system

Abstract

The system according to this embodiment aims to provide the analysis results of a shogi AI in a format that is easy for humans to understand. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the analysis results of the Shogi AI. The analysis unit analyzes the analysis results collected by the collection unit. The generation unit generates an explanation based on the results analyzed by the analysis unit. The provision unit provides the explanation generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for humans to understand the analysis results of shogi AI, and it is particularly difficult for beginners to effectively utilize the analysis results.

[0005] The system according to the embodiment aims to provide the analysis results of shogi AI in a form that is easy for humans to understand.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the analysis results of the Shogi AI. The analysis unit analyzes the analysis results collected by the collection unit. The generation unit generates an explanation based on the results analyzed by the analysis unit. The provision unit provides the explanation generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide the analysis results of the Shogi AI in a format that is easy for humans to understand. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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] [[ID=二十一]] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Shogi AI support system according to an embodiment of the present invention is a system that uses Shogi AI to support the improvement of human Shogi skills. This Shogi AI support system automatically analyzes the numerical values ​​and reasons for candidate moves indicated by the AI ​​and outputs them in a human-readable format. This allows a wide range of people, from professional players to beginners, to efficiently utilize Shogi AI. Furthermore, because Shogi has its own unique usage of specialized terminology and expressions, Shogi books and game commentaries are used as a database to improve the accuracy of explanations. In addition, it can help ordinary people who do not have a good understanding of Shogi to understand the game. It can also be introduced into broadcasts of professional Shogi matches on television stations, enabling broadcasts that are easy for viewers to understand even when there is no commentator. First, the Shogi AI indicates the state of the game and the numerical values ​​for the best move. Next, it automatically analyzes those numerical values ​​and reasons for candidate moves and outputs them in a human-readable format. For example, it provides a detailed explanation of the reasons for the best move indicated by the AI, explaining why that move is good. This makes it easier for users to understand the AI's analysis results. Furthermore, the accuracy of explanations is improved by using specialized Shogi terminology and expressions as a database. For example, it uses shogi books and game commentaries as a database to clearly explain the numerical values ​​and reasons behind candidate moves indicated by the AI. Furthermore, it can be introduced to broadcasts of professional shogi matches on television stations, enabling broadcasts that are easy for viewers to understand even when there are no commentators. For instance, by explaining in detail the reasons behind the AI's best move and explaining why that move is good, viewers can more easily understand the content of the match. In this way, the shogi AI support system can help users understand the analysis results of the shogi AI and support the improvement of their shogi skills.

[0029] The Shogi AI support system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the analysis results of the Shogi AI. The collection unit collects, for example, the positional status and numerical values ​​of the best move indicated by the Shogi AI. The collection unit can collect numerical data such as evaluation values ​​and win rates indicated by the Shogi AI. The collection unit collects, for example, the numerical values ​​of the best move indicated by the Shogi AI and provides them to the analysis unit. The analysis unit analyzes the analysis results collected by the collection unit. The analysis unit analyzes, for example, the collected numerical values ​​and the reasons for candidate moves. The analysis unit analyzes the evaluation values ​​and reasons for candidate moves indicated by the Shogi AI and provides the results to the generation unit. The generation unit generates an explanation based on the results analyzed by the analysis unit. The generation unit generates an explanation in a human-readable format based on the analysis results. The generation unit uses Shogi terminology and expressions as a database to improve the accuracy of the explanation. The generation unit uses, for example, shogi books and game commentaries as a database to explain the analysis results in an easy-to-understand manner. The provision unit provides the explanation generated by the generation unit. The provision unit provides the generated explanation to the user, for example. The provision unit can provide the generated explanation in the form of screen display or audio output. As a result, the shogi AI support system according to this embodiment can make it easier for the user to understand the analysis results of the shogi AI and support the improvement of their shogi skills.

[0030] The data collection unit collects the analysis results of the Shogi AI. Specifically, it collects numerical values ​​for the positional advantage and best move as indicated by the Shogi AI. For example, the Shogi AI displays a numerical evaluation value for the current position, with a positive value indicating an advantage for the first player and a negative value indicating an advantage for the second player. The data collection unit acquires this evaluation value in real time and stores it in a database. It also collects numerical values ​​for the best move indicated by the Shogi AI. The best move is the move considered most advantageous in the current position, and collecting this numerical value provides reference information when selecting the next move. Furthermore, the data collection unit also collects numerical data such as the win rate indicated by the Shogi AI. The win rate indicates the probability of winning from the current position, and collecting this allows for a more concrete understanding of the advantage or disadvantage of the position. The data collection unit centrally manages this data and provides it to the analysis unit. The collected data is stored on a cloud server and made accessible to the analysis unit. This allows the data collection unit to efficiently and accurately collect the analysis results of the Shogi AI and improve the overall system performance.

[0031] The analysis unit analyzes the analysis results collected by the collection unit. Specifically, it analyzes the collected numerical values ​​and the reasons for candidate moves. For example, it analyzes the evaluation value shown by the Shogi AI and analyzes what factors determined that evaluation value. The evaluation value is a comprehensive evaluation of the position of the pieces, the value of the pieces, the progress of the game, etc., and the analysis unit analyzes these factors in detail. It also analyzes the reasons for candidate moves. Candidate moves are multiple possible moves in the current position, and the analysis unit analyzes why each move was chosen. For example, it analyzes whether a move is offensive, defensive, or aimed at exchanging pieces. Furthermore, the analysis unit analyzes the evaluation value shown by the Shogi AI and the reasons for candidate moves, and provides the results to the generation unit. The analysis unit can also use past game data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to quickly and accurately analyze the collected data and provide information to gain a deeper understanding of the Shogi AI's analysis results.

[0032] The generation unit generates explanations based on the results analyzed by the analysis unit. Specifically, it generates explanations in a human-readable format based on the analysis results. For example, it explains the evaluation values ​​and reasons for candidate moves shown by the Shogi AI using Shogi terminology and expressions. The generation unit uses Shogi terminology and expressions as a database to improve the accuracy of the explanations. For example, it generates specific explanations such as, "In this position, it is effective to launch an attack using the rook." The generation unit also uses Shogi books and game commentaries as a database to explain the analysis results in an easy-to-understand manner. This allows the generation unit to generate explanations that are easy for users without specialized knowledge of Shogi to understand. Furthermore, the generation unit can customize the generated explanations according to the user's level. For example, it can provide basic explanations for beginners and detailed explanations of strategies and tactics for advanced players. This allows the generation unit to deepen the user's understanding and support the improvement of their Shogi skills.

[0033] The providing unit provides explanations generated by the generating unit. Specifically, it provides the generated explanations to the user. For example, the generated explanations can be provided in the form of screen display or audio output. In the case of screen display, the analysis results are displayed next to the game screen so that the user can check the analysis results in real time during a game. In the case of audio output, the explanation is provided in audio so that the user can check the analysis results while concentrating on the game. For example, a specific explanation such as "In this position, it is effective to launch an attack using the rook" is provided in audio. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the explanation content. For example, it collects feedback on parts that the user found difficult to understand and revises the explanation content based on that feedback. In addition, the providing unit can reliably transmit information using multiple communication methods. For example, it can provide analysis results not only through smartphone notifications but also through email and social media. In this way, the providing unit can quickly and reliably provide analysis results to the user and support the improvement of their chess skills.

[0034] The data collection unit can collect numerical values ​​for the position and best move shown by the Shogi AI. For example, the data collection unit collects the position as an evaluation value. The data collection unit collects numerical values ​​for the best move shown by the Shogi AI and provides them to the analysis unit. For example, the data collection unit can collect numerical data such as evaluation values ​​and win rates shown by the Shogi AI. The data collection unit collects numerical values ​​for the best move shown by the Shogi AI and provides them to the analysis unit. In this way, basic data for analysis can be obtained by collecting numerical values ​​for the position and best move shown by the Shogi AI. The position is evaluated using specific evaluation criteria and methods such as evaluation values ​​and win rates. The numerical values ​​for the best move are calculated using specific content and calculation methods such as evaluation values ​​and number of moves. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the evaluation values ​​and numerical values ​​for the best move shown by the Shogi AI into an AI model to collect basic data for analysis.

[0035] The analysis unit can analyze the numerical data and reasons for candidate moves collected by the collection unit. For example, the analysis unit analyzes the collected numerical data and provides the results to the generation unit. The analysis unit analyzes the evaluation values ​​and reasons for candidate moves shown by the Shogi AI and provides the results to the generation unit. For example, the analysis unit analyzes the collected numerical data and reasons for candidate moves and provides the results to the generation unit. In this way, analysis results can be obtained by analyzing the collected numerical data and reasons for candidate moves. The numerical data is analyzed using specific types and ranges, such as evaluation values ​​and number of moves. The reasons for candidate moves are analyzed using specific content and explanation methods, such as strategic reasons and tactical reasons. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected numerical data and reasons for candidate moves into an AI model and obtain analysis results.

[0036] The generation unit can generate explanations in a human-readable format based on the results analyzed by the analysis unit. For example, the generation unit generates explanations in a human-readable format based on the analysis results. The generation unit uses a database of shogi terminology and expressions to improve the accuracy of the explanations. For example, the generation unit uses a database of shogi books and game commentaries to explain the analysis results in an easy-to-understand way. The generation unit generates explanations in a human-readable format based on the analysis results. This allows information to be provided in a way that is easy for users to understand by generating explanations based on the analysis results. Human-readable text is generated using specific criteria and examples, such as limiting the use of technical terms and using concise expressions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the analysis results into a generation AI and generate explanations in a human-readable format.

[0037] The providing unit can provide the user with the explanation generated by the generating unit. The providing unit can, for example, provide the generated explanation to the user. The providing unit can provide the generated explanation in the form of screen display or audio output. The providing unit can, for example, provide the user with the generated explanation. In this way, by providing the user with the generated explanation, the user can receive the information. The provision is carried out using specific methods and formats such as screen display, audio output, or printing. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the generated explanation into an AI model and provide it to the user.

[0038] The generation unit can improve the accuracy of its explanations by using a database of shogi terminology and expressions. The generation unit can, for example, use shogi books and game commentaries as a database to explain the analysis results in an easy-to-understand manner. The generation unit can improve the accuracy of its explanations by using a database of shogi terminology and expressions. The generation unit can, for example, use a database of shogi books and game commentaries to explain the analysis results in an easy-to-understand manner. This improves the accuracy of the explanations by using shogi terminology and expressions. Shogi terminology and expressions are used, for example, in the form of specific lists or usage criteria such as opening theory and tactical moves. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input shogi terminology and expressions into a generation AI to improve the accuracy of its explanations.

[0039] The data collection unit can dynamically change the collection frequency in response to changes in the game state when collecting analysis results from the Shogi AI. For example, the collection unit can increase the collection frequency when the game state changes significantly. The collection unit can decrease the collection frequency when the game state is stable. The collection unit can increase the collection frequency as the game approaches the endgame. This allows for efficient data collection by changing the collection frequency in response to changes in the game state. Changes in the game state are detected using specific criteria and detection methods, such as changes in evaluation values ​​or the progress of moves. Some or all of the above-described processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the analysis results from the Shogi AI into an AI model and dynamically change the collection frequency in response to changes in the game state.

[0040] The data collection unit can enable more detailed analysis by including the players' past game data in the data it collects. For example, the data collection unit collects the players' past game data and compares it with the current position. The data collection unit can extract specific patterns from the players' past game data. The data collection unit can predict the current position based on the players' past game data. This enables detailed analysis by including the players' past game data. The players' past game data is collected using specific details and collection methods, such as the date and time of the game, the game result, and the tactics used. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the players' past game data into an AI model and perform a detailed analysis.

[0041] The data collection unit can improve the accuracy of its analysis by including the players' playing style and tactical tendencies in the data it collects. For example, the data collection unit analyzes the players' playing style and selects the data to collect. The data collection unit can adjust the data to collect based on the players' tactical tendencies. The data collection unit can optimize the data to collect by referring to the players' past tactics. This improves the accuracy of the analysis by including the players' playing style and tactical tendencies. Playing style and tactical tendencies are analyzed using specific content and analysis methods, such as offensive or defensive play styles and the frequency of use of specific tactics. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the players' playing style and tactical tendencies into an AI model to improve the accuracy of the analysis.

[0042] The data collection unit can provide a multifaceted perspective for analysis by including the players' real-time heart rate and stress levels in the data it collects. For example, the data collection unit can collect the players' heart rates in real time and incorporate them into the analysis. The data collection unit can measure the players' stress levels and use them in the analysis. The data collection unit can supplement the analysis results based on the players' physiological data. This allows for a multifaceted perspective for analysis by including the players' physiological data. Heart rate and stress levels are measured using specific measurement methods and criteria, such as heart rate monitors and stress assessment scales. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the players' heart rates and stress levels into an AI model to provide a multifaceted perspective for analysis.

[0043] The analysis unit can dynamically change its analysis algorithm in response to changes in the game state during analysis. For example, the analysis unit changes the analysis algorithm when the game state changes significantly. The analysis unit can simplify the analysis algorithm when the game state is stable. The analysis unit can strengthen the analysis algorithm as the game approaches its endgame. This allows for efficient analysis by changing the analysis algorithm in response to changes in the game state. The analysis algorithm is changed using specific types and methods, such as changing the evaluation function or adjusting the search depth. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the analysis algorithm into an AI model in response to changes in the game state and dynamically change it.

[0044] The analysis unit can perform a more detailed analysis by including the players' past game data in the data being analyzed. For example, the analysis unit includes the players' past game data in the analysis. The analysis unit can extract specific patterns from the players' past game data. The analysis unit can predict the current position based on the players' past game data. This makes a detailed analysis possible by including the players' past game data. Past game data is collected using specific details and collection methods, such as the date and time of the game, the game result, and the tactics used. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the players' past game data into an AI model and perform a detailed analysis.

[0045] The analysis unit can improve the accuracy of its analysis by including the players' playing style and tactical tendencies in the data it analyzes. For example, the analysis unit can analyze the players' playing style and reflect it in the analysis. The analysis unit can adjust the analysis based on the players' tactical tendencies. The analysis unit can optimize the analysis by referring to the players' past tactics. This improves the accuracy of the analysis by including the players' playing style and tactical tendencies. Playing style and tactical tendencies are analyzed using specific content and analysis methods, such as offensive or defensive styles and the frequency of use of specific tactics. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the players' playing style and tactical tendencies into an AI model to improve the accuracy of the analysis.

[0046] The analysis unit can provide a multifaceted perspective on the analysis by including the players' real-time heart rate and stress levels in the data being analyzed. For example, the analysis unit can reflect the players' heart rate in the analysis in real time. The analysis unit can measure the players' stress levels and use them in the analysis. The analysis unit can supplement the analysis results based on the players' physiological data. This allows for a multifaceted perspective on the analysis by including the players' physiological data. Heart rate and stress levels are measured using specific measurement methods and criteria, such as heart rate monitors and stress assessment scales. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the players' heart rate and stress levels into an AI model to provide a multifaceted perspective on the analysis.

[0047] The generation unit can include elements in the generated explanation that are dynamically modified in response to changes in the game situation. For example, the generation unit can change the content of the explanation if the game situation changes significantly. If the game situation is stable, the generation unit can simplify the content of the explanation. As the game approaches the endgame, the generation unit can strengthen the content of the explanation. This allows for the provision of appropriate information by dynamically modifying the explanation in response to changes in the game situation. The elements that are dynamically modified are changed using specific content and modification criteria, such as the evaluation value of the game situation or the progress of moves. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the explanation into the generation AI in response to changes in the game situation and dynamically modify it.

[0048] The generation unit can provide more detailed explanations by including the players' past game data in the explanations it generates. For example, the generation unit includes the players' past game data in the explanation. The generation unit can extract specific patterns from the players' past game data. The generation unit can predict the current position based on the players' past game data. This makes it possible to provide detailed explanations by including the players' past game data. Past game data is collected using specific details and collection methods, such as the date and time of the game, the game result, and the tactics used. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the players' past game data into a generation AI to provide a detailed explanation.

[0049] The generation unit can improve the accuracy of its explanations by including the players' playing style and tactical tendencies in the explanations it generates. For example, the generation unit can analyze the players' playing style and reflect it in the explanation. The generation unit can adjust the explanation based on the players' tactical tendencies. The generation unit can optimize the explanation by referring to the players' past tactics. This improves the accuracy of the explanation by including the players' playing style and tactical tendencies. Playing style and tactical tendencies are analyzed using specific content and analysis methods, such as offensive or defensive play styles and the frequency of use of specific tactics. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the players' playing style and tactical tendencies into a generation AI to improve the accuracy of the explanation.

[0050] The generation unit can provide a multifaceted perspective on the explanation by including the players' real-time heart rate and stress levels in the explanation it generates. For example, the generation unit can reflect the players' heart rate in the explanation in real time. The generation unit can measure the players' stress levels and use them in the explanation. The generation unit can supplement the explanation results based on the players' physiological data. This allows for a multifaceted perspective on the explanation by including the players' physiological data. Heart rate and stress levels are measured using specific measurement methods and criteria, such as heart rate monitors and stress assessment scales. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the players' heart rate and stress levels into a generation AI to provide a multifaceted perspective on the explanation.

[0051] The provider may include elements in the explanations it provides that can be dynamically modified in response to changes in the game situation. For example, the provider may change the content of the explanation if the game situation changes significantly. The provider may simplify the content of the explanation if the game situation is stable. The provider may strengthen the content of the explanation as the game approaches its endgame. This allows for the provision of appropriate information by dynamically modifying the explanation in response to changes in the game situation. The elements that are dynamically modified may be changed using specific content or modification criteria, such as the evaluation value of the game situation or the progress of moves. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider may input the explanation into an AI model in response to changes in the game situation and modify it dynamically.

[0052] The service provider can provide more detailed explanations by including the players' past game data in the explanations it provides. For example, the service provider can include the players' past game data in the explanations. The service provider can extract specific patterns from the players' past game data. The service provider can predict the current position based on the players' past game data. This makes it possible to provide detailed explanations by including the players' past game data. Past game data is collected using specific content and collection methods, such as the date and time of the game, the result of the game, and the tactics used. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the players' past game data into an AI model to provide detailed explanations.

[0053] The service provider can improve the accuracy of its explanations by including the players' playing style and tactical tendencies. For example, the service provider can analyze the players' playing style and reflect it in the explanation. The service provider can adjust the explanation based on the players' tactical tendencies. The service provider can optimize the explanation by referring to the players' past tactics. This improves the accuracy of the explanation by including the players' playing style and tactical tendencies. Playing style and tactical tendencies are analyzed using specific content and analysis methods, such as offensive or defensive play styles and the frequency of use of specific tactics. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the players' playing style and tactical tendencies into an AI model to improve the accuracy of the explanation.

[0054] The service provider can offer a multifaceted perspective on the explanation by including the players' real-time heart rate and stress levels in the explanation. For example, the service provider can reflect the players' heart rate in the explanation in real time. The service provider can measure the players' stress levels and use them in the explanation. The service provider can supplement the explanation results based on the players' physiological data. This allows for a multifaceted perspective on the explanation by including the players' physiological data. Heart rate and stress levels are measured using specific measurement methods and criteria, such as heart rate monitors and stress assessment scales. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the players' heart rate and stress levels into an AI model to provide a multifaceted perspective on the explanation.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The Shogi AI support system can collect and analyze the user's past game data to help improve their Shogi skills. For example, the collection unit collects the user's past game data and provides it to the analysis unit. The analysis unit analyzes the collected past game data to identify the user's strengths and weaknesses. The generation unit generates specific advice to help the user overcome their weaknesses based on the analysis results. The delivery unit provides the generated advice to the user, enabling them to efficiently improve their Shogi skills. This makes it possible to provide individually optimized instruction by utilizing the user's past game data.

[0057] The Shogi AI support system can collect and analyze the player's real-time heart rate and stress level. For example, the collection unit collects the player's heart rate in real time and provides it to the analysis unit. The analysis unit analyzes the collected heart rate data and estimates the player's stress level. The generation unit generates advice based on the analysis results, tailored to the player's stress level. The delivery unit provides the generated advice to the player, enabling them to manage their stress levels while playing the game. This allows for more appropriate advice by utilizing the player's physiological data.

[0058] The Shogi AI support system can perform more detailed analysis by collecting past game data of the players and comparing it with the current position. For example, the collection unit collects past game data of the players and provides it to the analysis unit. The analysis unit analyzes the collected past game data and compares it with the current position. The generation unit generates specific advice for the current position based on the analysis results. The provision unit provides the generated advice to the players, enabling them to proceed with the game by utilizing their past experience. In this way, more detailed analysis becomes possible by utilizing the players' past game data.

[0059] The Shogi AI support system can dynamically change its analysis algorithm in response to changes in the game situation. For example, the analysis unit changes its analysis algorithm when the game situation changes significantly. If the game situation is stable, the analysis algorithm can be simplified. As the game approaches the endgame, the analysis algorithm can be strengthened. This allows for efficient analysis by changing the analysis algorithm in response to changes in the game situation. The analysis algorithm is changed using specific types and methods, such as changing the evaluation function or adjusting the search depth.

[0060] The Shogi AI support system can collect and analyze the playing style and tactical tendencies of players. For example, the collection unit analyzes the players' playing style and selects the data to collect. The analysis unit analyzes the collected playing style data and identifies the players' tactical tendencies. The generation unit generates specific advice on the players' tactics based on the analysis results. The provision unit provides the generated advice to the players, enabling them to proceed with the game while understanding the tactical tendencies. This allows for more detailed analysis by utilizing the players' playing style and tactical tendencies.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects the analysis results from the Shogi AI. For example, it collects numerical data such as the positional status of the game, the best move value, evaluation value, and win rate as indicated by the Shogi AI. Step 2: The analysis unit analyzes the analysis results collected by the collection unit. For example, it analyzes the collected numerical values ​​and the reasons for candidate moves, and analyzes the evaluation values ​​and reasons for candidate moves shown by the Shogi AI. Step 3: The generation unit generates an explanation based on the results analyzed by the analysis unit. For example, it generates an explanation in a human-readable format based on the analysis results, and uses a database of shogi terminology and expressions to improve the accuracy of the explanation. Step 4: The providing unit provides the explanation generated by the generating unit. For example, it provides the generated explanation to the user in the form of screen display or audio output.

[0063] (Example of form 2) The Shogi AI support system according to an embodiment of the present invention is a system that uses Shogi AI to support the improvement of human Shogi skills. This Shogi AI support system automatically analyzes the numerical values ​​and reasons for candidate moves indicated by the AI ​​and outputs them in a human-readable format. This allows a wide range of people, from professional players to beginners, to efficiently utilize Shogi AI. Furthermore, because Shogi has its own unique usage of specialized terminology and expressions, Shogi books and game commentaries are used as a database to improve the accuracy of explanations. In addition, it can help ordinary people who do not have a good understanding of Shogi to understand the game. It can also be introduced into broadcasts of professional Shogi matches on television stations, enabling broadcasts that are easy for viewers to understand even when there is no commentator. First, the Shogi AI indicates the state of the game and the numerical values ​​for the best move. Next, it automatically analyzes those numerical values ​​and reasons for candidate moves and outputs them in a human-readable format. For example, it provides a detailed explanation of the reasons for the best move indicated by the AI, explaining why that move is good. This makes it easier for users to understand the AI's analysis results. Furthermore, the accuracy of explanations is improved by using specialized Shogi terminology and expressions as a database. For example, it uses shogi books and game commentaries as a database to clearly explain the numerical values ​​and reasons behind candidate moves indicated by the AI. Furthermore, it can be introduced to broadcasts of professional shogi matches on television stations, enabling broadcasts that are easy for viewers to understand even when there are no commentators. For instance, by explaining in detail the reasons behind the AI's best move and explaining why that move is good, viewers can more easily understand the content of the match. In this way, the shogi AI support system can help users understand the analysis results of the shogi AI and support the improvement of their shogi skills.

[0064] The Shogi AI support system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the analysis results of the Shogi AI. The collection unit collects, for example, the positional status and numerical values ​​of the best move indicated by the Shogi AI. The collection unit can collect numerical data such as evaluation values ​​and win rates indicated by the Shogi AI. The collection unit collects, for example, the numerical values ​​of the best move indicated by the Shogi AI and provides them to the analysis unit. The analysis unit analyzes the analysis results collected by the collection unit. The analysis unit analyzes, for example, the collected numerical values ​​and the reasons for candidate moves. The analysis unit analyzes the evaluation values ​​and reasons for candidate moves indicated by the Shogi AI and provides the results to the generation unit. The generation unit generates an explanation based on the results analyzed by the analysis unit. The generation unit generates an explanation in a human-readable format based on the analysis results. The generation unit uses Shogi terminology and expressions as a database to improve the accuracy of the explanation. The generation unit uses, for example, shogi books and game commentaries as a database to explain the analysis results in an easy-to-understand manner. The provision unit provides the explanation generated by the generation unit. The provision unit provides the generated explanation to the user, for example. The provision unit can provide the generated explanation in the form of screen display or audio output. As a result, the shogi AI support system according to this embodiment can make it easier for the user to understand the analysis results of the shogi AI and support the improvement of their shogi skills.

[0065] The data collection unit collects the analysis results of the Shogi AI. Specifically, it collects numerical values ​​for the positional advantage and best move as indicated by the Shogi AI. For example, the Shogi AI displays a numerical evaluation value for the current position, with a positive value indicating an advantage for the first player and a negative value indicating an advantage for the second player. The data collection unit acquires this evaluation value in real time and stores it in a database. It also collects numerical values ​​for the best move indicated by the Shogi AI. The best move is the move considered most advantageous in the current position, and collecting this numerical value provides reference information when selecting the next move. Furthermore, the data collection unit also collects numerical data such as the win rate indicated by the Shogi AI. The win rate indicates the probability of winning from the current position, and collecting this allows for a more concrete understanding of the advantage or disadvantage of the position. The data collection unit centrally manages this data and provides it to the analysis unit. The collected data is stored on a cloud server and made accessible to the analysis unit. This allows the data collection unit to efficiently and accurately collect the analysis results of the Shogi AI and improve the overall system performance.

[0066] The analysis unit analyzes the analysis results collected by the collection unit. Specifically, it analyzes the collected numerical values ​​and the reasons for candidate moves. For example, it analyzes the evaluation value shown by the Shogi AI and analyzes what factors determined that evaluation value. The evaluation value is a comprehensive evaluation of the position of the pieces, the value of the pieces, the progress of the game, etc., and the analysis unit analyzes these factors in detail. It also analyzes the reasons for candidate moves. Candidate moves are multiple possible moves in the current position, and the analysis unit analyzes why each move was chosen. For example, it analyzes whether a move is offensive, defensive, or aimed at exchanging pieces. Furthermore, the analysis unit analyzes the evaluation value shown by the Shogi AI and the reasons for candidate moves, and provides the results to the generation unit. The analysis unit can also use past game data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to quickly and accurately analyze the collected data and provide information to gain a deeper understanding of the Shogi AI's analysis results.

[0067] The generation unit generates explanations based on the results analyzed by the analysis unit. Specifically, it generates explanations in a human-readable format based on the analysis results. For example, it explains the evaluation values ​​and reasons for candidate moves shown by the Shogi AI using Shogi terminology and expressions. The generation unit uses Shogi terminology and expressions as a database to improve the accuracy of the explanations. For example, it generates specific explanations such as, "In this position, it is effective to launch an attack using the rook." The generation unit also uses Shogi books and game commentaries as a database to explain the analysis results in an easy-to-understand manner. This allows the generation unit to generate explanations that are easy for users without specialized knowledge of Shogi to understand. Furthermore, the generation unit can customize the generated explanations according to the user's level. For example, it can provide basic explanations for beginners and detailed explanations of strategies and tactics for advanced players. This allows the generation unit to deepen the user's understanding and support the improvement of their Shogi skills.

[0068] The providing unit provides explanations generated by the generating unit. Specifically, it provides the generated explanations to the user. For example, the generated explanations can be provided in the form of screen display or audio output. In the case of screen display, the analysis results are displayed next to the game screen so that the user can check the analysis results in real time during a game. In the case of audio output, the explanation is provided in audio so that the user can check the analysis results while concentrating on the game. For example, a specific explanation such as "In this position, it is effective to launch an attack using the rook" is provided in audio. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the explanation content. For example, it collects feedback on parts that the user found difficult to understand and revises the explanation content based on that feedback. In addition, the providing unit can reliably transmit information using multiple communication methods. For example, it can provide analysis results not only through smartphone notifications but also through email and social media. In this way, the providing unit can quickly and reliably provide analysis results to the user and support the improvement of their chess skills.

[0069] The data collection unit can collect numerical values ​​for the position and best move shown by the Shogi AI. For example, the data collection unit collects the position as an evaluation value. The data collection unit collects numerical values ​​for the best move shown by the Shogi AI and provides them to the analysis unit. For example, the data collection unit can collect numerical data such as evaluation values ​​and win rates shown by the Shogi AI. The data collection unit collects numerical values ​​for the best move shown by the Shogi AI and provides them to the analysis unit. In this way, basic data for analysis can be obtained by collecting numerical values ​​for the position and best move shown by the Shogi AI. The position is evaluated using specific evaluation criteria and methods such as evaluation values ​​and win rates. The numerical values ​​for the best move are calculated using specific content and calculation methods such as evaluation values ​​and number of moves. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the evaluation values ​​and numerical values ​​for the best move shown by the Shogi AI into an AI model to collect basic data for analysis.

[0070] The analysis unit can analyze the numerical data and reasons for candidate moves collected by the collection unit. For example, the analysis unit analyzes the collected numerical data and provides the results to the generation unit. The analysis unit analyzes the evaluation values ​​and reasons for candidate moves shown by the Shogi AI and provides the results to the generation unit. For example, the analysis unit analyzes the collected numerical data and reasons for candidate moves and provides the results to the generation unit. In this way, analysis results can be obtained by analyzing the collected numerical data and reasons for candidate moves. The numerical data is analyzed using specific types and ranges, such as evaluation values ​​and number of moves. The reasons for candidate moves are analyzed using specific content and explanation methods, such as strategic reasons and tactical reasons. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected numerical data and reasons for candidate moves into an AI model and obtain analysis results.

[0071] The generation unit can generate explanations in a human-readable format based on the results analyzed by the analysis unit. For example, the generation unit generates explanations in a human-readable format based on the analysis results. The generation unit uses a database of shogi terminology and expressions to improve the accuracy of the explanations. For example, the generation unit uses a database of shogi books and game commentaries to explain the analysis results in an easy-to-understand way. The generation unit generates explanations in a human-readable format based on the analysis results. This allows information to be provided in a way that is easy for users to understand by generating explanations based on the analysis results. Human-readable text is generated using specific criteria and examples, such as limiting the use of technical terms and using concise expressions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the analysis results into a generation AI and generate explanations in a human-readable format.

[0072] The providing unit can provide the user with the explanation generated by the generating unit. The providing unit can, for example, provide the generated explanation to the user. The providing unit can provide the generated explanation in the form of screen display or audio output. The providing unit can, for example, provide the user with the generated explanation. In this way, by providing the user with the generated explanation, the user can receive the information. The provision is carried out using specific methods and formats such as screen display, audio output, or printing. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the generated explanation into an AI model and provide it to the user.

[0073] The generation unit can improve the accuracy of its explanations by using a database of shogi terminology and expressions. The generation unit can, for example, use shogi books and game commentaries as a database to explain the analysis results in an easy-to-understand manner. The generation unit can improve the accuracy of its explanations by using a database of shogi terminology and expressions. The generation unit can, for example, use a database of shogi books and game commentaries to explain the analysis results in an easy-to-understand manner. This improves the accuracy of the explanations by using shogi terminology and expressions. Shogi terminology and expressions are used, for example, in the form of specific lists or usage criteria such as opening theory and tactical moves. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input shogi terminology and expressions into a generation AI to improve the accuracy of its explanations.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is concentrating, the data collection unit can collect data in real time. If the user is tired, the data collection unit can reduce the collection frequency and allow for breaks. If the user is excited, the data collection unit can prioritize collecting only important moments. By adjusting the collection timing according to the user's emotions, the data can be collected at a more appropriate time. The user's emotions are estimated using specific estimation methods and criteria, such as facial recognition or voice analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and adjust the timing of data collection.

[0075] The data collection unit can dynamically change the collection frequency in response to changes in the game state when collecting analysis results from the Shogi AI. For example, the collection unit can increase the collection frequency when the game state changes significantly. The collection unit can decrease the collection frequency when the game state is stable. The collection unit can increase the collection frequency as the game approaches the endgame. This allows for efficient data collection by changing the collection frequency in response to changes in the game state. Changes in the game state are detected using specific criteria and detection methods, such as changes in evaluation values ​​or the progress of moves. Some or all of the above-described processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the analysis results from the Shogi AI into an AI model and dynamically change the collection frequency in response to changes in the game state.

[0076] The data collection unit can enable more detailed analysis by including the players' past game data in the data it collects. For example, the data collection unit collects the players' past game data and compares it with the current position. The data collection unit can extract specific patterns from the players' past game data. The data collection unit can predict the current position based on the players' past game data. This enables detailed analysis by including the players' past game data. The players' past game data is collected using specific details and collection methods, such as the date and time of the game, the game result, and the tactics used. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the players' past game data into an AI model and perform a detailed analysis.

[0077] The data collection unit can estimate the user's emotions and determine the priority of the analysis results to collect based on the estimated user emotions. For example, if the user is focused, the data collection unit may prioritize collecting important analysis results. If the user is tired, the data collection unit may prioritize collecting simple analysis results. If the user is excited, the data collection unit may prioritize collecting interesting analysis results. In this way, important information can be collected preferentially by determining the priority of analysis results according to the user's emotions. The priority of analysis results is determined using specific criteria and methods, such as importance or urgency. Emotion estimation is implemented using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and determine the priority of the analysis results.

[0078] The data collection unit can improve the accuracy of its analysis by including the players' playing style and tactical tendencies in the data it collects. For example, the data collection unit analyzes the players' playing style and selects the data to collect. The data collection unit can adjust the data to collect based on the players' tactical tendencies. The data collection unit can optimize the data to collect by referring to the players' past tactics. This improves the accuracy of the analysis by including the players' playing style and tactical tendencies. Playing style and tactical tendencies are analyzed using specific content and analysis methods, such as offensive or defensive play styles and the frequency of use of specific tactics. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the players' playing style and tactical tendencies into an AI model to improve the accuracy of the analysis.

[0079] The data collection unit can provide a multifaceted perspective for analysis by including the players' real-time heart rate and stress levels in the data it collects. For example, the data collection unit can collect the players' heart rates in real time and incorporate them into the analysis. The data collection unit can measure the players' stress levels and use them in the analysis. The data collection unit can supplement the analysis results based on the players' physiological data. This allows for a multifaceted perspective for analysis by including the players' physiological data. Heart rate and stress levels are measured using specific measurement methods and criteria, such as heart rate monitors and stress assessment scales. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the players' heart rates and stress levels into an AI model to provide a multifaceted perspective for analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is focused, the analysis unit can perform a detailed analysis. If the user is tired, the analysis unit can perform a simplified analysis. If the user is excited, the analysis unit can perform an analysis that highlights interesting parts. By adjusting the level of detail of the analysis according to the user's emotions, appropriate analysis results can be provided. The level of detail of the analysis is adjusted using specific adjustment criteria and methods, such as the depth of the analysis and the amount of information displayed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and adjust the level of detail of the analysis.

[0081] The analysis unit can dynamically change its analysis algorithm in response to changes in the game state during analysis. For example, the analysis unit changes the analysis algorithm when the game state changes significantly. The analysis unit can simplify the analysis algorithm when the game state is stable. The analysis unit can strengthen the analysis algorithm as the game approaches its endgame. This allows for efficient analysis by changing the analysis algorithm in response to changes in the game state. The analysis algorithm is changed using specific types and methods, such as changing the evaluation function or adjusting the search depth. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the analysis algorithm into an AI model in response to changes in the game state and dynamically change it.

[0082] The analysis unit can perform a more detailed analysis by including the players' past game data in the data being analyzed. For example, the analysis unit includes the players' past game data in the analysis. The analysis unit can extract specific patterns from the players' past game data. The analysis unit can predict the current position based on the players' past game data. This makes a detailed analysis possible by including the players' past game data. Past game data is collected using specific details and collection methods, such as the date and time of the game, the game result, and the tactics used. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the players' past game data into an AI model and perform a detailed analysis.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, an appropriate display method can be provided. The display method of the analysis results is adjusted using specific types and adjustment criteria, such as graph display, text display, and audio output. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and adjust the display method of the analysis results.

[0084] The analysis unit can improve the accuracy of its analysis by including the players' playing style and tactical tendencies in the data it analyzes. For example, the analysis unit can analyze the players' playing style and reflect it in the analysis. The analysis unit can adjust the analysis based on the players' tactical tendencies. The analysis unit can optimize the analysis by referring to the players' past tactics. This improves the accuracy of the analysis by including the players' playing style and tactical tendencies. Playing style and tactical tendencies are analyzed using specific content and analysis methods, such as offensive or defensive styles and the frequency of use of specific tactics. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the players' playing style and tactical tendencies into an AI model to improve the accuracy of the analysis.

[0085] The analysis unit can provide a multifaceted perspective on the analysis by including the players' real-time heart rate and stress levels in the data being analyzed. For example, the analysis unit can reflect the players' heart rate in the analysis in real time. The analysis unit can measure the players' stress levels and use them in the analysis. The analysis unit can supplement the analysis results based on the players' physiological data. This allows for a multifaceted perspective on the analysis by including the players' physiological data. Heart rate and stress levels are measured using specific measurement methods and criteria, such as heart rate monitors and stress assessment scales. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the players' heart rate and stress levels into an AI model to provide a multifaceted perspective on the analysis.

[0086] The generation unit can estimate the user's emotions and adjust the way the generated explanation is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate an explanation that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate an explanation that emphasizes the shortest route. If the user is excited, the generation unit can generate an explanation with visually stimulating effects. In this way, appropriate explanations can be provided by adjusting the way the explanation is presented according to the user's emotions. The way the explanation is presented is adjusted using specific types and adjustment criteria, such as text, audio, and diagrams. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and adjust the way the explanation is presented.

[0087] The generation unit can include elements in the generated explanation that are dynamically modified in response to changes in the game situation. For example, the generation unit can change the content of the explanation if the game situation changes significantly. If the game situation is stable, the generation unit can simplify the content of the explanation. As the game approaches the endgame, the generation unit can strengthen the content of the explanation. This allows for the provision of appropriate information by dynamically modifying the explanation in response to changes in the game situation. The elements that are dynamically modified are changed using specific content and modification criteria, such as the evaluation value of the game situation or the progress of moves. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the explanation into the generation AI in response to changes in the game situation and dynamically modify it.

[0088] The generation unit can provide more detailed explanations by including the players' past game data in the explanations it generates. For example, the generation unit includes the players' past game data in the explanation. The generation unit can extract specific patterns from the players' past game data. The generation unit can predict the current position based on the players' past game data. This makes it possible to provide detailed explanations by including the players' past game data. Past game data is collected using specific details and collection methods, such as the date and time of the game, the game result, and the tactics used. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the players' past game data into a generation AI to provide a detailed explanation.

[0089] The generation unit can estimate the user's emotions and adjust the length of the generated explanation based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise explanation. If the user is relaxed, the generation unit can generate a longer explanation that includes more detail. If the user is excited, the generation unit can generate an explanation with visually stimulating effects. This allows for the provision of appropriate information by adjusting the length of the explanation according to the user's emotions. The length of the explanation is adjusted using specific adjustment criteria and methods, such as the number of characters or time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and adjust the length of the explanation.

[0090] The generation unit can improve the accuracy of its explanations by including the players' playing style and tactical tendencies in the explanations it generates. For example, the generation unit can analyze the players' playing style and reflect it in the explanation. The generation unit can adjust the explanation based on the players' tactical tendencies. The generation unit can optimize the explanation by referring to the players' past tactics. This improves the accuracy of the explanation by including the players' playing style and tactical tendencies. Playing style and tactical tendencies are analyzed using specific content and analysis methods, such as offensive or defensive play styles and the frequency of use of specific tactics. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the players' playing style and tactical tendencies into a generation AI to improve the accuracy of the explanation.

[0091] The generation unit can provide a multifaceted perspective on the explanation by including the players' real-time heart rate and stress levels in the explanation it generates. For example, the generation unit can reflect the players' heart rate in the explanation in real time. The generation unit can measure the players' stress levels and use them in the explanation. The generation unit can supplement the explanation results based on the players' physiological data. This allows for a multifaceted perspective on the explanation by including the players' physiological data. Heart rate and stress levels are measured using specific measurement methods and criteria, such as heart rate monitors and stress assessment scales. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the players' heart rate and stress levels into a generation AI to provide a multifaceted perspective on the explanation.

[0092] The service provider can estimate the user's emotions and adjust the way the explanation is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. If the user is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, by adjusting the way the explanation is displayed according to the user's emotions, an appropriate display method can be provided. The way the explanation is displayed is adjusted using specific types and adjustment criteria, such as graph display, text display, and audio output. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and adjust the way the explanation is displayed.

[0093] The provider may include elements in the explanations it provides that can be dynamically modified in response to changes in the game situation. For example, the provider may change the content of the explanation if the game situation changes significantly. The provider may simplify the content of the explanation if the game situation is stable. The provider may strengthen the content of the explanation as the game approaches its endgame. This allows for the provision of appropriate information by dynamically modifying the explanation in response to changes in the game situation. The elements that are dynamically modified may be changed using specific content or modification criteria, such as the evaluation value of the game situation or the progress of moves. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider may input the explanation into an AI model in response to changes in the game situation and modify it dynamically.

[0094] The service provider can provide more detailed explanations by including the players' past game data in the explanations it provides. For example, the service provider can include the players' past game data in the explanations. The service provider can extract specific patterns from the players' past game data. The service provider can predict the current position based on the players' past game data. This makes it possible to provide detailed explanations by including the players' past game data. Past game data is collected using specific content and collection methods, such as the date and time of the game, the result of the game, and the tactics used. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the players' past game data into an AI model to provide detailed explanations.

[0095] The service provider can estimate the user's emotions and determine the priority of the explanations to be provided based on the estimated emotions. For example, if the user is focused, the service provider can prioritize important explanations. If the user is tired, the service provider can prioritize simple explanations. If the user is excited, the service provider can prioritize interesting explanations. In this way, important information can be prioritized by determining the priority of explanations according to the user's emotions. The priority of explanations is determined using specific criteria and methods, such as importance or urgency. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI to determine the priority of explanations.

[0096] The service provider can improve the accuracy of its explanations by including the players' playing style and tactical tendencies. For example, the service provider can analyze the players' playing style and reflect it in the explanation. The service provider can adjust the explanation based on the players' tactical tendencies. The service provider can optimize the explanation by referring to the players' past tactics. This improves the accuracy of the explanation by including the players' playing style and tactical tendencies. Playing style and tactical tendencies are analyzed using specific content and analysis methods, such as offensive or defensive play styles and the frequency of use of specific tactics. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the players' playing style and tactical tendencies into an AI model to improve the accuracy of the explanation.

[0097] The service provider can offer a multifaceted perspective on the explanation by including the players' real-time heart rate and stress levels in the explanation. For example, the service provider can reflect the players' heart rate in the explanation in real time. The service provider can measure the players' stress levels and use them in the explanation. The service provider can supplement the explanation results based on the players' physiological data. This allows for a multifaceted perspective on the explanation by including the players' physiological data. Heart rate and stress levels are measured using specific measurement methods and criteria, such as heart rate monitors and stress assessment scales. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the players' heart rate and stress levels into an AI model to provide a multifaceted perspective on the explanation.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The Shogi AI support system can collect and analyze the user's past game data to help improve their Shogi skills. For example, the collection unit collects the user's past game data and provides it to the analysis unit. The analysis unit analyzes the collected past game data to identify the user's strengths and weaknesses. The generation unit generates specific advice to help the user overcome their weaknesses based on the analysis results. The delivery unit provides the generated advice to the user, enabling them to efficiently improve their Shogi skills. This makes it possible to provide individually optimized instruction by utilizing the user's past game data.

[0100] The Shogi AI support system can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-read display method. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, an appropriate display method can be provided. Emotion estimation is performed using specific estimation methods and criteria, such as facial recognition and voice analysis.

[0101] The Shogi AI support system can collect and analyze the player's real-time heart rate and stress level. For example, the collection unit collects the player's heart rate in real time and provides it to the analysis unit. The analysis unit analyzes the collected heart rate data and estimates the player's stress level. The generation unit generates advice based on the analysis results, tailored to the player's stress level. The delivery unit provides the generated advice to the player, enabling them to manage their stress levels while playing the game. This allows for more appropriate advice by utilizing the player's physiological data.

[0102] The Shogi AI support system can estimate the user's emotions and prioritize analysis results based on those emotions. For example, the data collection unit prioritizes collecting important analysis results when the user is focused. When the user is tired, it can prioritize collecting simpler analysis results. When the user is excited, it can prioritize collecting interesting analysis results. In this way, by prioritizing analysis results according to the user's emotions, important information can be collected preferentially. Emotion estimation is performed using specific estimation methods and criteria, such as facial recognition or voice analysis.

[0103] The Shogi AI support system can perform more detailed analysis by collecting past game data of the players and comparing it with the current position. For example, the collection unit collects past game data of the players and provides it to the analysis unit. The analysis unit analyzes the collected past game data and compares it with the current position. The generation unit generates specific advice for the current position based on the analysis results. The provision unit provides the generated advice to the players, enabling them to proceed with the game by utilizing their past experience. In this way, more detailed analysis becomes possible by utilizing the players' past game data.

[0104] The Shogi AI support system can estimate the user's emotions and adjust the way it presents the generated explanations based on those emotions. For example, if the user is relaxed, the generation unit can produce explanations that proceed at a leisurely pace. If the user is in a hurry, it can generate explanations that emphasize the shortest route. If the user is excited, it can generate explanations with visually stimulating effects. In this way, by adjusting the way explanations are presented according to the user's emotions, the system can provide appropriate explanations. Emotion estimation is performed using specific estimation methods and criteria, such as facial recognition or voice analysis.

[0105] The Shogi AI support system can dynamically change its analysis algorithm in response to changes in the game situation. For example, the analysis unit changes its analysis algorithm when the game situation changes significantly. If the game situation is stable, the analysis algorithm can be simplified. As the game approaches the endgame, the analysis algorithm can be strengthened. This allows for efficient analysis by changing the analysis algorithm in response to changes in the game situation. The analysis algorithm is changed using specific types and methods, such as changing the evaluation function or adjusting the search depth.

[0106] The Shogi AI support system can estimate the user's emotions and adjust the length of the explanations it generates based on those emotions. For example, if the user is in a hurry, the generation unit will produce a short, concise explanation. If the user is relaxed, it can generate a longer explanation that includes more details. If the user is excited, it can generate an explanation with visually stimulating effects. By adjusting the length of the explanation according to the user's emotions, it can provide appropriate information. Emotion estimation is performed using specific estimation methods and criteria, such as facial recognition or voice analysis.

[0107] The Shogi AI support system can collect and analyze the playing style and tactical tendencies of players. For example, the collection unit analyzes the players' playing style and selects the data to collect. The analysis unit analyzes the collected playing style data and identifies the players' tactical tendencies. The generation unit generates specific advice on the players' tactics based on the analysis results. The provision unit provides the generated advice to the players, enabling them to proceed with the game while understanding the tactical tendencies. This allows for more detailed analysis by utilizing the players' playing style and tactical tendencies.

[0108] The Shogi AI support system can estimate the user's emotions and adjust the way explanations are displayed based on those emotions. For example, if the user is nervous, the system can provide a simple and easy-to-read explanation. If the user is relaxed, it can provide an explanation that includes detailed information. If the user is in a hurry, it can provide an explanation that gets straight to the point. In this way, by adjusting the explanation display method according to the user's emotions, the system can provide an appropriate explanation. Emotion estimation is performed using specific estimation methods and criteria, such as facial recognition or voice analysis.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects the analysis results from the Shogi AI. For example, it collects numerical data such as the positional status of the game, the best move value, evaluation value, and win rate as indicated by the Shogi AI. Step 2: The analysis unit analyzes the analysis results collected by the collection unit. For example, it analyzes the collected numerical values ​​and the reasons for candidate moves, and analyzes the evaluation values ​​and reasons for candidate moves shown by the Shogi AI. Step 3: The generation unit generates an explanation based on the results analyzed by the analysis unit. For example, it generates an explanation in a human-readable format based on the analysis results, and uses a database of shogi terminology and expressions to improve the accuracy of the explanation. Step 4: The providing unit provides the explanation generated by the generating unit. For example, it provides the generated explanation to the user in the form of screen display or audio output.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the analysis results of the shogi AI using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected numerical values ​​and the reasons for candidate moves. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates human-readable text based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the generated explanation to the user in the form of screen display or audio output. The collection unit can estimate the user's emotions and adjust the timing of collecting the analysis results based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the analysis results of the shogi AI using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected numerical values ​​and the reasons for candidate moves. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates human-readable text based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart glasses 214 and provides the generated explanation to the user in the form of screen display or audio output. The collection unit can estimate the user's emotions and adjust the timing of collecting the analysis results based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the analysis results of the shogi AI using the camera 42 and microphone 238 of the headset terminal 314 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected numerical values ​​and the reasons for candidate moves. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates human-readable text based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides the generated explanation to the user in the form of screen display or audio output. The collection unit can estimate the user's emotions and adjust the timing of collecting the analysis results based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the analysis results of the shogi AI using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected numerical values ​​and the reasons for candidate moves. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates human-readable text based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the robot 414 and provides the generated explanation to the user in the form of screen display or audio output. The collection unit can estimate the user's emotions and adjust the timing of collecting the analysis results based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A collection unit that collects the analysis results of the Shogi AI, An analysis unit analyzes the analysis results collected by the aforementioned collection unit, A generation unit that generates an explanation based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the explanation generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect numerical data on the state of the game and the best move as indicated by the Shogi AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The numerical data and reasons for candidate moves collected by the aforementioned collection unit are analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the results analyzed by the aforementioned analysis unit, an explanation is generated in a human-readable format. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The explanation generated by the generation unit is provided to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We use a database of shogi terminology and expressions to improve the accuracy of explanations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting analysis results from a shogi AI, the collection frequency is dynamically changed according to changes in the game situation. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Including past match data of the players in the collected data enables more detailed analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the analysis results to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is By including the players' playing styles and tactical tendencies in the collected data, the accuracy of the analysis can be improved. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Including real-time heart rate and stress levels of the players in the collected data provides a multifaceted perspective for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is dynamically changed in response to changes in the game situation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Including the players' past game data in the analysis allows for a more detailed analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, By including the players' playing styles and tactical tendencies in the data being analyzed, the accuracy of the analysis can be improved. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Including real-time heart rate and stress levels of the players in the data to be analyzed provides a multifaceted perspective on the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the way the explanations are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is The generated description should include elements that change dynamically in response to changes in the situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is Including the players' past game data in the generated description allows for a more detailed explanation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the explanation generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is The accuracy of the generated descriptions can be improved by including the playing style and tactical tendencies of the players. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is By including the players' real-time heart rate and stress levels in the generated explanation, the system provides a multifaceted perspective on the explanation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the explanations are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The explanations provided should include elements that change dynamically in response to shifts in the situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, Including the players' past game data in the explanations provided allows for more detailed explanations. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of explanations to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, By including the playing style and tactical tendencies of the players in the explanation provided, the accuracy of the explanation can be improved. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, By including the players' real-time heart rate and stress levels in the explanations provided, we offer a multifaceted perspective on the analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects the analysis results of the Shogi AI, An analysis unit analyzes the analysis results collected by the aforementioned collection unit, A generation unit that generates an explanation based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the explanation generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect numerical data on the positional status and best move shown by the Shogi AI. The system according to feature 1.

3. The aforementioned analysis unit, The numerical data and reasons for candidate moves collected by the aforementioned collection unit are analyzed. The system according to feature 1.

4. The generating unit is Based on the results analyzed by the aforementioned analysis unit, an explanation is generated in a human-readable format. The system according to feature 1.

5. The aforementioned supply unit is, The explanation generated by the generation unit is provided to the user. The system according to feature 1.

6. The generating unit is We use a database of shogi terminology and expressions to improve the accuracy of explanations. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting analysis results based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting analysis results from a shogi AI, the collection frequency is dynamically changed according to changes in the game situation. The system according to feature 1.

9. The aforementioned collection unit is Including past match data of the players in the collected data enables more detailed analysis. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of the analysis results to collect based on the estimated user emotions. The system according to feature 1.

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