system

Abstract

The system according to this embodiment aims to enable shogi enthusiasts to efficiently manage their game records and use them to improve their shogi skills. [Solution] The system according to the embodiment comprises a reception unit, a classification unit, a presentation unit, and a comparison unit. The reception unit receives the game record of a game from the user. The classification unit analyzes the game record received by the reception unit and classifies the type of game. The presentation unit presents similar games based on the type of game classified by the classification unit. The comparison unit presents differences between the games presented by the presentation unit and professional game records, standard openings, and the user's own research procedures.

Description

Technical Field

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[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 prior art, there was a problem that it was difficult for shogi enthusiasts to efficiently manage their own game records and use them to improve their skills.

[0005] The system according to the embodiment aims to enable shogi enthusiasts to efficiently manage their own game records and use them to improve their skills.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a classification unit, a presentation unit, and a comparison unit. The reception unit receives game records from the user. The classification unit analyzes the game records entered by the reception unit and classifies the opening type. The presentation unit presents similar games based on the opening types classified by the classification unit. The comparison unit presents differences between the games presented by the presentation unit and professional game records, standard openings, and the user's own research procedures. [Effects of the Invention]

[0007] The system according to this embodiment allows shogi enthusiasts to efficiently manage their game records and use them to improve their shogi skills. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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] 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 game record management system for shogi enthusiasts according to an embodiment of the present invention is a system that allows users to centrally manage their own game records and efficiently improve their shogi skills. This system begins with the user inputting game records from matches played in real-world or online tournaments or practice sessions into the app. Next, the AI ​​automatically classifies the opening type and presents past matches similar to the shogi match played by the user. Furthermore, by presenting differences between the user's matches and professional game records, opening theory, and their own research procedures, the system allows users to easily review their mistakes and areas for improvement in their matches. This mechanism allows users to easily manage their game records and quickly find areas for improvement, points to consider, and methods for improvement from their past matches and professional shogi matches. First, the user inputs game records from matches played in real-world or online tournaments or practice sessions into the app. At this time, the user needs to input detailed information about the match. For example, they input information such as the date of the match, the opponent, and the result of the match. This allows the user to manage their match records in detail. Next, the AI ​​automatically classifies the opening type. The AI ​​analyzes the input game records and identifies the type of opening. For example, the AI ​​classifies the type of opening played by the user into categories such as "Yagura," "Kakugawari," and "Yokofudori." This allows the user to easily understand the type of opening in their games. Furthermore, the AI ​​presents past games similar to the one the user played. The AI ​​analyzes the game records of the user's games and searches for similar games from past games. For example, the AI ​​presents past games with the same opening as the one the user played. This allows the user to refer to past games similar to their own. The AI ​​also presents differences between the user's games and professional game records, opening theory, and the user's own research procedures. The AI ​​analyzes the game records of the user's games and identifies differences between them and professional game records, opening theory, and the user's own research procedures. For example, the AI ​​identifies places in the user's games where moves differ from professional game records, opening theory, and the user's own research procedures. This allows the user to easily review their mistakes and areas for improvement in their games. This system allows users to easily manage their game records and quickly identify areas for improvement, points to consider, and methods for improvement by reviewing their past games and professional shogi matches.For example, users can identify mistakes made in their games and learn how to improve those areas. This allows users to efficiently improve their chess skills. In this way, a game record management system for chess enthusiasts allows users to easily manage their game records and efficiently improve their chess skills.

[0029] The game record management system for shogi enthusiasts according to this embodiment comprises a reception unit, a classification unit, a presentation unit, and a comparison unit. The reception unit receives game records from users. When users input game records, they can also input detailed information such as the date of the game, the opponent, and the result of the game. The reception unit can manage game records in detail by allowing users to input detailed information about the games. The classification unit analyzes the game records entered by the reception unit and classifies the opening type. The classification unit can classify the opening type of the shogi game played by the user into, for example, "Yagura," "Kakugawari," "Yokofudori," etc. The classification unit uses AI to analyze the entered game records and identify the opening type. The presentation unit presents similar games based on the opening type classified by the classification unit. The presentation unit can, for example, present past games of the same opening type as the shogi game played by the user. The presentation unit uses AI to analyze the game records of the shogi games played by the user and search for similar games from past games. The comparison unit presents differences between the game presented by the presentation unit and professional game records, opening theory, and the user's own research procedures. For example, the comparison unit can identify points in a shogi game played by the user where moves differ from professional game records, opening theory, or the user's own research procedures. The comparison unit uses AI to analyze the game records of shogi games played by the user and identify differences from professional game records, opening theory, and the user's own research procedures. As a result, the game record management system for shogi enthusiasts according to this embodiment allows users to easily manage their own game records and efficiently improve their shogi skills.

[0030] The reception section allows users to input game records. When users input game records, they can enter detailed information such as the date of the game, the opponent, and the result of the game. Specifically, users can also enter information such as the start and end times of the game, the time limit used, and the location of the game. This allows for a detailed record of the game, which is useful for reviewing later. The reception section can manage game records in detail by allowing users to input detailed information about the game. Furthermore, the reception section has a function to automatically save the information entered by the user and back it up to a cloud-based database. This prevents data loss or corruption and allows access anytime, anywhere. The reception section also has a function to automatically format the game records entered by the user and convert them into standard game record formats (e.g., KIF format or CSA format). This ensures compatibility with other shogi software and applications. In addition, the reception section provides functions that allow users to input game records by voice using voice input and image recognition technology, or to automatically digitize game records written on paper by taking a picture with a camera. This makes it easier for users to input game records, significantly reducing the effort required for data entry.

[0031] The classification unit analyzes the game records entered by the reception unit and classifies the type of opening. For example, the classification unit can classify the type of opening played by a user into "Yagura," "Kakugawari," "Yokofudori," etc. Specifically, the classification unit uses AI to analyze the entered game records and identify the type of opening. The AI ​​has learned from a vast amount of past game data and can recognize the characteristics and patterns of each move. As a result, the AI ​​can analyze the flow and arrangement of moves made by the user and identify which type of opening it belongs to with high accuracy. For example, the AI ​​has an algorithm that identifies the type of opening based on specific sequences and arrangements that appear in the first few moves. This allows the classification unit to quickly and accurately classify the type of opening played by the user. Furthermore, the classification unit can analyze the characteristics and tendencies of the moves made by the user and identify the user's playing style and preferred types of openings. This allows the user to understand their preferred types of openings and weaknesses and efficiently improve their shogi skills. The classification unit also has a function to visually display the results of the opening classification, allowing the user to check their game history at a glance. This makes it easier for users to track their own growth and progress.

[0032] The presentation unit presents similar games based on the opening patterns classified by the classification unit. For example, the presentation unit can present past games of the same opening pattern as the shogi game played by the user. Specifically, the presentation unit uses AI to analyze the game records of the shogi games played by the user and search for similar games from past games. The AI ​​has an algorithm that references a database of past games and quickly searches for games that match the moves and positions played by the user. This allows the presentation unit to quickly present past games of the same opening pattern as the shogi game played by the user. Furthermore, the presentation unit also has a function to select particularly useful games from the presented games and recommend them to the user. For example, by prioritizing the presentation of games played by professional shogi players or games based on opening theory, the user can obtain higher-quality reference materials. The presentation unit also has a function to visually display the game records of the presented games, making it easy for the user to compare them. This allows the user to learn by comparing their own games with past games. Furthermore, the display section also provides a function that allows users to register games they are interested in as favorites, which is convenient for reviewing them later. In this way, the display section can support users in efficiently learning and improving their Go skills.

[0033] The comparison unit presents differences between the game played by the user, professional game records, opening theory, and the user's own research procedures, based on the game presented by the presentation unit. For example, the comparison unit can identify moves in a shogi game played by the user that differ from professional game records, opening theory, or the user's own research procedures. Specifically, the comparison unit uses AI to analyze the game records of the shogi games played by the user and identify differences from professional game records, opening theory, and the user's own research procedures. The AI ​​has an algorithm that references professional shogi player game data and opening theory databases and compares the user's moves with those databases. This allows the comparison unit to quickly identify moves made by the user that differ from professional game records and opening theory. Furthermore, the comparison unit also has a function to provide detailed explanations of the identified differences. For example, by explaining why a move differs from opening theory, its advantages and disadvantages, and alternative moves, the user can concretely understand areas for improvement in their own moves. The comparison unit also provides a function for the user to compare their moves with their own research procedures, allowing the user to confirm and further deepen their research results. This allows the comparison unit to objectively evaluate the user's moves and provide support to efficiently improve their chess skills.

[0034] The reception unit can input detailed information such as the date of the match, the opponent, and the result of the match. For example, the reception unit can manage detailed match records by having the user input the date of the match. The reception unit can also maintain detailed match records by having the opponent's information entered. Furthermore, the reception unit can manage match results by having the result of the match entered. In this way, the user can manage detailed match records by having the detailed information of the match entered. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the detailed match information entered by the user into the AI, and the AI ​​can analyze and manage that information.

[0035] The classification unit can classify the shogi opening played by the user into categories such as "Yagura," "Kakugawari," and "Yokofudori." For example, the classification unit can identify the opening played by the user and classify it based on that opening. The classification unit uses AI to analyze the input game record and identify the opening. For example, the classification unit can identify the opening played by the user as "Yagura" and classify it based on that opening. The classification unit can also identify the opening played by the user as "Kakugawari" and classify it based on that opening. Furthermore, the classification unit can identify the opening played by the user as "Yokofudori" and classify it based on that opening. This allows the user to easily understand the opening of their games. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the game record entered by the user into the AI, and the AI ​​can analyze the game record and identify the opening.

[0036] The display unit can display past games with the same opening strategy as the shogi game played by the user. For example, the display unit can search for past games with the same opening strategy as the shogi game played by the user and display those games. The display unit uses AI to analyze the game record of the shogi game played by the user and search for similar games from past games. For example, the display unit can display past games with the same opening strategy as the shogi game played by the user. The display unit can also display past games with a similar opening strategy to the shogi game played by the user. Furthermore, the display unit can display professional games with the same opening strategy as the shogi game played by the user. This allows the user to refer to past games similar to their own. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the game record entered by the user into the AI, and the AI ​​can analyze the game record and search for similar games.

[0037] The comparison unit can identify moves in a shogi game played by the user that differ from professional game records, opening theory, or the user's own research procedures. For example, the comparison unit identifies moves in a shogi game played by the user that differ from professional game records, opening theory, or the user's own research procedures. The comparison unit uses AI to analyze the game records of shogi games played by the user and identify differences from professional game records, opening theory, or the user's own research procedures. For example, the comparison unit can identify moves in a shogi game played by the user that differ from professional game records. The comparison unit can also identify moves in a shogi game played by the user that differ from opening theory. Furthermore, the comparison unit can identify moves in a shogi game played by the user that differ from the user's own research procedures. This allows the user to easily review their mistakes and areas for improvement in their games. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the game record entered by the user into the AI, which can then analyze the game record and identify the differences.

[0038] The comparison unit can identify mistakes and areas for improvement in the user's games. For example, the comparison unit can identify mistakes in the shogi games played by the user and present them. The comparison unit uses AI to analyze the game records of the shogi games played by the user and identify mistakes and areas for improvement. For example, the comparison unit can identify moves that deviate from standard opening theory in the shogi games played by the user and present those moves as mistakes. The comparison unit can also identify bad moves in the shogi games played by the user and present those moves as mistakes. Furthermore, the comparison unit can identify areas for improvement in the shogi games played by the user and present those areas for improvement. This allows the user to quickly find areas for improvement in their games. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the game records entered by the user into the AI, and the AI ​​can analyze the game records to identify mistakes and areas for improvement.

[0039] The reception desk can analyze the user's past game history and select the optimal input method. For example, if the user has frequently used voice input in the past, the reception desk will prioritize suggesting voice input. The reception desk uses AI to analyze the user's past game history and select the optimal input method. For example, if the user has frequently used voice input in the past, the reception desk can prioritize suggesting voice input. The reception desk can also prioritize suggesting handwriting input if the user has frequently used handwriting input in the past. Furthermore, if the reception desk has frequently used text input in the past, the reception desk can prioritize suggesting text input. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past game history data into the AI, and the AI ​​can analyze that data to select the optimal input method.

[0040] The reception unit can filter game records based on the user's current skill level and areas of interest when inputting them. For example, the reception unit can suggest inputting only games of an appropriate level based on the user's skill level. The reception unit uses AI to filter game records based on the user's current skill level and areas of interest when inputting them. For example, the reception unit can suggest inputting only games of an appropriate level based on the user's skill level. The reception unit can also suggest inputting only games of a specific type based on the user's areas of interest. Furthermore, the reception unit can combine the user's skill level and areas of interest to suggest inputting the most suitable games. This allows for the input of appropriate games according to the user's skill level and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's skill level and areas of interest into the AI, which can then analyze and filter that data.

[0041] The reception unit can prioritize inputting games that are highly relevant when entering game records, taking into account the user's geographical location information. For example, if the user played a game in a specific region, the reception unit will prioritize inputting games from that region. The reception unit uses AI to prioritize inputting games that are highly relevant when entering game records, taking into account the user's geographical location information. For example, if the user played a game in a specific region, the reception unit can prioritize inputting games from that region. Furthermore, if the user is traveling, the reception unit can prioritize inputting games played at their travel destination. In addition, if the user played a game at home, the reception unit can prioritize inputting games played at home. This allows for the priority input of highly relevant games based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into the AI, and the AI ​​can analyze that information to prioritize inputting highly relevant games.

[0042] The reception unit can analyze the user's social media activity and input relevant games when entering game records. For example, the reception unit can prioritize inputting games mentioned by the user on social media. The reception unit can use AI to analyze the user's social media activity and input relevant games when entering game records. For example, the reception unit can prioritize inputting games mentioned by the user on social media. The reception unit can also prioritize inputting games of professional players that the user follows on social media. Furthermore, the reception unit can prioritize inputting games from tournaments that the user has expressed interest in participating in on social media. This allows the reception unit to input relevant games based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into AI, and the AI ​​can analyze that data and input relevant games.

[0043] The classification unit can improve the accuracy of its classification by considering the interrelationships between games when classifying opening types. For example, the classification unit can classify multiple games between the same players together. The classification unit uses AI to improve the accuracy of its classification by considering the interrelationships between games when classifying opening types. For example, the classification unit can classify multiple games between the same players together. The classification unit can also classify multiple games within the same tournament together. Furthermore, the classification unit can classify different games of the same opening type by relating them to each other. This improves the accuracy of opening type classification by considering the interrelationships between games. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input interrelationship data between games into the AI, and the AI ​​can analyze that data to improve the accuracy of its classification.

[0044] The classification unit can classify opening types while considering the attribute information of the players. For example, the classification unit can classify opening types based on the players' skill level. The classification unit can use AI to classify opening types while considering the attribute information of the players. For example, the classification unit can classify opening types based on the players' skill level. The classification unit can also classify opening types based on the players' age. Furthermore, the classification unit can classify opening types based on the players' gender. This allows for the classification of opening types based on the players' attribute information. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the players' attribute information into the AI, and the AI ​​can analyze that information to classify opening types.

[0045] The classification unit can classify chess games by considering their geographical distribution when classifying chess game types. For example, the classification unit can classify games played in a specific region as a whole. The classification unit can use AI to classify chess games by considering their geographical distribution when classifying chess game types. For example, the classification unit can classify games played in a specific region as a whole. The classification unit can also classify games of the same type played in multiple regions by associating them. Furthermore, the classification unit can prioritize the classification of games that are geographically close. This allows for the classification of chess game types based on the geographical distribution of the games. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input geographical distribution data of games into AI, and the AI ​​can analyze that data to classify chess game types.

[0046] The classification unit can improve the accuracy of its classification by referring to relevant literature on the game when classifying game types. For example, the classification unit can set classification criteria by referring to literature on game types. The classification unit can use AI to improve the accuracy of its classification by referring to relevant literature on the game when classifying game types. For example, the classification unit can set classification criteria by referring to literature on game types. The classification unit can also modify the classification of game types based on the literature. Furthermore, the classification unit can add new game types based on the information in the literature. This improves the accuracy of game type classification by referring to relevant literature on the game. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input game-related literature data into the AI, and the AI ​​can analyze that data to classify game types.

[0047] The presentation unit can optimize its presentation algorithm by referring to past presentation data when presenting similar games. For example, the presentation unit can prioritize presenting games that the user has previously referenced. The presentation unit uses AI to optimize its presentation algorithm by referring to past presentation data when presenting similar games. For example, the presentation unit can prioritize presenting games that the user has previously referenced. The presentation unit can also analyze past presentation data and propose the optimal presentation method. Furthermore, the presentation unit can provide a presentation method tailored to the user's preferences based on past presentation data. In this way, by optimizing the presentation algorithm based on past presentation data, the presentation unit can provide the optimal presentation method for the user. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input past presentation data into AI, and the AI ​​can analyze that data to optimize the presentation algorithm.

[0048] The presentation unit can apply different presentation methods to each game category when presenting similar games. For example, the presentation unit can apply different presentation methods to each type of opening. The presentation unit can use AI to apply different presentation methods to each game category when presenting similar games. For example, the presentation unit can apply different presentation methods to each type of opening. The presentation unit can also apply different presentation methods to each player's skill level. Furthermore, the presentation unit can apply different presentation methods to each game result. By applying different presentation methods to each game category, the presentation unit can provide the user with the most optimal information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input game category data into the AI, which can then analyze the data and apply a presentation method.

[0049] The presentation unit can determine the priority of presentations based on the submission date of the games when presenting similar games. For example, the presentation unit may prioritize presenting recent games. The presentation unit can use AI to determine the priority of presentations based on the submission date of the games when presenting similar games. For example, the presentation unit may prioritize presenting recent games. The presentation unit may also prioritize presenting games within a specific period. Furthermore, the presentation unit may prioritize presenting games of high importance based on the submission date. This allows the presentation unit to provide users with the most optimal information by determining the priority of presentations based on the submission date of the games. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input game submission date data into AI, and the AI ​​can analyze that data to determine the priority of presentations.

[0050] The presentation unit can adjust the presentation order based on the relevance of the games when presenting similar games. For example, the presentation unit can prioritize presenting games with a high degree of similarity. The presentation unit can use AI to adjust the presentation order based on the relevance of the games when presenting similar games. For example, the presentation unit can prioritize presenting games with a high degree of similarity. It can also prioritize presenting games with a high degree of relevance. Furthermore, the presentation unit can combine similarity and relevance to present games in the optimal order. By adjusting the presentation order based on the relevance of the games, the presentation unit can provide the user with the most relevant information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input game relevance data into the AI, and the AI ​​can analyze that data to adjust the presentation order.

[0051] The comparison unit can improve the accuracy of identifying differences by referring to past game data when presenting differences. For example, the comparison unit can improve the accuracy of identifying differences based on past game data. The comparison unit can use AI to improve the accuracy of identifying differences by referring to past game data when presenting differences. For example, the comparison unit can improve the accuracy of identifying differences based on past game data. The comparison unit can also analyze past game data to optimize the method of identifying differences. Furthermore, the comparison unit can improve the method of displaying differences by referring to past game data. In this way, the accuracy of identifying differences can be improved by referring to past game data. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input past game data into AI, and the AI ​​can analyze that data to improve the accuracy of identifying differences.

[0052] The comparison unit can apply different difference analysis methods to each game category when presenting differences. For example, the comparison unit can apply different difference analysis methods to each type of opening. The comparison unit uses AI to apply different difference analysis methods to each game category when presenting differences. For example, the comparison unit can apply different difference analysis methods to each type of opening. The comparison unit can also apply different difference analysis methods to each player's skill level. Furthermore, the comparison unit can apply different difference analysis methods to each game result. By applying different difference analysis methods to each game category, the user can be provided with the most optimal information. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input game category data into AI, and the AI ​​can analyze that data and apply a difference analysis method.

[0053] The comparison unit can analyze changes in differences based on the submission timing of the games when presenting the differences. For example, the comparison unit can compare the differences between recent games and past games. The comparison unit uses AI to analyze changes in differences based on the submission timing of the games when presenting the differences. For example, the comparison unit can compare the differences between recent games and past games. The comparison unit can also analyze differences between games within a specific period. Furthermore, the comparison unit can display changes in differences chronologically based on the submission timing. This allows for the provision of optimal information to the user by analyzing changes in differences based on the submission timing of the games. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input game submission timing data into the AI, and the AI ​​can analyze the data to analyze changes in differences.

[0054] The comparison unit can analyze differences by referring to relevant market data for the game when presenting differences. For example, the comparison unit can evaluate the importance of the differences based on the market data. The comparison unit uses AI to analyze differences by referring to relevant market data for the game when presenting differences. For example, the comparison unit can evaluate the importance of the differences based on the market data. The comparison unit can also analyze the impact of the differences by referring to the market data. Furthermore, the comparison unit can propose ways to improve the differences based on the market data. In this way, the accuracy of difference analysis can be improved by referring to relevant market data for the game. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input relevant market data for the game into the AI, and the AI ​​can analyze the differences by analyzing that data.

[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 reception desk can provide a voice input function when users enter game records. For example, users can input detailed game information by voice. The voice input function helps users quickly and accurately enter detailed game information. It can also reduce the burden on users when entering detailed game information. Furthermore, the voice input function can improve the convenience for users when entering detailed game information. As a result, users can quickly and accurately enter detailed game information and manage game records in detail.

[0057] The classification unit can refer to the user's past game history when classifying the type of shogi game played by the user. For example, it can identify the type of game being played in the current game by referring to the types of games the user has played in the past. This allows the classification unit to classify the type of game more accurately by considering the user's past game history. In addition, the classification unit can understand the user's tendencies in terms of game types by referring to the user's past game history. Furthermore, the classification unit can analyze changes in the user's game types based on the user's past game history. This allows the user to understand the tendencies and changes in their own game types, and to classify the type of game being played more accurately.

[0058] The display unit can consider the user's skill level when presenting past games similar to the shogi game the user has played. For example, it can present games of an appropriate level according to the user's skill level. This allows the display unit to provide games that are easy for the user to refer to by presenting games that match the user's skill level. The display unit can also adjust the difficulty of the games according to the user's skill level. Furthermore, the display unit can provide commentary on the games based on the user's skill level. This allows the user to refer to games that match their skill level and efficiently improve their shogi skills.

[0059] The reception desk can analyze the user's past game history and select the optimal input method. For example, if the user has frequently used voice input in the past, it can prioritize suggesting voice input. Similarly, if the user has frequently used handwriting input in the past, it can prioritize suggesting handwriting input. Furthermore, if the user has frequently used text input in the past, it can prioritize suggesting text input. This allows the system to suggest the most suitable input method based on the user's past input history.

[0060] The reception system can filter game records based on the user's current skill level and areas of interest. For example, it can suggest that only games of an appropriate level be entered based on the user's skill level. It can also suggest that only games of a specific opening type be entered based on the user's areas of interest. Furthermore, it can combine the user's skill level and areas of interest to suggest the most suitable games to enter. This ensures that appropriate games are entered according to the user's skill level and areas of interest.

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

[0062] Step 1: The reception desk receives the game record from the user. Users can enter detailed information such as the date of the game, the opponent, and the result of the game, allowing for detailed management of the game record. Step 2: The classification unit analyzes the game records entered by the reception unit and classifies the opening type. The classification unit can classify the games into categories such as "Yagura," "Kakugawari," and "Yokofudori," and uses AI to analyze the entered game records and identify the opening type. Step 3: The presentation unit presents similar games based on the opening types classified by the classification unit. The presentation unit can present past games of the same opening type as the shogi game played by the user, and uses AI to analyze the game records of the shogi games played by the user and search for similar games from past games. Step 4: The comparison unit presents the differences between the game played by the user, professional game records, opening theory, and the user's own research procedures, based on the game presented by the presentation unit. The comparison unit can identify the parts of the shogi game played by the user that differ from professional game records, opening theory, and the user's own research procedures, and uses AI to analyze the game record of the shogi game played by the user to identify the differences between it and professional game records, opening theory, and the user's own research procedures.

[0063] (Example of form 2) The Shogi game record management system for shogi enthusiasts according to an embodiment of the present invention is a system that allows users to centrally manage their own game records and efficiently improve their shogi skills. This system begins with the user inputting game records from matches played in real-world or online tournaments or practice sessions into the app. Next, the AI ​​automatically classifies the opening type and presents past matches similar to the shogi match played by the user. Furthermore, by presenting differences between the user's matches and professional game records, opening theory, and their own research procedures, the system allows users to easily review their mistakes and areas for improvement in their matches. This mechanism allows users to easily manage their game records and quickly find areas for improvement, points to consider, and methods for improvement from their past matches and professional shogi matches. First, the user inputs game records from matches played in real-world or online tournaments or practice sessions into the app. At this time, the user needs to input detailed information about the match. For example, they input information such as the date of the match, the opponent, and the result of the match. This allows the user to manage their match records in detail. Next, the AI ​​automatically classifies the opening type. The AI ​​analyzes the input game records and identifies the type of opening. For example, the AI ​​classifies the type of opening played by the user into categories such as "Yagura," "Kakugawari," and "Yokofudori." This allows the user to easily understand the type of opening in their games. Furthermore, the AI ​​presents past games similar to the one the user played. The AI ​​analyzes the game records of the user's games and searches for similar games from past games. For example, the AI ​​presents past games with the same opening as the one the user played. This allows the user to refer to past games similar to their own. The AI ​​also presents differences between the user's games and professional game records, opening theory, and the user's own research procedures. The AI ​​analyzes the game records of the user's games and identifies differences between them and professional game records, opening theory, and the user's own research procedures. For example, the AI ​​identifies places in the user's games where moves differ from professional game records, opening theory, and the user's own research procedures. This allows the user to easily review their mistakes and areas for improvement in their games. This system allows users to easily manage their game records and quickly identify areas for improvement, points to consider, and methods for improvement by reviewing their past games and professional shogi matches.For example, users can identify mistakes made in their games and learn how to improve those areas. This allows users to efficiently improve their chess skills. In this way, a game record management system for chess enthusiasts allows users to easily manage their game records and efficiently improve their chess skills.

[0064] The game record management system for shogi enthusiasts according to this embodiment comprises a reception unit, a classification unit, a presentation unit, and a comparison unit. The reception unit receives game records from users. When users input game records, they can also input detailed information such as the date of the game, the opponent, and the result of the game. The reception unit can manage game records in detail by allowing users to input detailed information about the games. The classification unit analyzes the game records entered by the reception unit and classifies the opening type. The classification unit can classify the opening type of the shogi game played by the user into, for example, "Yagura," "Kakugawari," "Yokofudori," etc. The classification unit uses AI to analyze the entered game records and identify the opening type. The presentation unit presents similar games based on the opening type classified by the classification unit. The presentation unit can, for example, present past games of the same opening type as the shogi game played by the user. The presentation unit uses AI to analyze the game records of the shogi games played by the user and search for similar games from past games. The comparison unit presents differences between the game presented by the presentation unit and professional game records, opening theory, and the user's own research procedures. For example, the comparison unit can identify points in a shogi game played by the user where moves differ from professional game records, opening theory, or the user's own research procedures. The comparison unit uses AI to analyze the game records of shogi games played by the user and identify differences from professional game records, opening theory, and the user's own research procedures. As a result, the game record management system for shogi enthusiasts according to this embodiment allows users to easily manage their own game records and efficiently improve their shogi skills.

[0065] The reception section allows users to input game records. When users input game records, they can enter detailed information such as the date of the game, the opponent, and the result of the game. Specifically, users can also enter information such as the start and end times of the game, the time limit used, and the location of the game. This allows for a detailed record of the game, which is useful for reviewing later. The reception section can manage game records in detail by allowing users to input detailed information about the game. Furthermore, the reception section has a function to automatically save the information entered by the user and back it up to a cloud-based database. This prevents data loss or corruption and allows access anytime, anywhere. The reception section also has a function to automatically format the game records entered by the user and convert them into standard game record formats (e.g., KIF format or CSA format). This ensures compatibility with other shogi software and applications. In addition, the reception section provides functions that allow users to input game records by voice using voice input and image recognition technology, or to automatically digitize game records written on paper by taking a picture with a camera. This makes it easier for users to input game records, significantly reducing the effort required for data entry.

[0066] The classification unit analyzes the game records entered by the reception unit and classifies the type of opening. For example, the classification unit can classify the type of opening played by a user into "Yagura," "Kakugawari," "Yokofudori," etc. Specifically, the classification unit uses AI to analyze the entered game records and identify the type of opening. The AI ​​has learned from a vast amount of past game data and can recognize the characteristics and patterns of each move. As a result, the AI ​​can analyze the flow and arrangement of moves made by the user and identify which type of opening it belongs to with high accuracy. For example, the AI ​​has an algorithm that identifies the type of opening based on specific sequences and arrangements that appear in the first few moves. This allows the classification unit to quickly and accurately classify the type of opening played by the user. Furthermore, the classification unit can analyze the characteristics and tendencies of the moves made by the user and identify the user's playing style and preferred types of openings. This allows the user to understand their preferred types of openings and weaknesses and efficiently improve their shogi skills. The classification unit also has a function to visually display the results of the opening classification, allowing the user to check their game history at a glance. This makes it easier for users to track their own growth and progress.

[0067] The presentation unit presents similar games based on the opening patterns classified by the classification unit. For example, the presentation unit can present past games of the same opening pattern as the shogi game played by the user. Specifically, the presentation unit uses AI to analyze the game records of the shogi games played by the user and search for similar games from past games. The AI ​​has an algorithm that references a database of past games and quickly searches for games that match the moves and positions played by the user. This allows the presentation unit to quickly present past games of the same opening pattern as the shogi game played by the user. Furthermore, the presentation unit also has a function to select particularly useful games from the presented games and recommend them to the user. For example, by prioritizing the presentation of games played by professional shogi players or games based on opening theory, the user can obtain higher-quality reference materials. The presentation unit also has a function to visually display the game records of the presented games, making it easy for the user to compare them. This allows the user to learn by comparing their own games with past games. Furthermore, the display section also provides a function that allows users to register games they are interested in as favorites, which is convenient for reviewing them later. In this way, the display section can support users in efficiently learning and improving their Go skills.

[0068] The comparison unit presents differences between the game played by the user, professional game records, opening theory, and the user's own research procedures, based on the game presented by the presentation unit. For example, the comparison unit can identify moves in a shogi game played by the user that differ from professional game records, opening theory, or the user's own research procedures. Specifically, the comparison unit uses AI to analyze the game records of the shogi games played by the user and identify differences from professional game records, opening theory, and the user's own research procedures. The AI ​​has an algorithm that references professional shogi player game data and opening theory databases and compares the user's moves with those databases. This allows the comparison unit to quickly identify moves made by the user that differ from professional game records and opening theory. Furthermore, the comparison unit also has a function to provide detailed explanations of the identified differences. For example, by explaining why a move differs from opening theory, its advantages and disadvantages, and alternative moves, the user can concretely understand areas for improvement in their own moves. The comparison unit also provides a function for the user to compare their moves with their own research procedures, allowing the user to confirm and further deepen their research results. This allows the comparison unit to objectively evaluate the user's moves and provide support to efficiently improve their chess skills.

[0069] The reception unit can input detailed information such as the date of the match, the opponent, and the result of the match. For example, the reception unit can manage detailed match records by having the user input the date of the match. The reception unit can also maintain detailed match records by having the opponent's information entered. Furthermore, the reception unit can manage match results by having the result of the match entered. In this way, the user can manage detailed match records by having the detailed information of the match entered. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the detailed match information entered by the user into the AI, and the AI ​​can analyze and manage that information.

[0070] The classification unit can classify the shogi opening played by the user into categories such as "Yagura," "Kakugawari," and "Yokofudori." For example, the classification unit can identify the opening played by the user and classify it based on that opening. The classification unit uses AI to analyze the input game record and identify the opening. For example, the classification unit can identify the opening played by the user as "Yagura" and classify it based on that opening. The classification unit can also identify the opening played by the user as "Kakugawari" and classify it based on that opening. Furthermore, the classification unit can identify the opening played by the user as "Yokofudori" and classify it based on that opening. This allows the user to easily understand the opening of their games. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the game record entered by the user into the AI, and the AI ​​can analyze the game record and identify the opening.

[0071] The display unit can display past games with the same opening strategy as the shogi game played by the user. For example, the display unit can search for past games with the same opening strategy as the shogi game played by the user and display those games. The display unit uses AI to analyze the game record of the shogi game played by the user and search for similar games from past games. For example, the display unit can display past games with the same opening strategy as the shogi game played by the user. The display unit can also display past games with a similar opening strategy to the shogi game played by the user. Furthermore, the display unit can display professional games with the same opening strategy as the shogi game played by the user. This allows the user to refer to past games similar to their own. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the game record entered by the user into the AI, and the AI ​​can analyze the game record and search for similar games.

[0072] The comparison unit can identify moves in a shogi game played by the user that differ from professional game records, opening theory, or the user's own research procedures. For example, the comparison unit identifies moves in a shogi game played by the user that differ from professional game records, opening theory, or the user's own research procedures. The comparison unit uses AI to analyze the game records of shogi games played by the user and identify differences from professional game records, opening theory, or the user's own research procedures. For example, the comparison unit can identify moves in a shogi game played by the user that differ from professional game records. The comparison unit can also identify moves in a shogi game played by the user that differ from opening theory. Furthermore, the comparison unit can identify moves in a shogi game played by the user that differ from the user's own research procedures. This allows the user to easily review their mistakes and areas for improvement in their games. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the game record entered by the user into the AI, which can then analyze the game record and identify the differences.

[0073] The comparison unit can identify mistakes and areas for improvement in the user's games. For example, the comparison unit can identify mistakes in the shogi games played by the user and present them. The comparison unit uses AI to analyze the game records of the shogi games played by the user and identify mistakes and areas for improvement. For example, the comparison unit can identify moves that deviate from standard opening theory in the shogi games played by the user and present those moves as mistakes. The comparison unit can also identify bad moves in the shogi games played by the user and present those moves as mistakes. Furthermore, the comparison unit can identify areas for improvement in the shogi games played by the user and present those areas for improvement. This allows the user to quickly find areas for improvement in their games. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the game records entered by the user into the AI, and the AI ​​can analyze the game records to identify mistakes and areas for improvement.

[0074] The reception unit can estimate the user's emotions and adjust the timing of game record input based on the estimated emotions. For example, if the user is relaxed, the reception unit can send a notification prompting input. The reception unit uses AI to estimate the user's emotions and adjust the input timing based on those emotions. For example, if the user is relaxed, the reception unit can send a notification prompting input. The reception unit can also suggest postponing input if the user is stressed. Furthermore, if the user is focused, the reception unit can prompt immediate input. This allows the system to prompt game record input at the appropriate time according to the user's emotions. 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 reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into the AI, and the AI ​​can analyze that data to adjust the input timing.

[0075] The reception desk can analyze the user's past game history and select the optimal input method. For example, if the user has frequently used voice input in the past, the reception desk will prioritize suggesting voice input. The reception desk uses AI to analyze the user's past game history and select the optimal input method. For example, if the user has frequently used voice input in the past, the reception desk can prioritize suggesting voice input. The reception desk can also prioritize suggesting handwriting input if the user has frequently used handwriting input in the past. Furthermore, if the reception desk has frequently used text input in the past, the reception desk can prioritize suggesting text input. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past game history data into the AI, and the AI ​​can analyze that data to select the optimal input method.

[0076] The reception unit can filter game records based on the user's current skill level and areas of interest when inputting them. For example, the reception unit can suggest inputting only games of an appropriate level based on the user's skill level. The reception unit uses AI to filter game records based on the user's current skill level and areas of interest when inputting them. For example, the reception unit can suggest inputting only games of an appropriate level based on the user's skill level. The reception unit can also suggest inputting only games of a specific type based on the user's areas of interest. Furthermore, the reception unit can combine the user's skill level and areas of interest to suggest inputting the most suitable games. This allows for the input of appropriate games according to the user's skill level and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's skill level and areas of interest into the AI, which can then analyze and filter that data.

[0077] The reception unit can estimate the user's emotions and determine the priority of the game records to be entered based on the estimated emotions. For example, if the user is relaxed, the reception unit will prioritize entering games of high importance. The reception unit uses AI to estimate the user's emotions and determine the priority of the game records to be entered based on those emotions. For example, if the user is relaxed, the reception unit can prioritize entering games of high importance. Also, if the user is stressed, the reception unit can postpone entering games of low importance. Furthermore, if the user is focused, the reception unit can immediately enter games of high importance. This allows the priority of the game records to be entered to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into the AI, which can then analyze that data to determine the priority of which game records to input.

[0078] The reception unit can prioritize inputting games that are highly relevant when entering game records, taking into account the user's geographical location information. For example, if the user played a game in a specific region, the reception unit will prioritize inputting games from that region. The reception unit uses AI to prioritize inputting games that are highly relevant when entering game records, taking into account the user's geographical location information. For example, if the user played a game in a specific region, the reception unit can prioritize inputting games from that region. Furthermore, if the user is traveling, the reception unit can prioritize inputting games played at their travel destination. In addition, if the user played a game at home, the reception unit can prioritize inputting games played at home. This allows for the priority input of highly relevant games based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into the AI, and the AI ​​can analyze that information to prioritize inputting highly relevant games.

[0079] The reception unit can analyze the user's social media activity and input relevant games when entering game records. For example, the reception unit can prioritize inputting games mentioned by the user on social media. The reception unit can use AI to analyze the user's social media activity and input relevant games when entering game records. For example, the reception unit can prioritize inputting games mentioned by the user on social media. The reception unit can also prioritize inputting games of professional players that the user follows on social media. Furthermore, the reception unit can prioritize inputting games from tournaments that the user has expressed interest in participating in on social media. This allows the reception unit to input relevant games based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into AI, and the AI ​​can analyze that data and input relevant games.

[0080] The classification unit can estimate the user's emotions and adjust the classification criteria for the fighting style based on those emotions. For example, if the user is relaxed, the classification unit can apply detailed classification criteria. The classification unit uses AI to estimate the user's emotions and adjust the classification criteria for the fighting style based on those emotions. For example, if the user is relaxed, the classification unit can apply detailed classification criteria. The classification unit can also apply simplified classification criteria if the user is stressed. Furthermore, if the user is focused, the classification unit can apply highly accurate classification criteria. This allows the classification criteria for the fighting style to be adjusted according to the user's emotions. 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 classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input user emotion data into an AI, and the AI ​​can analyze that data to adjust the classification criteria for the fighting style.

[0081] The classification unit can improve the accuracy of its classification by considering the interrelationships between games when classifying opening types. For example, the classification unit can classify multiple games between the same players together. The classification unit uses AI to improve the accuracy of its classification by considering the interrelationships between games when classifying opening types. For example, the classification unit can classify multiple games between the same players together. The classification unit can also classify multiple games within the same tournament together. Furthermore, the classification unit can classify different games of the same opening type by relating them to each other. This improves the accuracy of opening type classification by considering the interrelationships between games. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input interrelationship data between games into the AI, and the AI ​​can analyze that data to improve the accuracy of its classification.

[0082] The classification unit can classify opening types while considering the attribute information of the players. For example, the classification unit can classify opening types based on the players' skill level. The classification unit can use AI to classify opening types while considering the attribute information of the players. For example, the classification unit can classify opening types based on the players' skill level. The classification unit can also classify opening types based on the players' age. Furthermore, the classification unit can classify opening types based on the players' gender. This allows for the classification of opening types based on the players' attribute information. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the players' attribute information into the AI, and the AI ​​can analyze that information to classify opening types.

[0083] The classification unit can estimate the user's emotions and adjust the order in which it displays the classification results of the battle type based on the estimated user emotions. For example, if the user is relaxed, the classification unit can prioritize displaying detailed classification results. The classification unit uses AI to estimate the user's emotions and adjust the order in which it displays the classification results of the battle type based on those emotions. For example, if the user is relaxed, the classification unit can prioritize displaying detailed classification results. Also, if the user is stressed, the classification unit can prioritize displaying simplified classification results. Furthermore, if the user is focused, the classification unit can prioritize displaying high-importance classification results. This allows the display order of the classification results of the battle type to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input user emotion data into the AI, which then analyzes that data and adjusts the order in which the classification results for the battle style are displayed.

[0084] The classification unit can classify chess games by considering their geographical distribution when classifying chess game types. For example, the classification unit can classify games played in a specific region as a whole. The classification unit can use AI to classify chess games by considering their geographical distribution when classifying chess game types. For example, the classification unit can classify games played in a specific region as a whole. The classification unit can also classify games of the same type played in multiple regions by associating them. Furthermore, the classification unit can prioritize the classification of games that are geographically close. This allows for the classification of chess game types based on the geographical distribution of the games. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input geographical distribution data of games into AI, and the AI ​​can analyze that data to classify chess game types.

[0085] The classification unit can improve the accuracy of its classification by referring to relevant literature on the game when classifying game types. For example, the classification unit can set classification criteria by referring to literature on game types. The classification unit can use AI to improve the accuracy of its classification by referring to relevant literature on the game when classifying game types. For example, the classification unit can set classification criteria by referring to literature on game types. The classification unit can also modify the classification of game types based on the literature. Furthermore, the classification unit can add new game types based on the information in the literature. This improves the accuracy of game type classification by referring to relevant literature on the game. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input game-related literature data into the AI, and the AI ​​can analyze that data to classify game types.

[0086] The presentation unit can estimate the user's emotions and adjust the presentation method of similar games based on the estimated user emotions. For example, if the user is relaxed, the presentation unit can provide a presentation method that includes detailed information. The presentation unit uses AI to estimate the user's emotions and adjust the presentation method of similar games based on those emotions. For example, if the user is relaxed, the presentation unit can provide a presentation method that includes detailed information. The presentation unit can also provide a simplified presentation method if the user is stressed. Furthermore, if the user is focused, the presentation unit can prioritize the presentation of highly important information. This allows the presentation method of similar games to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input user emotion data into the AI, which can then analyze that data to adjust the method of presenting similar games.

[0087] The presentation unit can optimize its presentation algorithm by referring to past presentation data when presenting similar games. For example, the presentation unit can prioritize presenting games that the user has previously referenced. The presentation unit uses AI to optimize its presentation algorithm by referring to past presentation data when presenting similar games. For example, the presentation unit can prioritize presenting games that the user has previously referenced. The presentation unit can also analyze past presentation data and propose the optimal presentation method. Furthermore, the presentation unit can provide a presentation method tailored to the user's preferences based on past presentation data. In this way, by optimizing the presentation algorithm based on past presentation data, the presentation unit can provide the optimal presentation method for the user. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input past presentation data into AI, and the AI ​​can analyze that data to optimize the presentation algorithm.

[0088] The presentation unit can apply different presentation methods to each game category when presenting similar games. For example, the presentation unit can apply different presentation methods to each type of opening. The presentation unit can use AI to apply different presentation methods to each game category when presenting similar games. For example, the presentation unit can apply different presentation methods to each type of opening. The presentation unit can also apply different presentation methods to each player's skill level. Furthermore, the presentation unit can apply different presentation methods to each game result. By applying different presentation methods to each game category, the presentation unit can provide the user with the most optimal information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input game category data into the AI, which can then analyze the data and apply a presentation method.

[0089] The display unit can estimate the user's emotions and adjust the display order of similar games based on the estimated emotions. For example, if the user is relaxed, the display unit will prioritize displaying games with detailed information. The display unit uses AI to estimate the user's emotions and adjust the display order of similar games based on those emotions. For example, if the user is relaxed, the display unit can prioritize displaying games with detailed information. Also, if the user is stressed, the display unit can prioritize displaying games with simplified information. Furthermore, if the user is focused, the display unit can prioritize displaying games of high importance. In this way, by adjusting the display order of similar games according to the user's emotions, the display unit can provide the user with the most optimal information. 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 display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the AI, which can then analyze that data to adjust the display order of similar games.

[0090] The presentation unit can determine the priority of presentations based on the submission date of the games when presenting similar games. For example, the presentation unit may prioritize presenting recent games. The presentation unit can use AI to determine the priority of presentations based on the submission date of the games when presenting similar games. For example, the presentation unit may prioritize presenting recent games. The presentation unit may also prioritize presenting games within a specific period. Furthermore, the presentation unit may prioritize presenting games of high importance based on the submission date. This allows the presentation unit to provide users with the most optimal information by determining the priority of presentations based on the submission date of the games. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input game submission date data into AI, and the AI ​​can analyze that data to determine the priority of presentations.

[0091] The presentation unit can adjust the presentation order based on the relevance of the games when presenting similar games. For example, the presentation unit can prioritize presenting games with a high degree of similarity. The presentation unit can use AI to adjust the presentation order based on the relevance of the games when presenting similar games. For example, the presentation unit can prioritize presenting games with a high degree of similarity. It can also prioritize presenting games with a high degree of relevance. Furthermore, the presentation unit can combine similarity and relevance to present games in the optimal order. By adjusting the presentation order based on the relevance of the games, the presentation unit can provide the user with the most relevant information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input game relevance data into the AI, and the AI ​​can analyze that data to adjust the presentation order.

[0092] The comparison unit can estimate the user's emotions and adjust how differences are displayed based on the estimated emotions. For example, if the user is relaxed, the comparison unit can display detailed differences. The comparison unit uses AI to estimate the user's emotions and adjust how differences are displayed based on those emotions. For example, if the user is relaxed, the comparison unit can display detailed differences. The comparison unit can also display simplified differences if the user is stressed. Furthermore, if the user is focused, the comparison unit can prioritize displaying differences of high importance. This allows the system to provide the user with optimal information by adjusting how differences are displayed according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input user emotion data into an AI, which can then analyze the data and adjust how differences are displayed.

[0093] The comparison unit can improve the accuracy of identifying differences by referring to past game data when presenting differences. For example, the comparison unit can improve the accuracy of identifying differences based on past game data. The comparison unit can use AI to improve the accuracy of identifying differences by referring to past game data when presenting differences. For example, the comparison unit can improve the accuracy of identifying differences based on past game data. The comparison unit can also analyze past game data to optimize the method of identifying differences. Furthermore, the comparison unit can improve the method of displaying differences by referring to past game data. In this way, the accuracy of identifying differences can be improved by referring to past game data. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without using AI. For example, the comparison unit can input past game data into AI, and the AI ​​can analyze that data to improve the accuracy of identifying differences.

[0094] The comparison unit can apply different difference analysis methods to each game category when presenting differences. For example, the comparison unit can apply different difference analysis methods to each type of opening. The comparison unit uses AI to apply different difference analysis methods to each game category when presenting differences. For example, the comparison unit can apply different difference analysis methods to each type of opening. The comparison unit can also apply different difference analysis methods to each player's skill level. Furthermore, the comparison unit can apply different difference analysis methods to each game result. By applying different difference analysis methods to each game category, the user can be provided with the most optimal information. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input game category data into AI, and the AI ​​can analyze that data and apply a difference analysis method.

[0095] The comparison unit can estimate the user's emotions and adjust the importance of differences based on the estimated emotions. For example, if the user is relaxed, the comparison unit can display detailed differences. The comparison unit uses AI to estimate the user's emotions and adjust the importance of differences based on those emotions. For example, if the user is relaxed, the comparison unit can display detailed differences. The comparison unit can also omit less important differences if the user is stressed. Furthermore, if the user is focused, the comparison unit can prioritize displaying more important differences. This allows the system to provide the user with optimal information by adjusting the importance of differences according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input user emotion data into the AI, which can then analyze that data and adjust the importance of the differences.

[0096] The comparison unit can analyze changes in differences based on the submission timing of the games when presenting the differences. For example, the comparison unit can compare the differences between recent games and past games. The comparison unit uses AI to analyze changes in differences based on the submission timing of the games when presenting the differences. For example, the comparison unit can compare the differences between recent games and past games. The comparison unit can also analyze differences between games within a specific period. Furthermore, the comparison unit can display changes in differences chronologically based on the submission timing. This allows for the provision of optimal information to the user by analyzing changes in differences based on the submission timing of the games. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input game submission timing data into the AI, and the AI ​​can analyze the data to analyze changes in differences.

[0097] The comparison unit can analyze differences by referring to relevant market data for the game when presenting differences. For example, the comparison unit can evaluate the importance of the differences based on the market data. The comparison unit uses AI to analyze differences by referring to relevant market data for the game when presenting differences. For example, the comparison unit can evaluate the importance of the differences based on the market data. The comparison unit can also analyze the impact of the differences by referring to the market data. Furthermore, the comparison unit can propose ways to improve the differences based on the market data. In this way, the accuracy of difference analysis can be improved by referring to relevant market data for the game. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input relevant market data for the game into the AI, and the AI ​​can analyze the differences by analyzing that data.

[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 reception desk can provide a voice input function when users enter game records. For example, users can input detailed game information by voice. The voice input function helps users quickly and accurately enter detailed game information. It can also reduce the burden on users when entering detailed game information. Furthermore, the voice input function can improve the convenience for users when entering detailed game information. As a result, users can quickly and accurately enter detailed game information and manage game records in detail.

[0100] The classification unit can refer to the user's past game history when classifying the type of shogi game played by the user. For example, it can identify the type of game being played in the current game by referring to the types of games the user has played in the past. This allows the classification unit to classify the type of game more accurately by considering the user's past game history. In addition, the classification unit can understand the user's tendencies in terms of game types by referring to the user's past game history. Furthermore, the classification unit can analyze changes in the user's game types based on the user's past game history. This allows the user to understand the tendencies and changes in their own game types, and to classify the type of game being played more accurately.

[0101] The display unit can consider the user's skill level when presenting past games similar to the shogi game the user has played. For example, it can present games of an appropriate level according to the user's skill level. This allows the display unit to provide games that are easy for the user to refer to by presenting games that match the user's skill level. The display unit can also adjust the difficulty of the games according to the user's skill level. Furthermore, the display unit can provide commentary on the games based on the user's skill level. This allows the user to refer to games that match their skill level and efficiently improve their shogi skills.

[0102] The comparison unit, when identifying moves in a shogi game played by the user that differ from professional game records, opening theory, or the user's own researched procedures, can estimate the user's emotions and adjust how the differences are presented based on those emotions. For example, if the user is relaxed, detailed differences can be presented. If the user is stressed, simplified differences can be presented. Furthermore, if the user is focused, high-importance differences can be prioritized. In this way, by adjusting how differences are presented according to the user's emotions, the system can provide the user with the most optimal information.

[0103] The reception system can estimate the user's emotions and adjust the timing of game record input based on those estimates. For example, if the user is relaxed, it can send a notification prompting them to input. If the user is stressed, it can suggest postponing the input. Furthermore, if the user is focused, it can prompt them to input immediately. This allows the system to prompt the user to input game records at the appropriate time according to their emotions.

[0104] The classification unit can estimate the user's emotions and adjust the classification criteria for their play style based on those emotions. For example, if the user is relaxed, detailed classification criteria can be applied. If the user is stressed, simplified classification criteria can be applied. Furthermore, if the user is focused, highly accurate classification criteria can be applied. By adjusting the classification criteria for play style according to the user's emotions, the system can provide the user with the most optimal classification result.

[0105] The presentation unit can estimate the user's emotions and adjust the presentation method of similar games based on the estimated emotions. For example, if the user is relaxed, it can provide a presentation method that includes detailed information. If the user is stressed, it can provide a simplified presentation method. Furthermore, if the user is focused, it can prioritize the presentation of highly important information. In this way, by adjusting the presentation method of similar games according to the user's emotions, the system can provide the user with the most optimal information.

[0106] The comparison unit can estimate the user's emotions and adjust the importance of differences based on those emotions. For example, if the user is relaxed, detailed differences can be displayed. If the user is stressed, less important differences can be omitted. Furthermore, if the user is focused, highly important differences can be prioritized. By adjusting the importance of differences according to the user's emotions, the system can provide the user with the most relevant information.

[0107] The reception desk can analyze the user's past game history and select the optimal input method. For example, if the user has frequently used voice input in the past, it can prioritize suggesting voice input. Similarly, if the user has frequently used handwriting input in the past, it can prioritize suggesting handwriting input. Furthermore, if the user has frequently used text input in the past, it can prioritize suggesting text input. This allows the system to suggest the most suitable input method based on the user's past input history.

[0108] The reception system can filter game records based on the user's current skill level and areas of interest. For example, it can suggest that only games of an appropriate level be entered based on the user's skill level. It can also suggest that only games of a specific opening type be entered based on the user's areas of interest. Furthermore, it can combine the user's skill level and areas of interest to suggest the most suitable games to enter. This ensures that appropriate games are entered according to the user's skill level and areas of interest.

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

[0110] Step 1: The reception desk receives the game record from the user. Users can enter detailed information such as the date of the game, the opponent, and the result of the game, allowing for detailed management of the game record. Step 2: The classification unit analyzes the game records entered by the reception unit and classifies the opening type. The classification unit can classify the games into categories such as "Yagura," "Kakugawari," and "Yokofudori," and uses AI to analyze the entered game records and identify the opening type. Step 3: The presentation unit presents similar games based on the opening types classified by the classification unit. The presentation unit can present past games of the same opening type as the shogi game played by the user, and uses AI to analyze the game records of the shogi games played by the user and search for similar games from past games. Step 4: The comparison unit presents the differences between the game played by the user, professional game records, opening theory, and the user's own research procedures, based on the game presented by the presentation unit. The comparison unit can identify the parts of the shogi game played by the user that differ from professional game records, opening theory, and the user's own research procedures, and uses AI to analyze the game record of the shogi game played by the user to identify the differences between it and professional game records, opening theory, and the user's own research procedures.

[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 reception unit, classification unit, presentation unit, and comparison unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and is used when the user inputs the game record of a match. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the input game record to classify the type of game. The presentation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and presents similar matches based on the classified type of game. The comparison unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and presents differences between the presented match and professional game records, opening theory, or the user's own research procedures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed 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 commands and other instructions from the user by receiving voice signals. 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 reception unit, classification unit, presentation unit, and comparison unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and is used when the user inputs the game record of a match. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the input game record to classify the type of game. The presentation unit is implemented by the identification processing unit 290 of the data processing unit 12 and presents similar matches based on the classified type of game. The comparison unit is implemented by the identification processing unit 290 of the data processing unit 12 and presents differences between the presented match and professional game records, opening theory, or the user's own research procedures. 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 commands and other instructions from the user by receiving voice signals. 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 reception unit, classification unit, presentation unit, and comparison unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and is used when the user inputs the game record of a match. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the input game record to classify the type of game. The presentation unit is implemented by the identification processing unit 290 of the data processing unit 12 and presents similar games based on the classified type of game. The comparison unit is implemented by the identification processing unit 290 of the data processing unit 12 and presents differences between the presented game and professional game records, opening theory, or the user's own research procedures. 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 commands and other instructions from the user by receiving voice signals. 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 reception unit, classification unit, presentation unit, and comparison unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and is used when a user inputs a game record. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the input game record to classify the type of game. The presentation unit is implemented by the identification processing unit 290 of the data processing unit 12 and presents similar games based on the classified type of game. The comparison unit is implemented by the identification processing unit 290 of the data processing unit 12 and presents differences between the presented game and professional game records, opening theory, or the user's own research procedures. 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 reception area where users input game records, A classification unit analyzes the game records entered by the reception unit and classifies the types of games played, A presentation unit that presents similar games based on the types of games classified by the aforementioned classification unit, The system includes a comparison unit that presents differences between the game presented by the presentation unit and professional game records, opening theory, and the user's own research procedures. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter detailed information such as the date of the match, the opponent, and the result of the match. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned classification unit is The shogi openings played by the user are classified into categories such as "Yagura," "Kakugawari," and "Yokofudori." The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is, The system displays past games using the same opening strategy as the shogi game the user played. The system described in Appendix 1, characterized by the features described herein. (Note 5) The comparison unit is, This tool identifies instances in a user's shogi games where moves differ from professional game records, established openings, or their own researched strategies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The comparison unit is, It points out mistakes and areas for improvement in the user's gameplay. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of game record input based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past game history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering game records, filtering is performed based on the user's current skill level and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the game records to input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering game records, the system prioritizes inputting games that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering game records, the system analyzes the user's social media activity and inputs related games. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned classification unit is It estimates the user's emotions and adjusts the classification criteria for battle styles based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned classification unit is When classifying opening strategies, consider the interrelationships between games to improve the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned classification unit is When classifying opening strategies, the attribute information of the players is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned classification unit is The system estimates the user's emotions and adjusts the order in which the battle type classification results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned classification unit is When classifying opening strategies, the geographical distribution of matches should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned classification unit is When classifying opening strategies, we improve the accuracy of the classification by referring to relevant literature on the games. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, The system estimates the user's emotions and adjusts the presentation method of similar games based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When presenting similar games, the presentation algorithm is optimized by referring to past presentation data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, When presenting similar games, different presentation methods are applied depending on the category of the game. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, The system estimates the user's emotions and adjusts the display order of similar games based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting similar games, the priority of presentation is determined based on when the games were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, When presenting similar games, the order of presentation will be adjusted based on the relevance of the games. The system described in Appendix 1, characterized by the features described herein. (Note 25) The comparison unit is, It estimates the user's emotions and adjusts how differences are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The comparison unit is, When presenting differences, we improve the accuracy of identifying those differences by referring to past game data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The comparison unit is, When presenting the differences, different difference analysis methods are applied to each category of game. The system described in Appendix 1, characterized by the features described herein. (Note 28) The comparison unit is, It estimates the user's emotions and adjusts the importance of differences based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The comparison unit is, When presenting the differences, we analyze how those differences change based on when the game was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The comparison unit is, When presenting the differences, we analyze them by referring to relevant market data for the game. 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 reception area where users input game records, A classification unit analyzes the game records entered by the reception unit and classifies the types of games played, A presentation unit that presents similar games based on the types of games classified by the aforementioned classification unit, The system includes a comparison unit that presents differences between the game presented by the presentation unit and professional game records, opening theory, and the user's own research procedures. A system characterized by the following features.

2. The aforementioned reception unit is Enter detailed information such as the date of the match, the opponent, and the result of the match. The system according to feature 1.

3. The aforementioned classification unit is The shogi openings played by the user are classified into categories such as Yagura, Kakugawari, and Yokofudori. The system according to feature 1.

4. The aforementioned display unit is, The system displays past games using the same opening strategy as the shogi game the user played. The system according to feature 1.

5. The comparison unit is, This tool identifies instances in a user's shogi games where moves differ from professional game records, established openings, or their own researched strategies. The system according to feature 1.

6. The comparison unit is, It points out mistakes and areas for improvement in the user's gameplay. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of game record input based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past game history and selects the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering game records, filtering is performed based on the user's current skill level and areas of interest. The system according to feature 1.

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