System

Abstract

An object of the system according to the embodiment is for an AI to communicate with a human player in real time and give advice on strategies and tactics.SOLUTION: A system according to an embodiment includes a AI player, a generation AI, a natural language processor, and an advice providing unit. The AI player communicates in real-time with the human player at the natural language processor using the generated AI. The advice providing unit gives advice on strategies and tactics.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, there was a problem in e-sports where AI was not able to communicate with human players in real time and provide advice on strategy and tactics.

[0005] The system of the embodiment aims to enable AI to communicate with human players in real time and provide advice on strategy and tactics. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI player, a generation AI, a natural language processing unit, and an advice providing unit. The AI ​​player uses the generation AI to communicate with a human player in real time through the natural language processing unit. The advice providing unit provides advice on strategy and tactics. [Effects of the Invention]

[0007] In the system according to the embodiment, AI can communicate with human players in real time and provide advice on strategy and tactics. [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 e-sports motorsports system according to an embodiment of the present invention utilizes a large-scale language model to allow players to form teams with AI players capable of conversing with human players in natural language. This allows the e-sports motorsports system to provide real-time advice on strategies and tactics to improve the performance of the entire team.

[0029] An e-sports motorsports system according to an embodiment includes an AI player, a generation AI, a natural language processing unit, and an advice providing unit. The AI ​​player communicates with a human player in real time using the generation AI and the natural language processing unit. For example, the AI ​​player uses the generation AI to generate appropriate answers to the player's questions. The AI ​​player can also use the generation AI to analyze the player's statements and provide appropriate advice. The generation AI uses a large-scale language model such as GPT-3 or BERT. The natural language processing unit analyzes the text generated by the generation AI to smoothly communicate with the player. For example, the natural language processing unit performs morphological analysis and grammatical analysis to accurately understand the meaning of the player's statements. The natural language processing unit can also perform semantic analysis to understand the player's intentions. The advice providing unit uses the generation AI to provide strategy and tactical advice. For example, the advice providing unit advises the player on how to approach the next curve or braking points. The advice providing unit can also provide advice based on the player's emotional state. As a result, the e-sports motorsports system according to the embodiment allows the AI ​​player to provide strategic and tactical advice in real time, improving the player's performance.

[0030] The advice providing unit can provide specific advice in real time during play, such as "take the inside of the next curve." For example, the advice providing unit can provide specific advice in real time during play, such as "take the inside of the next curve." For example, the AI ​​player analyzes the tone and speed of a human player's voice while playing to estimate their emotional state. For example, if the player is feeling impatient, the AI ​​player can provide advice to stay calm. The AI ​​player can also analyze the player's facial expressions with a camera while playing to grasp their emotional state in real time. For example, if the player is feeling nervous, the AI ​​player can provide advice to relax. The AI ​​player can also measure the player's heart rate and galvanic response with sensors while playing to estimate their emotional state. For example, if the player is feeling excited, the AI ​​player can provide advice to stay calm. This allows the player to make quick and accurate decisions by providing specific advice in real time.

[0031] The advice providing unit can support communication in different languages ​​during play, enabling smooth communication even in international teams. The advice providing unit, for example, supports communication in different languages ​​during play, enabling smooth communication even in international teams. For example, an AI player translates into different languages ​​in real time to support communication between players. For example, it interprets between a player speaking Japanese and a player speaking English. The AI ​​player also provides advice in different languages, enabling international teams to share strategies. For example, it translates advice in English and provides it in Spanish. The AI ​​player also communicates taking different cultural backgrounds into consideration, strengthening cooperation among international teams. For example, it provides appropriate advice after understanding cultural differences. In this way, by supporting communication in different languages, cooperation among international teams can be strengthened.

[0032] The advice providing unit can provide visual feedback during play and display visual advice in real time. The advice providing unit, for example, provides visual feedback during play and displays visual advice in real time. For example, the advice providing unit displays visual feedback on the screen while the AI ​​player is playing to provide advice that is visually easy to understand. For example, it displays the optimal line for the next corner. Furthermore, the AI ​​player displays visual feedback on the dashboard while playing to share strategies in real time. For example, it visually indicates the timing of a pit stop. Furthermore, the AI ​​player provides visual feedback using a head-up display (HUD) while playing to display advice directly in the player's field of vision. For example, it indicates braking points. In this way, by providing visual feedback, the player can receive advice in a form that is visually easy to understand.

[0033] The advice providing unit generates a summary of the discussion in real time during a meeting, thereby promoting efficient discussions. The advice providing unit, for example, generates a summary of the discussion in real time during a meeting, thereby promoting efficient discussions. For example, an AI player generates a summary of the discussion in real time during a meeting and shares it with team members. For example, the AI ​​player instantly summarizes and displays important points and decisions. The AI ​​player also automatically extracts the key points of the discussion during a meeting, thereby promoting efficient discussions. For example, the AI ​​player provides a summary to help the discussion progress smoothly. The AI ​​player also generates a summary of the discussion in real time during a meeting and saves it for later reference. For example, the summary is shared by email after the meeting ends. In this way, efficient discussions can be promoted by generating a summary of the discussion in real time.

[0034] The advice providing unit can generate visual notes during meetings to provide advice in a form that is visually easy to understand. The advice providing unit, for example, generates visual notes during meetings to provide advice in a form that is visually easy to understand. For example, an AI player generates visual notes during meetings to visually display the main points of the discussion. For example, important points are indicated with diagrams or icons. The AI ​​player also generates visual notes during meetings to provide advice in a form that is visually easy to understand for team members. For example, a strategy flowchart is displayed. The AI ​​player also generates visual notes during meetings to save them for later reference. For example, the visual notes are shared after the meeting ends. In this way, by generating visual notes, advice can be provided in a form that is visually easy to understand.

[0035] The advice providing unit can integrate knowledge from different fields of expertise during a meeting to provide comprehensive advice. The advice providing unit can, for example, integrate knowledge from different fields of expertise during a meeting to provide comprehensive advice. For example, an AI player can integrate knowledge from different fields of expertise during a meeting to provide comprehensive advice. For example, it can propose a strategy that combines technical knowledge and marketing knowledge. The AI ​​player can also analyze data from different fields of expertise during a meeting to provide comprehensive advice. For example, it can propose a strategy by integrating technical data and market data. The AI ​​player can also integrate knowledge from different fields of expertise in real time during a meeting to provide comprehensive advice. For example, it can make proposals to solve technical problems and business challenges simultaneously. This makes it possible to provide comprehensive advice by integrating knowledge from different fields of expertise.

[0036] The advice providing unit can learn past race data and provide skills and know-how that are optimal for specific situations. The advice providing unit, for example, learns past race data and provides skills and know-how that are optimal for specific situations. For example, the AI ​​player analyzes past race data and provides the optimal skills and know-how for specific situations. For example, it teaches the optimal driving techniques for races in the rain. The AI ​​player also learns the player's past race results and suggests the optimal strategy for specific situations. For example, it provides specific advice on starting to a player who is not good at starting dashes. The AI ​​player also analyzes the player's past mistakes and provides skills and know-how to prevent the player from repeating the same mistakes. For example, it provides specific instructions on braking points at specific corners. In this way, the AI ​​player can improve the player's performance by learning past race data and providing the optimal skills and know-how for specific situations.

[0037] The advice providing unit can simulate different race scenarios and provide the optimal skills and know-how for each scenario. The advice providing unit, for example, simulates different race scenarios and provides the optimal skills and know-how for each scenario. For example, an AI player simulates different race scenarios and provides the optimal skills and know-how for each scenario. For example, teaching techniques for maintaining visibility in night races. The AI ​​player also simulates different weather conditions and provides the optimal skills and know-how for each condition. For example, teaching tire selection and driving techniques for rainy weather. The AI ​​player also simulates different course layouts and provides the optimal skills and know-how for each course. For example, teaching the optimal line to take and braking points for a particular course. In this way, by simulating different race scenarios and providing the optimal skills and know-how for each scenario, the player's performance can be improved.

[0038] The advice providing unit can use visual feedback to provide specialized skills and know-how in a visually easy-to-understand form. The advice providing unit, for example, uses visual feedback to provide specialized skills and know-how in a visually easy-to-understand form. For example, an AI player uses visual feedback to provide specialized skills and know-how in a visually easy-to-understand form. For example, the optimal line for the next corner is displayed. The AI ​​player also uses visual feedback to provide specialized skills and know-how in a visually easy-to-understand form. For example, the timing of a pit stop is visually indicated. The AI ​​player also uses visual feedback to provide specialized skills and know-how in a visually easy-to-understand form. For example, braking points are indicated. In this way, by using visual feedback to provide skills and know-how in a visually easy-to-understand form, it is possible to improve the player's performance.

[0039] The advice providing unit can learn past play data and provide feedback according to the player's growth. The advice providing unit, for example, learns past play data and provides feedback according to the player's growth. For example, an AI player analyzes past play data and provides specific feedback according to the player's growth. For example, it proposes strategies based on past success stories. The AI ​​player also learns the player's past performance data and points out areas for improvement according to the player's growth. For example, it proposes specific training methods for players who need to improve a specific skill. The AI ​​player also learns past play data and provides feedback according to the player's growth. For example, it provides specific advice to prevent past mistakes from being repeated. In this way, the player's performance can be improved by learning past play data and providing feedback according to the player's growth.

[0040] The advice providing unit can integrate different data sources to provide comprehensive analysis results. For example, the advice providing unit integrates different data sources to provide comprehensive analysis results. For example, an AI player analyzes play data and integrates different data sources to provide comprehensive analysis results. For example, race data and heart rate data are integrated and analyzed. The AI ​​player also analyzes play data and integrates different data sources to provide comprehensive analysis results. For example, race data and weather data are integrated and analyzed. The AI ​​player also analyzes play data and integrates different data sources to provide comprehensive analysis results. For example, race data and vehicle data are integrated and analyzed. This allows for the integration of different data sources to provide comprehensive analysis results, thereby improving player performance.

[0041] The advice providing unit can provide the analysis results in a visually easy-to-understand format using visual feedback. The advice providing unit, for example, uses visual feedback to provide the analysis results in a visually easy-to-understand format. For example, an AI player analyzes play data and provides the analysis results in a visually easy-to-understand format using visual feedback. For example, a graph of lap times is displayed. Alternatively, the AI ​​player analyzes play data and provides the analysis results in a visually easy-to-understand format using visual feedback. For example, cornering data is displayed in a graph. Alternatively, the AI ​​player analyzes play data and provides the analysis results in a visually easy-to-understand format using visual feedback. For example, braking points are displayed. In this way, by using visual feedback to provide the analysis results in a visually easy-to-understand format, the player's performance can be improved.

[0042] The advice providing unit can integrate data from different sports and fields and provide crossover analysis results. The advice providing unit, for example, integrates data from different sports and fields and provides crossover analysis results. For example, an AI player integrates data from different sports and provides crossover analysis results. For example, soccer data is applied to motorsports. Also, an AI player integrates data from different fields and provides crossover analysis results. For example, aircraft data is applied to motorsports. Also, an AI player integrates data from different sports and fields and provides crossover analysis results. For example, chess data is applied to motorsports. In this way, by integrating data from different sports and fields and providing crossover analysis results, it is possible to improve player performance.

[0043] The advice providing unit can learn from past race data and provide the optimal strategy for a specific situation. The advice providing unit, for example, learns from past race data and provides the optimal strategy for a specific situation. For example, an AI player analyzes past race data and provides the optimal strategy for a specific situation. For example, it may suggest the optimal pit stop timing for a race in rainy weather. The AI ​​player also learns the player's past race results and suggests the optimal strategy for a specific situation. For example, it may provide specific advice on the start to a player who is not good at starting dashes. The AI ​​player may also analyze the player's past mistakes and provide a strategy to prevent the player from repeating the same mistake. For example, it may provide specific instructions on braking points at specific corners. In this way, by learning from past race data and providing the optimal strategy for a specific situation, the performance of the entire team can be improved.

[0044] The advice providing unit can simulate different race scenarios and provide the optimal strategy for each scenario. The advice providing unit, for example, simulates different race scenarios and provides the optimal strategy for each scenario. For example, an AI player simulates different race scenarios and provides the optimal strategy for each scenario. For example, teaching techniques for ensuring visibility in night races. Also, an AI player simulates different weather conditions and provides the optimal strategy for each condition. For example, teaching tire selection and driving techniques for rainy weather. Also, an AI player simulates different course layouts and provides the optimal strategy for each course. For example, teaching the optimal line to take and braking points for a particular course. In this way, by simulating different race scenarios and providing the optimal strategy for each scenario, the performance of the entire team can be improved.

[0045] The advice providing unit can integrate knowledge of different sports and fields to provide crossover strategies. The advice providing unit, for example, integrates knowledge of different sports and fields to provide crossover strategies. For example, an AI player integrates knowledge of different sports to provide crossover strategies. For example, soccer tactics are applied to motorsports. Also, an AI player integrates knowledge of different fields to provide crossover strategies. For example, aircraft piloting techniques are applied to motorsports. Also, an AI player integrates knowledge of different sports and fields to provide crossover strategies. For example, chess strategies are applied to motorsports. In this way, by integrating knowledge of different sports and fields to provide crossover strategies, it is possible to improve the performance of the entire team.

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

[0047] The advice providing unit can support communication in different languages ​​during gameplay, enabling smooth communication even in international teams. For example, an AI player can translate into different languages ​​in real time to support communication between players. For example, it can act as an interpreter between a player speaking Japanese and a player speaking English. The AI ​​player can also provide advice in different languages, enabling international teams to share strategies. For example, it can translate advice in English into Spanish. The AI ​​player can also communicate taking into account different cultural backgrounds, strengthening cooperation among international teams. For example, it can provide appropriate advice after understanding cultural differences. This makes it possible to strengthen cooperation among international teams by supporting communication in different languages.

[0048] The advice providing unit can provide visual feedback during play and display visual advice in real time. For example, the AI ​​player can display visual feedback on the screen while playing to provide advice that is visually easy to understand. For example, it can display the optimal line for the next corner. The AI ​​player can also display visual feedback on the dashboard while playing to share strategies in real time. For example, it can visually indicate the timing of pit stops. The AI ​​player can also provide visual feedback using a head-up display (HUD) while playing to display advice directly in the player's field of vision. For example, it can indicate braking points. In this way, by providing visual feedback, the player can receive advice in a form that is visually easy to understand.

[0049] The advice providing unit generates a summary of the discussion in real time during a meeting, which can promote efficient discussions. For example, the AI ​​player generates a summary of the discussion in real time during a meeting and shares it with team members. For example, it instantly summarizes and displays important points and decisions. The AI ​​player also automatically extracts the key points of the discussion during a meeting and promotes efficient discussions. For example, it provides a summary to help the discussion progress smoothly. The AI ​​player also generates a summary of the discussion in real time during a meeting and saves it for later reference. For example, it shares the summary by email after the meeting ends. In this way, efficient discussions can be promoted by generating a summary of the discussion in real time.

[0050] The advice provider can study past race data and provide the optimal skills and know-how for specific situations. For example, an AI player can analyze past race data and provide the optimal skills and know-how for specific situations. For example, it can teach the optimal driving techniques for races in the rain. The AI ​​player can also study the player's past race results and suggest the optimal strategy for specific situations. For example, it can provide specific advice on starting to a player who is not good at starting dashes. The AI ​​player can also analyze the player's past mistakes and provide the skills and know-how to prevent the player from repeating the same mistakes. For example, it can provide specific instructions on braking points at specific corners. In this way, the AI ​​player can improve the player's performance by learning past race data and providing the optimal skills and know-how for specific situations.

[0051] The advice providing unit can simulate different race scenarios and provide the optimal skills and know-how for each scenario. For example, an AI player can simulate different race scenarios and provide the optimal skills and know-how for each scenario. For example, it can teach techniques for maintaining visibility in night races. Alternatively, the AI ​​player can simulate different weather conditions and provide the optimal skills and know-how for each condition. For example, it can teach tire selection and driving techniques for rainy weather. Alternatively, the AI ​​player can simulate different course layouts and provide the optimal skills and know-how for each course. For example, it can teach the optimal line to take and braking points for a particular course. In this way, by simulating different race scenarios and providing the optimal skills and know-how for each scenario, the player's performance can be improved.

[0052] The advice provider can integrate knowledge from different sports and fields to provide crossover strategies. For example, an AI player can integrate knowledge from different sports to provide crossover strategies. For example, soccer tactics can be applied to motorsports. Alternatively, an AI player can integrate knowledge from different fields to provide crossover strategies. For example, aircraft piloting techniques can be applied to motorsports. Alternatively, an AI player can integrate knowledge from different sports and fields to provide crossover strategies. For example, chess strategies can be applied to motorsports. In this way, by integrating knowledge from different sports and fields to provide crossover strategies, it is possible to improve the performance of the entire team.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The AI ​​player uses the generative AI to communicate with the human player in real time using a natural language processing unit. For example, the AI ​​player uses the generative AI to generate appropriate answers to the player's questions. The AI ​​player can also use the generative AI to analyze the player's comments and provide appropriate advice. Step 2: Generative AI uses large-scale language models such as GPT-3 and BERT, which gives the AI ​​player advanced natural language generation capabilities and allows it to communicate smoothly with players. Step 3: The natural language processing unit analyzes the text generated by the generation AI and facilitates communication with the player. For example, the natural language processing unit performs morphological and grammatical analysis to accurately understand the meaning of the player's statements. The natural language processing unit can also perform semantic analysis to understand the player's intentions. Step 4: The advice provider uses the generative AI to provide strategic and tactical advice. For example, the advice provider may provide advice to the player on how to attack the next curve or on braking points. The advice provider may also provide advice based on the player's emotional state.

[0055] (Example 2) The e-sports motorsports system according to an embodiment of the present invention utilizes a large-scale language model to allow players to form teams with AI players capable of conversing with human players in natural language. This allows the e-sports motorsports system to provide real-time advice on strategies and tactics to improve the performance of the entire team.

[0056] An e-sports motorsports system according to an embodiment includes an AI player, a generation AI, a natural language processing unit, and an advice providing unit. The AI ​​player communicates with a human player in real time using the generation AI and the natural language processing unit. For example, the AI ​​player uses the generation AI to generate appropriate answers to the player's questions. The AI ​​player can also use the generation AI to analyze the player's statements and provide appropriate advice. The generation AI uses a large-scale language model such as GPT-3 or BERT. The natural language processing unit analyzes the text generated by the generation AI to smoothly communicate with the player. For example, the natural language processing unit performs morphological analysis and grammatical analysis to accurately understand the meaning of the player's statements. The natural language processing unit can also perform semantic analysis to understand the player's intentions. The advice providing unit uses the generation AI to provide strategy and tactical advice. For example, the advice providing unit advises the player on how to approach the next curve or braking points. The advice providing unit can also provide advice based on the player's emotional state. As a result, the e-sports motorsports system according to the embodiment allows the AI ​​player to provide strategic and tactical advice in real time, improving the player's performance.

[0057] The advice providing unit can provide specific advice in real time during play, such as "take the inside of the next curve." For example, the advice providing unit can provide specific advice in real time during play, such as "take the inside of the next curve." For example, the AI ​​player analyzes the tone and speed of a human player's voice while playing to estimate their emotional state. For example, if the player is feeling impatient, the AI ​​player can provide advice to stay calm. The AI ​​player can also analyze the player's facial expressions with a camera while playing to grasp their emotional state in real time. For example, if the player is feeling nervous, the AI ​​player can provide advice to relax. The AI ​​player can also measure the player's heart rate and galvanic response with sensors while playing to estimate their emotional state. For example, if the player is feeling excited, the AI ​​player can provide advice to stay calm. This allows the player to make quick and accurate decisions by providing specific advice in real time.

[0058] The advice providing unit can estimate the player's emotions while playing and provide advice according to their emotional state. For example, the advice providing unit estimates the player's emotions while playing and provides advice according to their emotional state. For example, the AI ​​player analyzes past play data to identify courses that the player is good at and difficult at. For example, it provides advice on points that the player should pay particular attention to on courses that the player is not good at. The AI ​​player also learns the player's past race results and suggests the optimal strategy for specific situations. For example, it provides specific advice on the start to a player who is not good at starting dashes. The AI ​​player also analyzes the player's past mistakes and provides advice to prevent the player from repeating the same mistakes. For example, it provides specific instructions on braking points at specific corners. In this way, by providing advice according to the player's emotional state, the player's performance can be optimized.

[0059] The advice providing unit can support communication in different languages ​​during play, enabling smooth communication even in international teams. The advice providing unit, for example, supports communication in different languages ​​during play, enabling smooth communication even in international teams. For example, an AI player translates into different languages ​​in real time to support communication between players. For example, it interprets between a player speaking Japanese and a player speaking English. The AI ​​player also provides advice in different languages, enabling international teams to share strategies. For example, it translates advice in English and provides it in Spanish. The AI ​​player also communicates taking different cultural backgrounds into consideration, strengthening cooperation among international teams. For example, it provides appropriate advice after understanding cultural differences. In this way, by supporting communication in different languages, cooperation among international teams can be strengthened.

[0060] The advice providing unit can use voice recognition technology to infer a player's emotions from the tone and speed of their voice during play and provide advice based on that. The advice providing unit can, for example, use voice recognition technology to infer a player's emotions from the tone and speed of their voice during play and provide advice based on that. For example, an AI player can analyze the tone of a player's voice to infer their emotional state. For example, if their voice is getting higher, it can determine that they are nervous and provide advice to relax. The AI ​​player can also analyze the speed of the player's voice to infer their emotional state. For example, if their voice is getting faster, it can determine that they are impatient and provide advice to stay calm. The AI ​​player can also analyze the volume of the player's voice to infer their emotional state. For example, if their voice is getting louder, it can determine that they are excited and provide advice to stay calm. In this way, by inferring a player's emotions from the tone and speed of their voice and providing advice based on that, it is possible to optimize the player's performance.

[0061] The advice providing unit can provide visual feedback during play and display visual advice in real time. The advice providing unit, for example, provides visual feedback during play and displays visual advice in real time. For example, the advice providing unit displays visual feedback on the screen while the AI ​​player is playing to provide advice that is visually easy to understand. For example, it displays the optimal line for the next corner. Furthermore, the AI ​​player displays visual feedback on the dashboard while playing to share strategies in real time. For example, it visually indicates the timing of a pit stop. Furthermore, the AI ​​player provides visual feedback using a head-up display (HUD) while playing to display advice directly in the player's field of vision. For example, it indicates braking points. In this way, by providing visual feedback, the player can receive advice in a form that is visually easy to understand.

[0062] The advice providing unit can use the emotion estimation function to provide messages to improve motivation that correspond to the player's emotions. The advice providing unit, for example, uses the emotion estimation function to provide messages to improve motivation that correspond to the player's emotions. For example, the AI ​​player analyzes the player's emotional state and provides messages to improve motivation. For example, if the player is feeling down, it will offer words of encouragement. The AI ​​player also monitors the player's emotional state in real time and provides motivational messages at appropriate times. For example, if the player is tired, it will encourage the player to take a break. The AI ​​player also provides optimal motivational messages for each individual player based on the player's emotional data. For example, it selects words of encouragement by referring to the player's past data. In this way, by providing motivational messages that correspond to the player's emotions, it is possible to optimize the player's performance.

[0063] The advice providing unit can use the emotion estimation function to analyze the emotions of team members during a team meeting and provide feedback based on their emotions. The advice providing unit can, for example, use the emotion estimation function to analyze the emotions of team members during a team meeting and provide feedback based on their emotions. For example, the AI ​​player analyzes the facial expressions of team members during a meeting to grasp their emotional state in real time. For example, if a member feels anxious, the AI ​​player provides feedback to reassure them. The AI ​​player can also analyze the tone and speed of the team members' voices during a meeting to estimate their emotional state. For example, if a member is excited, the AI ​​player provides feedback to calm down. The AI ​​player can also measure the heart rate and galvanic response of team members with sensors during a meeting to estimate their emotional state. For example, if a member feels nervous, the AI ​​player provides feedback to relax. In this way, by providing feedback based on the emotions of team members, the performance of the entire team can be improved.

[0064] The advice providing unit generates a summary of the discussion in real time during a meeting, thereby promoting efficient discussions. The advice providing unit, for example, generates a summary of the discussion in real time during a meeting, thereby promoting efficient discussions. For example, an AI player generates a summary of the discussion in real time during a meeting and shares it with team members. For example, the AI ​​player instantly summarizes and displays important points and decisions. The AI ​​player also automatically extracts the key points of the discussion during a meeting, thereby promoting efficient discussions. For example, the AI ​​player provides a summary to help the discussion progress smoothly. The AI ​​player also generates a summary of the discussion in real time during a meeting and saves it for later reference. For example, the summary is shared by email after the meeting ends. In this way, efficient discussions can be promoted by generating a summary of the discussion in real time.

[0065] The advice providing unit can generate visual notes during meetings to provide advice in a form that is visually easy to understand. The advice providing unit, for example, generates visual notes during meetings to provide advice in a form that is visually easy to understand. For example, an AI player generates visual notes during meetings to visually display the main points of the discussion. For example, important points are indicated with diagrams or icons. The AI ​​player also generates visual notes during meetings to provide advice in a form that is visually easy to understand for team members. For example, a strategy flowchart is displayed. The AI ​​player also generates visual notes during meetings to save them for later reference. For example, the visual notes are shared after the meeting ends. In this way, by generating visual notes, advice can be provided in a form that is visually easy to understand.

[0066] The advice providing unit can integrate knowledge from different fields of expertise during a meeting to provide comprehensive advice. The advice providing unit can, for example, integrate knowledge from different fields of expertise during a meeting to provide comprehensive advice. For example, an AI player can integrate knowledge from different fields of expertise during a meeting to provide comprehensive advice. For example, it can propose a strategy that combines technical knowledge and marketing knowledge. The AI ​​player can also analyze data from different fields of expertise during a meeting to provide comprehensive advice. For example, it can propose a strategy by integrating technical data and market data. The AI ​​player can also integrate knowledge from different fields of expertise in real time during a meeting to provide comprehensive advice. For example, it can make proposals to solve technical problems and business challenges simultaneously. This makes it possible to provide comprehensive advice by integrating knowledge from different fields of expertise.

[0067] The advice providing unit can use the emotion estimation function to monitor the emotions of team members during meetings in real time and support the progress of discussions based on their emotions. The advice providing unit, for example, uses the emotion estimation function to monitor the emotions of team members during meetings in real time and support the progress of discussions based on their emotions. For example, an AI player monitors the emotional states of team members in real time during meetings and supports the progress of discussions based on their emotions. For example, if a member feels anxious, the AI ​​player provides feedback to reassure the member. The AI ​​player also analyzes the emotional data of team members during meetings and supports the progress of discussions based on their emotions. For example, if a member seems excited, the AI ​​player provides feedback to calm down. The AI ​​player also grasps the emotional states of team members in real time during meetings and supports the progress of discussions based on their emotions. For example, if a member seems nervous, the AI ​​player provides feedback to relax. This supports the progress of discussions based on the team members' emotions, thereby promoting efficient discussions.

[0068] The advice providing unit can use the emotion estimation function to provide specialized skills and know-how according to the player's emotional state. The advice providing unit, for example, uses the emotion estimation function to provide specialized skills and know-how according to the player's emotional state. For example, the AI ​​player analyzes the player's emotional state and provides specialized skills and know-how according to the emotion. For example, if the player is feeling anxious, it teaches skills to help the player relax. The AI ​​player also monitors the player's emotional state in real time and provides specialized skills and know-how at the appropriate time. For example, if the player is feeling anxious, it teaches techniques to help the player stay calm. The AI ​​player also provides specialized skills and know-how that are optimal for each individual player based on the player's emotional data. For example, it provides optimal advice by referring to the player's past data. In this way, the player's performance can be improved by providing specialized skills and know-how that are appropriate for the player's emotional state.

[0069] The advice providing unit can learn past race data and provide skills and know-how that are optimal for specific situations. The advice providing unit, for example, learns past race data and provides skills and know-how that are optimal for specific situations. For example, the AI ​​player analyzes past race data and provides the optimal skills and know-how for specific situations. For example, it teaches the optimal driving techniques for races in the rain. The AI ​​player also learns the player's past race results and suggests the optimal strategy for specific situations. For example, it provides specific advice on starting to a player who is not good at starting dashes. The AI ​​player also analyzes the player's past mistakes and provides skills and know-how to prevent the player from repeating the same mistakes. For example, it provides specific instructions on braking points at specific corners. In this way, the AI ​​player can improve the player's performance by learning past race data and providing the optimal skills and know-how for specific situations.

[0070] The advice providing unit can simulate different race scenarios and provide the optimal skills and know-how for each scenario. The advice providing unit, for example, simulates different race scenarios and provides the optimal skills and know-how for each scenario. For example, an AI player simulates different race scenarios and provides the optimal skills and know-how for each scenario. For example, teaching techniques for maintaining visibility in night races. The AI ​​player also simulates different weather conditions and provides the optimal skills and know-how for each condition. For example, teaching tire selection and driving techniques for rainy weather. The AI ​​player also simulates different course layouts and provides the optimal skills and know-how for each course. For example, teaching the optimal line to take and braking points for a particular course. In this way, by simulating different race scenarios and providing the optimal skills and know-how for each scenario, the player's performance can be improved.

[0071] The advice providing unit can use visual feedback to provide specialized skills and know-how in a visually easy-to-understand form. The advice providing unit, for example, uses visual feedback to provide specialized skills and know-how in a visually easy-to-understand form. For example, an AI player uses visual feedback to provide specialized skills and know-how in a visually easy-to-understand form. For example, the optimal line for the next corner is displayed. The AI ​​player also uses visual feedback to provide specialized skills and know-how in a visually easy-to-understand form. For example, the timing of a pit stop is visually indicated. The AI ​​player also uses visual feedback to provide specialized skills and know-how in a visually easy-to-understand form. For example, braking points are indicated. In this way, by using visual feedback to provide skills and know-how in a visually easy-to-understand form, it is possible to improve the player's performance.

[0072] The advice providing unit can use the emotion estimation function to provide skills and know-how for improving motivation based on the player's emotions. The advice providing unit, for example, uses the emotion estimation function to provide skills and know-how for improving motivation based on the player's emotions. For example, the AI ​​player analyzes the player's emotional state and provides skills and know-how for improving motivation. For example, if the player is feeling down, it offers words of encouragement. The AI ​​player also monitors the player's emotional state in real time and provides skills and know-how for improving motivation at the appropriate time. For example, if the player is tired, it encourages the player to take a break. The AI ​​player also provides skills and know-how for improving motivation that are optimal for each individual player based on the player's emotional data. For example, it selects words of encouragement by referring to the player's past data. In this way, by providing skills and know-how for improving motivation based on the player's emotions, it is possible to improve the player's performance.

[0073] The advice providing unit can use the emotion estimation function to provide emotional feedback based on the analysis results of the play data. The advice providing unit, for example, uses the emotion estimation function to provide emotional feedback based on the analysis results of the play data. For example, an AI player analyzes the play data and provides emotional feedback using the emotion estimation function. For example, the AI ​​player may offer words of encouragement when the player makes a mistake. Also, the AI ​​player may analyze the play data and provide emotional feedback using the emotion estimation function. For example, the AI ​​player may offer words of praise when the player succeeds. Also, the AI ​​player may analyze the play data and provide emotional feedback using the emotion estimation function. For example, the AI ​​player may encourage the player to take a break if the player is tired. In this way, by providing emotional feedback based on the analysis results of the play data, it is possible to improve the player's performance.

[0074] The advice providing unit can learn past play data and provide feedback according to the player's growth. The advice providing unit, for example, learns past play data and provides feedback according to the player's growth. For example, an AI player analyzes past play data and provides specific feedback according to the player's growth. For example, it proposes strategies based on past success stories. The AI ​​player also learns the player's past performance data and points out areas for improvement according to the player's growth. For example, it proposes specific training methods for players who need to improve a specific skill. The AI ​​player also learns past play data and provides feedback according to the player's growth. For example, it provides specific advice to prevent past mistakes from being repeated. In this way, the player's performance can be improved by learning past play data and providing feedback according to the player's growth.

[0075] The advice providing unit can integrate different data sources to provide comprehensive analysis results. For example, the advice providing unit integrates different data sources to provide comprehensive analysis results. For example, an AI player analyzes play data and integrates different data sources to provide comprehensive analysis results. For example, race data and heart rate data are integrated and analyzed. The AI ​​player also analyzes play data and integrates different data sources to provide comprehensive analysis results. For example, race data and weather data are integrated and analyzed. The AI ​​player also analyzes play data and integrates different data sources to provide comprehensive analysis results. For example, race data and vehicle data are integrated and analyzed. This allows for the integration of different data sources to provide comprehensive analysis results, thereby improving player performance.

[0076] The advice providing unit can provide the analysis results in a visually easy-to-understand format using visual feedback. The advice providing unit, for example, uses visual feedback to provide the analysis results in a visually easy-to-understand format. For example, an AI player analyzes play data and provides the analysis results in a visually easy-to-understand format using visual feedback. For example, a graph of lap times is displayed. Alternatively, the AI ​​player analyzes play data and provides the analysis results in a visually easy-to-understand format using visual feedback. For example, cornering data is displayed in a graph. Alternatively, the AI ​​player analyzes play data and provides the analysis results in a visually easy-to-understand format using visual feedback. For example, braking points are displayed. In this way, by using visual feedback to provide the analysis results in a visually easy-to-understand format, the player's performance can be improved.

[0077] The advice providing unit can integrate data from different sports and fields and provide crossover analysis results. The advice providing unit, for example, integrates data from different sports and fields and provides crossover analysis results. For example, an AI player integrates data from different sports and provides crossover analysis results. For example, soccer data is applied to motorsports. Also, an AI player integrates data from different fields and provides crossover analysis results. For example, aircraft data is applied to motorsports. Also, an AI player integrates data from different sports and fields and provides crossover analysis results. For example, chess data is applied to motorsports. In this way, by integrating data from different sports and fields and providing crossover analysis results, it is possible to improve player performance.

[0078] The advice providing unit can use the emotion estimation function to provide feedback to improve motivation based on the analysis results of the play data. The advice providing unit, for example, uses the emotion estimation function to provide feedback to improve motivation based on the analysis results of the play data. For example, an AI player analyzes the play data and uses the emotion estimation function to provide feedback to improve motivation. For example, when the player makes a mistake, the AI ​​player may offer words of encouragement. Also, the AI ​​player analyzes the play data and uses the emotion estimation function to provide feedback to improve motivation. For example, when the player succeeds, the AI ​​player may offer words of praise. Also, the AI ​​player analyzes the play data and uses the emotion estimation function to provide feedback to improve motivation. For example, if the player is tired, the AI ​​player may encourage the player to take a break. In this way, by providing feedback to improve motivation based on the analysis results of the play data, it is possible to improve the player's performance.

[0079] The advice providing unit can use the emotion estimation function to optimize strategies based on the emotional states of team members. The advice providing unit, for example, uses the emotion estimation function to optimize strategies based on the emotional states of team members. For example, the AI ​​player analyzes the emotional states of team members and optimizes strategies based on their emotions. For example, if a member is feeling anxious, it proposes a strategy to help them relax. The AI ​​player also monitors the emotional states of team members in real time and optimizes strategies based on their emotions. For example, if a member is excited, it proposes a strategy to help them stay calm. The AI ​​player also provides optimal strategies to individual team members based on the emotional data of the team members. For example, it selects the optimal strategy by referring to the member's past data. In this way, by optimizing strategies based on the emotional states of team members, it is possible to improve the performance of the entire team.

[0080] The advice providing unit can learn from past race data and provide the optimal strategy for a specific situation. The advice providing unit, for example, learns from past race data and provides the optimal strategy for a specific situation. For example, an AI player analyzes past race data and provides the optimal strategy for a specific situation. For example, it may suggest the optimal pit stop timing for a race in rainy weather. The AI ​​player also learns the player's past race results and suggests the optimal strategy for a specific situation. For example, it may provide specific advice on the start to a player who is not good at starting dashes. The AI ​​player may also analyze the player's past mistakes and provide a strategy to prevent the player from repeating the same mistake. For example, it may provide specific instructions on braking points at specific corners. In this way, by learning from past race data and providing the optimal strategy for a specific situation, the performance of the entire team can be improved.

[0081] The advice providing unit can simulate different race scenarios and provide the optimal strategy for each scenario. The advice providing unit, for example, simulates different race scenarios and provides the optimal strategy for each scenario. For example, an AI player simulates different race scenarios and provides the optimal strategy for each scenario. For example, teaching techniques for ensuring visibility in night races. Also, an AI player simulates different weather conditions and provides the optimal strategy for each condition. For example, teaching tire selection and driving techniques for rainy weather. Also, an AI player simulates different course layouts and provides the optimal strategy for each course. For example, teaching the optimal line to take and braking points for a particular course. In this way, by simulating different race scenarios and providing the optimal strategy for each scenario, the performance of the entire team can be improved.

[0082] The advice providing unit can integrate knowledge of different sports and fields to provide crossover strategies. The advice providing unit, for example, integrates knowledge of different sports and fields to provide crossover strategies. For example, an AI player integrates knowledge of different sports to provide crossover strategies. For example, soccer tactics are applied to motorsports. Also, an AI player integrates knowledge of different fields to provide crossover strategies. For example, aircraft piloting techniques are applied to motorsports. Also, an AI player integrates knowledge of different sports and fields to provide crossover strategies. For example, chess strategies are applied to motorsports. In this way, by integrating knowledge of different sports and fields to provide crossover strategies, it is possible to improve the performance of the entire team.

[0083] The advice providing unit can use the emotion estimation function to provide strategies for improving motivation based on the emotions of team members. The advice providing unit, for example, uses the emotion estimation function to provide strategies for improving motivation based on the emotions of team members. For example, an AI player analyzes the emotional state of team members and provides strategies for improving motivation. For example, if a member is feeling down, the AI ​​player offers words of encouragement. The AI ​​player also monitors the emotional state of team members in real time and provides strategies for improving motivation at appropriate times. For example, if a member is tired, the AI ​​player encourages them to take a break. The AI ​​player also provides optimal motivation strategies for each team member based on the emotional data of the team members. For example, the AI ​​player selects words of encouragement by referring to the member's past data. In this way, by providing strategies for improving motivation based on the emotions of team members, the performance of the entire team can be improved.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The advice providing unit can support communication in different languages ​​during gameplay, enabling smooth communication even in international teams. For example, an AI player can translate into different languages ​​in real time to support communication between players. For example, it can act as an interpreter between a player speaking Japanese and a player speaking English. The AI ​​player can also provide advice in different languages, enabling international teams to share strategies. For example, it can translate advice in English into Spanish. The AI ​​player can also communicate taking into account different cultural backgrounds, strengthening cooperation among international teams. For example, it can provide appropriate advice after understanding cultural differences. This makes it possible to strengthen cooperation among international teams by supporting communication in different languages.

[0086] The advice providing unit can provide visual feedback during play and display visual advice in real time. For example, the AI ​​player can display visual feedback on the screen while playing to provide advice that is visually easy to understand. For example, it can display the optimal line for the next corner. The AI ​​player can also display visual feedback on the dashboard while playing to share strategies in real time. For example, it can visually indicate the timing of pit stops. The AI ​​player can also provide visual feedback using a head-up display (HUD) while playing to display advice directly in the player's field of vision. For example, it can indicate braking points. In this way, by providing visual feedback, the player can receive advice in a form that is visually easy to understand.

[0087] The advice providing unit can use the emotion estimation function to provide messages to improve motivation according to the player's emotions. For example, the AI ​​player analyzes the player's emotional state and provides messages to improve motivation. For example, if the player is feeling down, it will offer words of encouragement. The AI ​​player can also monitor the player's emotional state in real time and provide motivational messages at the appropriate time. For example, if the player is tired, it will encourage the player to take a break. The AI ​​player can also provide optimal motivational messages for each individual player based on the player's emotional data. For example, it can select words of encouragement based on the player's past data. This makes it possible to optimize the player's performance by providing motivational messages according to the player's emotions.

[0088] The advice providing unit generates a summary of the discussion in real time during a meeting, which can promote efficient discussions. For example, the AI ​​player generates a summary of the discussion in real time during a meeting and shares it with team members. For example, it instantly summarizes and displays important points and decisions. The AI ​​player also automatically extracts the key points of the discussion during a meeting and promotes efficient discussions. For example, it provides a summary to help the discussion progress smoothly. The AI ​​player also generates a summary of the discussion in real time during a meeting and saves it for later reference. For example, it shares the summary by email after the meeting ends. In this way, efficient discussions can be promoted by generating a summary of the discussion in real time.

[0089] The advice providing unit can use the emotion estimation function to monitor the emotions of team members during meetings in real time and support the progress of discussions based on their emotions. For example, an AI player can monitor the emotional state of team members in real time during meetings and support the progress of discussions based on their emotions. For example, if a member feels anxious, it can provide feedback to reassure them. The AI ​​player can also analyze the emotional data of team members during meetings and support the progress of discussions based on their emotions. For example, if a member seems excited, it can provide feedback to calm down. The AI ​​player can also grasp the emotional state of team members in real time during meetings and support the progress of discussions based on their emotions. For example, if a member seems nervous, it can provide feedback to relax. This can promote efficient discussions by supporting the progress of discussions based on the team members' emotions.

[0090] The advice provider can study past race data and provide the optimal skills and know-how for specific situations. For example, an AI player can analyze past race data and provide the optimal skills and know-how for specific situations. For example, it can teach the optimal driving techniques for races in the rain. The AI ​​player can also study the player's past race results and suggest the optimal strategy for specific situations. For example, it can provide specific advice on starting to a player who is not good at starting dashes. The AI ​​player can also analyze the player's past mistakes and provide the skills and know-how to prevent the player from repeating the same mistakes. For example, it can provide specific instructions on braking points at specific corners. In this way, the AI ​​player can improve the player's performance by learning past race data and providing the optimal skills and know-how for specific situations.

[0091] The advice providing unit can use the emotion estimation function to provide emotional feedback based on the analysis results of the play data. For example, an AI player analyzes the play data and provides emotional feedback using the emotion estimation function. For example, when the player makes a mistake, the AI ​​player may offer words of encouragement. Also, the AI ​​player analyzes the play data and provides emotional feedback using the emotion estimation function. For example, when the player succeeds, the AI ​​player may offer words of praise. Also, the AI ​​player analyzes the play data and provides emotional feedback using the emotion estimation function. For example, when the player is tired, the AI ​​player may encourage the player to take a break. In this way, by providing emotional feedback based on the analysis results of the play data, the player's performance can be improved.

[0092] The advice providing unit can simulate different race scenarios and provide the optimal skills and know-how for each scenario. For example, an AI player can simulate different race scenarios and provide the optimal skills and know-how for each scenario. For example, it can teach techniques for maintaining visibility in night races. Alternatively, the AI ​​player can simulate different weather conditions and provide the optimal skills and know-how for each condition. For example, it can teach tire selection and driving techniques for rainy weather. Alternatively, the AI ​​player can simulate different course layouts and provide the optimal skills and know-how for each course. For example, it can teach the optimal line to take and braking points for a particular course. In this way, by simulating different race scenarios and providing the optimal skills and know-how for each scenario, the player's performance can be improved.

[0093] The advice provider can use the emotion estimation function to optimize strategies based on the emotional states of team members. For example, the AI ​​player analyzes the emotional states of team members and optimizes strategies based on their emotions. For example, if a member is feeling anxious, it will suggest a strategy to help them relax. The AI ​​player can also monitor the emotional states of team members in real time and optimize strategies based on their emotions. For example, if a member is excited, it will suggest a strategy to help them stay calm. The AI ​​player can also provide the optimal strategy for each team member based on their emotional data. For example, it can select the optimal strategy by referring to the member's past data. In this way, by optimizing strategies based on the emotional states of team members, it is possible to improve the performance of the entire team.

[0094] The advice provider can integrate knowledge from different sports and fields to provide crossover strategies. For example, an AI player can integrate knowledge from different sports to provide crossover strategies. For example, soccer tactics can be applied to motorsports. Alternatively, an AI player can integrate knowledge from different fields to provide crossover strategies. For example, aircraft piloting techniques can be applied to motorsports. Alternatively, an AI player can integrate knowledge from different sports and fields to provide crossover strategies. For example, chess strategies can be applied to motorsports. In this way, by integrating knowledge from different sports and fields to provide crossover strategies, it is possible to improve the performance of the entire team.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The AI ​​player uses the generative AI to communicate with the human player in real time using a natural language processing unit. For example, the AI ​​player uses the generative AI to generate appropriate answers to the player's questions. The AI ​​player can also use the generative AI to analyze the player's comments and provide appropriate advice. Step 2: Generative AI uses large-scale language models such as GPT-3 and BERT, which gives the AI ​​player advanced natural language generation capabilities and allows it to communicate smoothly with players. Step 3: The natural language processing unit analyzes the text generated by the generation AI and facilitates communication with the player. For example, the natural language processing unit performs morphological and grammatical analysis to accurately understand the meaning of the player's statements. The natural language processing unit can also perform semantic analysis to understand the player's intentions. Step 4: The advice provider uses the generative AI to provide strategic and tactical advice. For example, the advice provider may provide advice to the player on how to attack the next curve or on braking points. The advice provider may also provide advice based on the player's emotional state.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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]

[0164] 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. AI player and Generative AI and a natural language processing unit; an advice providing unit; The AI ​​player: Using the generation AI, the natural language processing unit communicates with a human player in real time, The advice providing department provides strategic and tactical advice A system characterized by:

2. The advice providing unit Support communication in different languages ​​during play, allowing smooth communication even among international teams The system of claim 1 .

3. The advice providing unit Providing visual feedback during play and displaying visual advice in real time The system of claim 1 .

4. The advice providing unit Learn from past meeting data and provide advice as your team grows The system of claim 1 .

5. The advice providing unit Providing specialized skills and know-how in response to the emotional state of said players The system of claim 1 .

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