player's emotional state 2. The system of claim 1.

8. The story generation unit Analyze the player's selection history and predict and present the next choice.

2. The system of claim 1.

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