System

Abstract

An object of the system according to the embodiment is to compete with a AI that imitates a professional play style.SOLUTION: A system according to an embodiment includes a play AI collecting unit, an analyzing unit, a copy data generating unit, and a matching unit. The play data collection section collects professional play data. The analysis section analyzes the professional play data collected by the play data collection section. The copy AI generation unit generates copy AI imitating a professional play style on the basis of the information analyzed by the analysis unit. The competition unit allows the user to compete against the copy AI generated by the copy AI generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to play against AI that imitated the playing style of professionals, which limited opportunities to learn professional techniques.

[0005] The system according to the embodiment aims to play against an AI that imitates the playing style of a professional. [Means for solving the problem]

[0006] The system according to the embodiment includes a play data collection unit, an analysis unit, a copy AI generation unit, and a competition unit. The play data collection unit collects professional play data. The analysis unit analyzes the professional play data collected by the play data collection unit. The copy AI generation unit generates a copy AI that imitates the professional's playing style based on the data analyzed by the analysis unit. The competition unit allows a user to compete against the copy AI generated by the copy AI generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can play against AI that mimics the playing style of a professional. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The professional competition system according to an embodiment of the present invention generates copy AI that imitates the playing style of professionals in various games such as Go, Shogi, Chess, and card games, allowing anyone to easily play against professionals or practice against them. This allows users to play against professionals from the comfort of their own homes, which can be useful for improving skills and studying strategies.

[0029] A professional competition system according to an embodiment includes a play data collection unit, an analysis unit, a copy AI generation unit, and a competition unit. The play data collection unit collects professional play data. For example, it collects game data from professional Go players, game records from professional Shogi players, game records from professional chess players, and video clips of professional card game players. The play data collection unit can also collect biometric data (e.g., heart rate and brain waves) from professional games. The analysis unit analyzes the professional play data collected by the play data collection unit. For example, the generation AI analyzes the professional's playing style and strategy using data mining techniques, statistical analysis, and machine learning algorithms. The copy AI generation unit generates a copy AI that mimics the professional's playing style based on the data analyzed by the analysis unit. For example, the generation AI faithfully reproduces the professional's playing style using techniques such as deep learning and reinforcement learning. The competition unit allows a user to compete against the copy AI generated by the copy AI generation unit. For example, a user can play against an AI with a professional's playing style via their home computer or smartphone. This allows users to play against professionals from the comfort of their own homes, helping them improve their skills and research strategies.

[0030] The play data collection unit can collect biometric data, including heart rate and brain waves, from professional players during games. The generation AI can reproduce professional playing styles by analyzing the biometric data. For example, the play data collection unit monitors heart rate and brain waves in real time during professional games and collects the data. The generation AI analyzes this biometric data to learn the professional's psychological state and level of concentration. It also analyzes the biometric data during games to model the professional's psychological reactions at specific stages. For example, it identifies situations where players become tense or relaxed and reproduces a playing style based on that. It also uses the biometric data to analyze changes in the professional's playing style. For example, it learns whether to play an offensive or defensive move based on fluctuations in heart rate and reflects this in the generation AI. In this way, analyzing the biometric data allows for a more realistic playing style to be reproduced.

[0031] The play data collection unit can collect post-game interviews and comments from professional players. By analyzing the interviews and comments, the generation AI can understand the professional's strategy and thought process. For example, the play data collection unit collects post-game interviews from professional players as text data, and the generation AI analyzes the content. For example, it learns in detail the strategy and thought process during the game. It also collects the professional's comments as audio data and converts them into text using speech recognition technology. The generation AI analyzes the text data to understand the professional's strategy and thought process. It also analyzes post-game interviews and comments to learn the professional's intentions and decision-making criteria at specific situations. For example, it analyzes why a move was chosen and what strategy the professional was considering. This allows for a deeper understanding of the professional's strategy and thought process by analyzing the interviews and comments.

[0032] The play data collection unit can also collect amateur play data. The generation AI can learn different levels of play styles based on the amateur play data. For example, the play data collection unit collects amateur game data, and the generation AI analyzes that data. This allows the generation AI to learn the differences between professional and amateur play styles and reproduce play styles at different levels. The generation AI can also analyze amateur play data to learn what strategies players of a particular level use. For example, it can model the play styles of beginners, intermediate players, and advanced players. The generation AI can also learn different levels of play styles based on the amateur play data and provide opponents appropriate to the user's level. For example, it can provide a beginner-level AI to beginners and an advanced-level AI to advanced players. In this way, by collecting amateur play data, it is possible to learn different levels of play styles.

[0033] The play data collection unit can simultaneously analyze data from different games. The generation AI can extract common strategies and thinking patterns based on data from different games. For example, the play data collection unit can simultaneously analyze game data from Go and Shogi to extract common strategies and thinking patterns. For example, it learns the balance between offense and defense in both games. It also analyzes data from different games to model common thinking patterns. For example, it extracts common points in strategic thinking based on data from Go and chess. It also analyzes data from different games simultaneously, and the generation AI learns common strategies for each game. For example, it learns risk management methods based on data from Go and card games. This makes it possible to extract common strategies and thinking patterns by analyzing data from different games.

[0034] The generative AI can learn the evolution of professional playing styles. The generative AI can reproduce changes from the past to the present. For example, the generative AI collects professional playing data by year and analyzes that data. This allows it to learn the evolution of professional playing styles and reproduce changes from the past to the present. It also analyzes the changes in professional playing styles and models changes in strategy at specific times. For example, it learns how strategies have evolved based on past data. The generative AI also learns the evolution of professional playing styles and reproduces those changes. For example, it reproduces changes in professional playing styles based on data from past games. This allows it to learn the evolution of professional playing styles and reproduce changes from the past to the present.

[0035] The generation AI can learn the weaknesses and mistakes of professional players' playing styles. The generation AI can generate a playing style that improves on those weaknesses and mistakes. For example, the generation AI analyzes professional game data and learns the weaknesses and mistakes in specific situations. Based on that data, the generation AI generates a playing style that improves on those weaknesses. The generation AI can also analyze the weaknesses of professional playing styles and learn strategies to improve them. For example, it can learn moves to avoid mistakes in specific situations. The generation AI can also generate a playing style that improves on those weaknesses and mistakes based on professional game data. For example, it can analyze past mistakes and learn moves to avoid them. In this way, the generation AI can learn the weaknesses and mistakes of professional playing styles and generate a playing style that improves on them.

[0036] The generation AI can generate hybrid AI that combines the playing styles of different professionals. For example, the generation AI collects game data from different professionals and analyzes their respective playing styles. This generates a hybrid AI that combines the playing styles of different professionals. The generation AI also analyzes the playing styles of different professionals and learns a playing style that combines their respective strengths. The generation AI generates a hybrid AI based on that data. Furthermore, by combining the playing styles of different professionals, the generation AI learns new strategies and generates a hybrid AI. For example, the generation AI combines the playing styles of an offensive professional with a defensive professional. This makes it possible to generate a hybrid AI that combines the playing styles of different professionals.

[0037] The generation AI can generate a multi-game AI that combines the play styles of different games. For example, the generation AI collects game data for Go and Shogi and analyzes the play styles of each. This generates a multi-game AI that combines the play styles of different games. The generation AI also analyzes the play styles of different games and learns a play style that combines the strategies of each game. The generation AI generates a multi-game AI based on that data. Furthermore, by combining the play styles of different games, the generation AI learns new strategies and generates a multi-game AI. For example, combining Go and chess strategies. This makes it possible to generate a multi-game AI that combines the play styles of different games.

[0038] The match unit can learn the user's playing style. The match unit can provide an AI that evolves with each match. For example, the match unit collects the user's match data, and the generation AI analyzes that data. This allows the match unit to learn the user's playing style and play more adaptively in the next match. The generation AI also analyzes the user's playing style and learns specific strategies and patterns. The generation AI uses that data to provide an AI that evolves with each match. The generation AI also evolves the playing style based on the user's match data. For example, it learns the user's weaknesses and adopts strategies that exploit them. This allows the match unit to learn the user's playing style and provide an AI that evolves with each match.

[0039] The match unit can analyze the user's past match data. The match unit can adopt strategies that exploit the user's weaknesses. For example, the match unit collects the user's past match data, and the generation AI analyzes that data. This identifies the user's weaknesses and learns strategies to exploit them. The match unit also analyzes the user's match data and models weaknesses in specific situations. The generation AI adopts strategies to exploit the user's weaknesses based on that data. The generation AI also learns strategies to exploit weaknesses based on the user's past match data. For example, for a user who is weak against a particular move, a strategy that makes frequent use of that move is adopted. This makes it possible to analyze the user's past match data and adopt strategies to exploit the user's weaknesses.

[0040] The battle unit can provide a multiplayer function that allows multiple users to compete against each other simultaneously. The battle unit, for example, builds a system that enables the generated copy AI to compete against multiple users simultaneously. For example, it provides a platform that allows multiple users to compete online at the same time. Furthermore, using the multiplayer function, the generated copy AI competes against multiple users simultaneously. This promotes competition between users. It also provides an interface for simultaneous competition against multiple users, and the generated copy AI plays in a way that is adapted to each user. For example, it adopts a strategy that suits each user's playing style. This makes it possible to provide a multiplayer function that allows multiple users to compete at the same time.

[0041] The match unit can provide a cross-game match function that allows simultaneous play against copy AIs of different games. The match unit provides a cross-game match function that allows simultaneous play against copy AIs of, for example, Go and Shogi. For example, a user can play both Go and Shogi at the same time. The match unit also provides an interface for simultaneous play against copy AIs of different games, allowing users to enjoy multiple games simultaneously. Furthermore, the cross-game match function allows generated copy AIs to play against each other in different games simultaneously. This allows users to enjoy a diverse gaming experience. This makes it possible to provide a cross-game match function that allows simultaneous play against copy AIs of different games.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The professional battle system may further include an analysis unit that collects the user's play data and analyzes the user's play style. The analysis unit can identify the user's strengths and weaknesses based on the user's play data and provide an individual training plan. For example, if the user frequently makes mistakes in a particular situation, the analysis unit can provide practice exercises specific to that situation. The analysis unit can also analyze the user's play style and clarify the differences from a professional's play style, thereby showing how the user should improve their play. Furthermore, the analysis unit can provide a function that tracks the user's growth and visualizes their progress based on the user's play data, making it easier for the user to realize their own improvement in skill.

[0044] The professional battle system may further include an advice unit that provides advice to improve the user's playing style based on the user's play data. The advice unit can analyze the user's play data and suggest optimal moves for specific situations. For example, if the user tends to choose aggressive moves, the advice unit can encourage a balanced playing style by suggesting defensive moves. The advice unit can also analyze the user's playing style and clarify the differences from professional playing styles to show how the user should improve their play. Furthermore, the advice unit can track the user's growth and provide advice according to the user's progress. This makes it easier for the user to realize their own improvement in skill.

[0045] The professional match system can further include a function for comparing a user's playing style with that of other users based on the user's play data. The comparison unit can analyze the user's play data and clarify the differences between the user and other users, thereby showing how the user should improve their play. For example, by comparing differences in hand selection or strategy in specific situations, the comparison unit can provide an opportunity for the user to reconsider their own playing style. The comparison unit can also visualize the skill difference between the user and other users based on the user's play data. This makes it easier for the user to objectively grasp their skill level. Furthermore, the comparison unit can track the user's growth and provide feedback according to their progress.

[0046] The professional battle system can further include a function to compare a user's playing style with that of other games based on the user's play data. The comparison unit can analyze the user's play data and clarify the differences with other games, thereby showing the user how to improve their play. For example, comparing the differences in play styles between Go and Shogi can provide an opportunity for the user to learn new strategies. The comparison unit can also visualize the skill difference with other games based on the user's play data. This makes it easier for the user to objectively grasp their own skill level. Furthermore, the comparison unit can track the user's growth and provide feedback according to their progress.

[0047] The professional battle system can further include a function to evolve a user's play style based on the user's play data. The evolution unit analyzes the user's play data and tracks changes in the user's play style, allowing the user to play more adaptively in the next battle. For example, if the user frequently makes mistakes in a particular situation, it can provide practice exercises specific to that situation. Furthermore, by analyzing the user's play style and clarifying the differences from a professional's play style, it can show the user how to improve their play. Furthermore, the evolution unit can track the user's growth and provide feedback according to their progress, making it easier for the user to realize their own skill improvement.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The play data collection unit collects professional play data. For example, it collects game data from professional Go players, game records from professional Shogi players, game records from professional chess players, and video of professional card game players. The play data collection unit can also collect biometric data (heart rate, brain waves, etc.) from professional players during games. Step 2: The analysis unit analyzes the professional's play data collected by the play data collection unit. For example, the generation AI analyzes the professional's playing style and strategy using data mining techniques, statistical analysis, and machine learning algorithms. Step 3: The copy AI generator generates a copy AI that mimics the professional playing style based on the data analyzed by the analysis unit. For example, the generator uses techniques such as deep learning and reinforcement learning to faithfully reproduce the professional playing style. Step 4: In the competition section, the user competes against the copy AI generated by the copy AI generation section. For example, the user can play against an AI with a professional playing style via their home computer or smartphone. This allows the professional competition system to allow users to compete against professionals from the comfort of their own home, helping them improve their skills and research strategies.

[0050] (Example 2) The professional competition system according to an embodiment of the present invention generates copy AI that imitates the playing style of professionals in various games such as Go, Shogi, Chess, and card games, allowing anyone to easily play against professionals or practice against them. This allows users to play against professionals from the comfort of their own homes, which can be useful for improving skills and studying strategies.

[0051] A professional competition system according to an embodiment includes a play data collection unit, an analysis unit, a copy AI generation unit, and a competition unit. The play data collection unit collects professional play data. For example, it collects game data from professional Go players, game records from professional Shogi players, game records from professional chess players, and video clips of professional card game players. The play data collection unit can also collect biometric data (e.g., heart rate and brain waves) from professional games. The analysis unit analyzes the professional play data collected by the play data collection unit. For example, the generation AI analyzes the professional's playing style and strategy using data mining techniques, statistical analysis, and machine learning algorithms. The copy AI generation unit generates a copy AI that mimics the professional's playing style based on the data analyzed by the analysis unit. For example, the generation AI faithfully reproduces the professional's playing style using techniques such as deep learning and reinforcement learning. The competition unit allows a user to compete against the copy AI generated by the copy AI generation unit. For example, a user can play against an AI with a professional's playing style via their home computer or smartphone. This allows users to play against professionals from the comfort of their own homes, helping them improve their skills and research strategies.

[0052] The play data collection unit can collect biometric data, including heart rate and brain waves, from professional players during games. The generation AI can reproduce professional playing styles by analyzing the biometric data. For example, the play data collection unit monitors heart rate and brain waves in real time during professional games and collects the data. The generation AI analyzes this biometric data to learn the professional's psychological state and level of concentration. It also analyzes the biometric data during games to model the professional's psychological reactions at specific stages. For example, it identifies situations where players become tense or relaxed and reproduces a playing style based on that. It also uses the biometric data to analyze changes in the professional's playing style. For example, it learns whether to play an offensive or defensive move based on fluctuations in heart rate and reflects this in the generation AI. In this way, analyzing the biometric data allows for a more realistic playing style to be reproduced.

[0053] The play data collection unit can collect post-game interviews and comments from professional players. By analyzing the interviews and comments, the generation AI can understand the professional's strategy and thought process. For example, the play data collection unit collects post-game interviews from professional players as text data, and the generation AI analyzes the content. For example, it learns in detail the strategy and thought process during the game. It also collects the professional's comments as audio data and converts them into text using speech recognition technology. The generation AI analyzes the text data to understand the professional's strategy and thought process. It also analyzes post-game interviews and comments to learn the professional's intentions and decision-making criteria at specific situations. For example, it analyzes why a move was chosen and what strategy the professional was considering. This allows for a deeper understanding of the professional's strategy and thought process by analyzing the interviews and comments.

[0054] The play data collection unit can use the emotion estimation function to analyze changes in emotions during professional games. The generation AI can learn changes in play style based on changes in emotions. For example, the play data collection unit analyzes facial expressions and voices of professional players during games and uses the emotion estimation function to monitor changes in emotions in real time. The generation AI learns changes in play style according to emotions based on that data. The emotion estimation function also analyzes changes in emotions during professional games and models play styles in specific emotional states. For example, it learns how to choose moves when nervous or relaxed. The emotional data of professional players during games is also analyzed to learn changes in strategy based on changes in emotions. For example, it analyzes whether players tend to choose aggressive moves when they are emotionally charged. In this way, by analyzing changes in emotions, it is possible to learn changes in play style based on emotions.

[0055] The play data collection unit can also collect amateur play data. The generation AI can learn different levels of play styles based on the amateur play data. For example, the play data collection unit collects amateur game data, and the generation AI analyzes that data. This allows the generation AI to learn the differences between professional and amateur play styles and reproduce play styles at different levels. The generation AI can also analyze amateur play data to learn what strategies players of a particular level use. For example, it can model the play styles of beginners, intermediate players, and advanced players. The generation AI can also learn different levels of play styles based on the amateur play data and provide opponents appropriate to the user's level. For example, it can provide a beginner-level AI to beginners and an advanced-level AI to advanced players. In this way, by collecting amateur play data, it is possible to learn different levels of play styles.

[0056] The play data collection unit can simultaneously analyze data from different games. The generation AI can extract common strategies and thinking patterns based on data from different games. For example, the play data collection unit can simultaneously analyze game data from Go and Shogi to extract common strategies and thinking patterns. For example, it learns the balance between offense and defense in both games. It also analyzes data from different games to model common thinking patterns. For example, it extracts common points in strategic thinking based on data from Go and chess. It also analyzes data from different games simultaneously, and the generation AI learns common strategies for each game. For example, it learns risk management methods based on data from Go and card games. This makes it possible to extract common strategies and thinking patterns by analyzing data from different games.

[0057] The play data collection unit can analyze changes in the professional's emotions using the emotion estimation function. The generation AI can propose different strategies based on changes in emotions. For example, the play data collection unit uses the emotion estimation function to analyze changes in the professional's emotions during a game and proposes different strategies based on that data. For example, it proposes a defensive strategy when the professional is nervous and an offensive strategy when the professional is relaxed. The play data collection unit also analyzes the professional's emotional data and learns the optimal strategy for a specific emotional state. The generation AI then proposes a strategy to the user based on that data, depending on the professional's emotions. The emotion estimation function also learns how to change the strategy based on changes in the professional's emotions and proposes that strategy to the user. For example, it proposes a risk-avoiding strategy when the player is emotionally charged. This allows for more adaptive play by proposing different strategies based on changes in emotions.

[0058] The generative AI can learn the evolution of professional playing styles. The generative AI can reproduce changes from the past to the present. For example, the generative AI collects professional playing data by year and analyzes that data. This allows it to learn the evolution of professional playing styles and reproduce changes from the past to the present. It also analyzes the changes in professional playing styles and models changes in strategy at specific times. For example, it learns how strategies have evolved based on past data. The generative AI also learns the evolution of professional playing styles and reproduces those changes. For example, it reproduces changes in professional playing styles based on data from past games. This allows it to learn the evolution of professional playing styles and reproduce changes from the past to the present.

[0059] The generation AI can learn the weaknesses and mistakes of professional players' playing styles. The generation AI can generate a playing style that improves on those weaknesses and mistakes. For example, the generation AI analyzes professional game data and learns the weaknesses and mistakes in specific situations. Based on that data, the generation AI generates a playing style that improves on those weaknesses. The generation AI can also analyze the weaknesses of professional playing styles and learn strategies to improve them. For example, it can learn moves to avoid mistakes in specific situations. The generation AI can also generate a playing style that improves on those weaknesses and mistakes based on professional game data. For example, it can analyze past mistakes and learn moves to avoid them. In this way, the generation AI can learn the weaknesses and mistakes of professional playing styles and generate a playing style that improves on them.

[0060] The generation AI can analyze changes in the emotions of professional players using the emotion estimation function. The generation AI can adjust its play style based on changes in emotion and reproduce an emotional play style. For example, the generation AI can use the emotion estimation function to analyze changes in emotions during a professional game and adjust its play style based on that data. The generation AI reproduces an emotional play style. It can also analyze emotional data of professional players and model play styles in specific emotional states. The generation AI can reproduce a play style that corresponds to the emotion based on that data. It can also use the emotion estimation function to learn how to adjust its play style based on changes in the emotions of professional players. The generation AI can reproduce an emotional play style based on that data. This allows it to adjust its play style based on changes in emotion and reproduce an emotional play style.

[0061] The generation AI can generate hybrid AI that combines the playing styles of different professionals. For example, the generation AI collects game data from different professionals and analyzes their respective playing styles. This generates a hybrid AI that combines the playing styles of different professionals. The generation AI also analyzes the playing styles of different professionals and learns a playing style that combines their respective strengths. The generation AI generates a hybrid AI based on that data. Furthermore, by combining the playing styles of different professionals, the generation AI learns new strategies and generates a hybrid AI. For example, the generation AI combines the playing styles of an offensive professional with a defensive professional. This makes it possible to generate a hybrid AI that combines the playing styles of different professionals.

[0062] The generation AI can generate a multi-game AI that combines the play styles of different games. For example, the generation AI collects game data for Go and Shogi and analyzes the play styles of each. This generates a multi-game AI that combines the play styles of different games. The generation AI also analyzes the play styles of different games and learns a play style that combines the strategies of each game. The generation AI generates a multi-game AI based on that data. Furthermore, by combining the play styles of different games, the generation AI learns new strategies and generates a multi-game AI. For example, combining Go and chess strategies. This makes it possible to generate a multi-game AI that combines the play styles of different games.

[0063] The generation AI can analyze the user's emotions using the emotion estimation function. The generation AI can adjust the play style based on the emotions and provide a match that is tailored to the user. For example, the generation AI can use the emotion estimation function to analyze the user's emotions in real time and adjust the play style based on that data. This allows for a match that is tailored to the user. The generation AI can also analyze the user's emotional data and learn the optimal play style for a specific emotional state. The generation AI can provide a play style that is tailored to the user based on that data. The generation AI can also use the emotion estimation function to learn how to adjust the play style based on changes in the user's emotions. The generation AI can provide a match that is tailored to the user based on that data. This allows for a match that is tailored to the user by adjusting the play style according to the user's emotions.

[0064] The match unit can learn the user's playing style. The match unit can provide an AI that evolves with each match. For example, the match unit collects the user's match data, and the generation AI analyzes that data. This allows the match unit to learn the user's playing style and play more adaptively in the next match. The generation AI also analyzes the user's playing style and learns specific strategies and patterns. The generation AI uses that data to provide an AI that evolves with each match. The generation AI also evolves the playing style based on the user's match data. For example, it learns the user's weaknesses and adopts strategies that exploit them. This allows the match unit to learn the user's playing style and provide an AI that evolves with each match.

[0065] The match unit can analyze the user's past match data. The match unit can adopt strategies that exploit the user's weaknesses. For example, the match unit collects the user's past match data, and the generation AI analyzes that data. This identifies the user's weaknesses and learns strategies to exploit them. The match unit also analyzes the user's match data and models weaknesses in specific situations. The generation AI adopts strategies to exploit the user's weaknesses based on that data. The generation AI also learns strategies to exploit weaknesses based on the user's past match data. For example, for a user who is weak against a particular move, a strategy that makes frequent use of that move is adopted. This makes it possible to analyze the user's past match data and adopt strategies to exploit the user's weaknesses.

[0066] The match unit can analyze the user's emotions using the emotion estimation function. The match unit can adjust the difficulty of the match based on the emotions, thereby maintaining the user's motivation. For example, the match unit uses the emotion estimation function to analyze the user's emotions in real time, and the generation AI adjusts the difficulty of the match based on that data. This maintains the user's motivation. The match unit also analyzes the user's emotional data and learns the optimal difficulty level for a specific emotional state. The generation AI provides a difficulty level tailored to the user based on that data. The emotion estimation function also learns to adjust the difficulty level based on changes in the user's emotions. The generation AI provides a match that maintains the user's motivation based on that data. This adjusts the difficulty of the match according to the user's emotions, thereby maintaining the user's motivation.

[0067] The battle unit can provide a multiplayer function that allows multiple users to compete against each other simultaneously. The battle unit, for example, builds a system that enables the generated copy AI to compete against multiple users simultaneously. For example, it provides a platform that allows multiple users to compete online at the same time. Furthermore, using the multiplayer function, the generated copy AI competes against multiple users simultaneously. This promotes competition between users. It also provides an interface for simultaneous competition against multiple users, and the generated copy AI plays in a way that is adapted to each user. For example, it adopts a strategy that suits each user's playing style. This makes it possible to provide a multiplayer function that allows multiple users to compete at the same time.

[0068] The match unit can provide a cross-game match function that allows simultaneous play against copy AIs of different games. The match unit provides a cross-game match function that allows simultaneous play against copy AIs of, for example, Go and Shogi. For example, a user can play both Go and Shogi at the same time. The match unit also provides an interface for simultaneous play against copy AIs of different games, allowing users to enjoy multiple games simultaneously. Furthermore, the cross-game match function allows generated copy AIs to play against each other in different games simultaneously. This allows users to enjoy a diverse gaming experience. This makes it possible to provide a cross-game match function that allows simultaneous play against copy AIs of different games.

[0069] The match unit can analyze the user's emotions using the emotion estimation function. The match unit can provide match feedback based on the emotions and support the user's skill improvement. For example, the match unit can use the emotion estimation function to analyze the user's emotions in real time, and the generation AI can provide match feedback based on that data. This supports the user's skill improvement. The match unit also analyzes the user's emotion data and learns optimal feedback for specific emotional states. The generation AI provides feedback tailored to the user based on that data. The emotion estimation function can also provide feedback based on changes in the user's emotions. The generation AI can provide matches to support the user's skill improvement based on that data. This allows the match unit to provide match feedback based on the user's emotions and support the user's skill improvement.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The professional battle system may further include an analysis unit that collects the user's play data and analyzes the user's play style. The analysis unit can identify the user's strengths and weaknesses based on the user's play data and provide an individual training plan. For example, if the user frequently makes mistakes in a particular situation, the analysis unit can provide practice exercises specific to that situation. The analysis unit can also analyze the user's play style and clarify the differences from a professional's play style, thereby showing how the user should improve their play. Furthermore, the analysis unit can provide a function that tracks the user's growth and visualizes their progress based on the user's play data, making it easier for the user to realize their own improvement in skill.

[0072] The professional battle system can further include a function for estimating a user's emotions and selecting an opponent based on the estimated emotions. For example, if the user is nervous, the system selects a more relaxed opponent, and if the user is relaxed, the system selects a more challenging opponent. The system can also analyze the user's emotional data and learn the optimal opponent for a particular emotional state. This makes it easier to maintain the user's motivation by providing opponents that correspond to the user's emotions. Furthermore, by using the emotion estimation function to select opponents based on changes in the user's emotions, a more adaptive battle experience can be provided.

[0073] The professional battle system may further include an advice unit that provides advice to improve the user's playing style based on the user's play data. The advice unit can analyze the user's play data and suggest optimal moves for specific situations. For example, if the user tends to choose aggressive moves, the advice unit can encourage a balanced playing style by suggesting defensive moves. The advice unit can also analyze the user's playing style and clarify the differences from professional playing styles to show how the user should improve their play. Furthermore, the advice unit can track the user's growth and provide advice according to the user's progress. This makes it easier for the user to realize their own improvement in skill.

[0074] The professional competition system can further include a function for estimating a user's emotions and adjusting the training plan based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a relaxing training plan, while if the user is feeling motivated, the system can provide a more challenging training plan. The system can also analyze the user's emotional data and learn the optimal training plan for a specific emotional state. This can help maintain the user's motivation by providing a training plan tailored to the user's emotions. Furthermore, the emotion estimation function can be used to adjust the training plan based on the user's emotional changes, providing a more adaptive training experience.

[0075] The professional match system can further include a function for comparing a user's playing style with that of other users based on the user's play data. The comparison unit can analyze the user's play data and clarify the differences between the user and other users, thereby showing how the user should improve their play. For example, by comparing differences in hand selection or strategy in specific situations, the comparison unit can provide an opportunity for the user to reconsider their own playing style. The comparison unit can also visualize the skill difference between the user and other users based on the user's play data. This makes it easier for the user to objectively grasp their skill level. Furthermore, the comparison unit can track the user's growth and provide feedback according to their progress.

[0076] The professional battle system can further include a function to estimate the user's emotions and provide feedback based on the estimated emotions. For example, if the user is depressed, the system can provide an encouraging message, and if the user is happy, it can provide a message encouraging further challenges. The system can also analyze the user's emotional data and learn optimal feedback for specific emotional states. This makes it easier to maintain the user's motivation by providing feedback according to the user's emotions. Furthermore, the emotion estimation function can be used to adjust feedback based on changes in the user's emotions, providing a more adaptive feedback experience.

[0077] The professional battle system can further include a function to compare a user's playing style with that of other games based on the user's play data. The comparison unit can analyze the user's play data and clarify the differences with other games, thereby showing the user how to improve their play. For example, comparing the differences in play styles between Go and Shogi can provide an opportunity for the user to learn new strategies. The comparison unit can also visualize the skill difference with other games based on the user's play data. This makes it easier for the user to objectively grasp their own skill level. Furthermore, the comparison unit can track the user's growth and provide feedback according to their progress.

[0078] The professional battle system can further include a function for estimating the user's emotions and adjusting the difficulty of the battle based on the estimated emotions. For example, if the user is feeling stressed, the system can lower the difficulty of the battle, and if the user is relaxed, the system can increase the difficulty. The system can also analyze the user's emotional data and learn the optimal difficulty level for a particular emotional state. This makes it easier to maintain the user's motivation by providing a battle difficulty that corresponds to the user's emotions. Furthermore, by using the emotion estimation function to adjust the difficulty level based on changes in the user's emotions, a more adaptive battle experience can be provided.

[0079] The professional battle system can further include a function to evolve a user's play style based on the user's play data. The evolution unit analyzes the user's play data and tracks changes in the user's play style, allowing the user to play more adaptively in the next battle. For example, if the user frequently makes mistakes in a particular situation, it can provide practice exercises specific to that situation. Furthermore, by analyzing the user's play style and clarifying the differences from a professional's play style, it can show the user how to improve their play. Furthermore, the evolution unit can track the user's growth and provide feedback according to their progress, making it easier for the user to realize their own skill improvement.

[0080] The professional battle system can further include a function for estimating the user's emotions and adjusting the opponent's playing style based on the estimated emotions. For example, if the user is nervous, the system adjusts the opponent's playing style to be more defensive, and if the user is relaxed, the system adjusts the opponent's playing style to be more aggressive. The system can also analyze the user's emotional data and learn the optimal playing style for a particular emotional state. This can help maintain the user's motivation by providing an opponent's playing style that corresponds to the user's emotions. Furthermore, the emotion estimation function can be used to adjust the opponent's playing style based on changes in the user's emotions, providing a more adaptive battle experience.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The play data collection unit collects professional play data. For example, it collects game data from professional Go players, game records from professional Shogi players, game records from professional chess players, and video of professional card game players. The play data collection unit can also collect biometric data (heart rate, brain waves, etc.) from professional players during games. Step 2: The analysis unit analyzes the professional's play data collected by the play data collection unit. For example, the generation AI analyzes the professional's playing style and strategy using data mining techniques, statistical analysis, and machine learning algorithms. Step 3: The copy AI generator generates a copy AI that mimics the professional playing style based on the data analyzed by the analysis unit. For example, the generator uses techniques such as deep learning and reinforcement learning to faithfully reproduce the professional playing style. Step 4: In the competition section, the user competes against the copy AI generated by the copy AI generation section. For example, the user can play against an AI with a professional playing style via their home computer or smartphone. This allows the professional competition system to allow users to compete against professionals from the comfort of their own home, helping them improve their skills and research strategies.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A play data collection department that collects professional play data; an analysis unit that analyzes the professional play data collected by the play data collection unit; a copy AI generation unit that imitates the playing style of a professional player based on the data analyzed by the analysis unit; a battle section in which a user battles against the copy AI generated by the copy AI generation section; A system characterized by:

2. The play data collection unit Collect biometric data, including heart rate and brain waves, during professional games. The generated AI is By analyzing the biometric data, we can reproduce the playing style of a professional.

2. The system of claim 1.

3. The play data collection unit We also collect amateur play data, The generated AI is Learning playing styles at different levels based on the amateur's playing data 2. The system of claim 1.

4. The generated AI is Learn the evolution of professional playing styles, Recreating the transition from the past to the present 2. The system of claim 1.

5. The competition section includes: learning the play style of the user; Providing AI that evolves with each match 2. The system of claim 1.

6. The play data collection unit Analyzing the emotional changes during professional matches, The generated AI is Learning how to change play style based on the change in emotion 2. The system of claim 1.

7. The generated AI is Analyzing the emotional changes of professionals, Adjusting a play style based on the change in emotion; Recreate an emotional playstyle 2. The system of claim 1.

8. The competition section includes: Analyzing the user's emotions; Adjusting the difficulty of the match based on the emotion; Keeping the user motivated 2. The system of claim 1.

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