system

Abstract

The system according to this embodiment aims to generate and provide explanatory text about shogi positions and candidate moves. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information about the position of a shogi game and candidate moves. The analysis unit analyzes the information collected by the collection unit. The generation unit generates an explanatory text based on the results of the analysis by the analysis unit. The provision unit provides the explanatory text generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the thinking process of the shogi AI and the reasons for the presented situation are difficult to understand, especially difficult for beginners to understand.

[0005] The system according to the embodiment aims to generate and provide explanatory texts about the shogi board positions and candidate moves.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information about shogi positions and candidate moves. The analysis unit analyzes the information collected by the collection unit. The generation unit generates explanatory text based on the results of the analysis by the analysis unit. The provision unit provides the explanatory text generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate and provide explanatory text about shogi positions and candidate moves. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F 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 commentary system according to an embodiment of the present invention is a system that uses a generating AI to provide commentary on the position of a Shogi game, indicating which side has the advantage, and on candidate moves to play next in that position. The Shogi commentary system collects and analyzes information about the Shogi game position and candidate moves, generates and provides commentary, thereby providing commentary that is easy for the user to understand. For example, the Shogi commentary system instructs the Shogi GUI to think about the position and candidate moves. The Shogi commentary system transmits the thinking results of the Shogi AI to a Shogi analyst, and the generating AI outputs commentary. Furthermore, the Shogi commentary system stores Shogi-specific expressions from previously published Shogi books and journalists' game reports in a database, and the generating AI refers to this to provide commentary that is easy for the user to understand. The Shogi commentary system generates summary texts and review problems, allowing users to review the analysis and commentary so far. As a result, the Shogi commentary system can provide even greater value to Shogi AI for people of various skill levels, enabling a wide range of users, from beginners to professional players, to efficiently utilize Shogi AI and improve their Shogi skills. Furthermore, the shogi commentary system has the potential to be expanded to shogi enthusiasts overseas in the future, and can also be applied to other games such as chess and go. This will allow the shogi commentary system to provide users with easy-to-understand explanations and enable the efficient use of shogi AI.

[0029] The shogi commentary system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information about the shogi position and candidate moves. The collection unit, for example, instructs the shogi GUI to think about the position and candidate moves. The collection unit transmits the thinking results of the shogi AI to a shogi analyst. The collection unit, for example, inputs information about the position and candidate moves into the shogi GUI and instructs the shogi AI to think based on that information. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, analyzes information about the shogi position and candidate moves and determines the state of the game and the candidate move to play next. The analysis unit analyzes the state of the game and the candidate move to play next based on the thinking results of the shogi AI. The analysis unit, for example, uses an evaluation function for the shogi position to determine the state of the game and selects the candidate move to play next. The generation unit generates commentary text based on the results analyzed by the analysis unit. The generation unit, for example, uses a generation AI to generate commentary text based on the analysis results. The generation unit generates explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. The generation unit, for example, uses a generation AI to generate explanatory text using shogi-specific expressions. The provision unit provides the explanatory text generated by the generation unit. The provision unit includes, for example, a customization function according to the user's shogi skill level. The provision unit generates summary text and review questions. The provision unit provides, for example, the generated explanatory text to the user, enabling the user to learn efficiently. As a result, the shogi commentary system according to this embodiment can efficiently collect and analyze information about shogi positions and candidate moves, generate explanatory text, and provide it.

[0030] The data collection unit gathers information about shogi positions and candidate moves. Specifically, it has a function to instruct the shogi GUI to think about positions and candidate moves, and transmits the information about positions and candidate moves entered by the user to the shogi AI. The shogi AI thinks based on this information and returns the results to the data collection unit. The data collection unit also plays a role in transmitting the shogi AI's thinking results to shogi analysts. For example, the data collection unit inputs information about positions and candidate moves into the shogi GUI and instructs the shogi AI to think based on that information. This allows the data collection unit to efficiently collect information about shogi positions and candidate moves and provide it to the analysis unit. The data collection unit also has a function to acquire the shogi AI's thinking results in real time and save them to a database as needed. This allows users to refer to past positions and candidate move information, which can be used for shogi research and analysis. Furthermore, the data collection unit provides an interface to simplify user operation and is designed to be intuitive. For example, it supports various input methods such as drag-and-drop and voice input to improve user convenience. This allows the data collection unit to quickly and accurately collect information about shogi positions and candidate moves, thereby improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the collection unit. Specifically, it analyzes information about the shogi position and candidate moves to determine the state of the game and the next candidate move. The analysis unit analyzes the state of the game and the next candidate move based on the thinking results of the shogi AI. For example, it uses an evaluation function for the shogi position to determine the state of the game and select the next candidate move. The evaluation function comprehensively evaluates and quantifies factors such as the arrangement of pieces in the position, the status of captured pieces, and the control of the board. The analysis unit uses this evaluation function to determine the superiority or inferiority of the position and select the candidate move to play next. In addition, the analysis unit compares multiple candidate moves based on the thinking results of the shogi AI and selects the most advantageous move. This allows the analysis unit to quickly and accurately analyze the collected information and determine the next candidate move. Furthermore, the analysis unit can perform more accurate analysis by referring to past game data and moves of professional shogi players. For example, based on past game data, the system analyzes win rates and move tendencies in specific situations to select candidate moves for the next move. The analysis unit can also refer to databases of shogi openings and strategies to select the optimal move in a given situation. This allows the analysis unit to analyze information about shogi positions and candidate moves with high accuracy, improving the overall system performance.

[0032] The generation unit generates explanatory text based on the results analyzed by the analysis unit. Specifically, it uses a generation AI to generate explanatory text based on the analysis results. The generation AI generates explanatory text using a database that references expressions specific to shogi from previously published shogi books and journalists' game commentaries. For example, the generation AI generates explanatory text using expressions specific to shogi. The generation AI generates appropriate explanatory text based on the position and candidate moves provided by the analysis unit. The generation AI has learned shogi terminology and expressions, and can generate explanatory text in natural language. For example, the generation AI generates explanatory text such as, "In this position, White has the advantage. The candidate move to play next is △△." The generation unit also has a function to customize the generated explanatory text according to the user's shogi skill level. For example, for beginners, it generates explanatory text that includes explanations of basic terminology and strategies, and for advanced players, it generates explanatory text that includes more specialized content and detailed analysis results. In this way, the generation unit can provide appropriate explanatory text according to the user's shogi skill level. Furthermore, the generation unit also has a function to save the generated explanatory texts in a database so that they can be referenced in the future. This allows users to refer to past explanatory texts to aid in learning and studying shogi. As a result, the generation unit can generate high-quality explanatory texts based on the analysis results, improving the overall performance of the system.

[0033] The service provider provides explanatory texts generated by the generation unit. Specifically, it has a customization function that adapts to the user's skill level and provides the generated explanatory texts to the user. The service provider has functions to generate summary texts and review problems, for example. The generated explanatory texts are provided in a way that allows the user to learn efficiently. For example, the service provider customizes the generated explanatory texts according to the user's skill level, providing explanatory texts that include basic terminology and strategies for beginners, and explanatory texts that include more specialized content and detailed analysis results for advanced players. In addition to providing the generated explanatory texts to the user, the service provider also has a function to collect user feedback and continuously improve the accuracy and effectiveness of the explanatory texts. For example, by providing feedback after the user has read the explanatory text, the service provider can improve the content and expression of the explanatory text and support more effective learning. Furthermore, the service provider also has a function to provide the generated explanatory texts in various formats. For example, by providing explanatory texts not only in text format but also in audio and video format, it can support flexible learning according to the user's learning style. In this way, the service provider can efficiently provide the generated explanatory texts to the user and support the learning and study of shogi.

[0034] The generation unit can generate explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. For example, the generation unit's AI generates explanatory text using shogi-specific expressions. The generation unit's AI generates explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. For example, the generation unit's AI generates explanatory text using shogi-specific expressions. The generation unit's AI generates explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. As a result, the generation unit can provide users with easy-to-understand explanations by generating explanatory text using shogi-specific expressions.

[0035] The service provider can include customization features that can be tailored to the user's skill level. For example, the service provider can customize the content of the explanatory text according to the user's skill level. The service provider can customize the content of the explanatory text according to the user's skill level. For example, the service provider can customize the content of the explanatory text according to the user's skill level. The service provider can customize the content of the explanatory text according to the user's skill level. This allows the service provider to cater to a wide range of users, from beginners to professional players, by providing customization features that can be tailored to the user's skill level.

[0036] The service provider can generate summary texts and review questions. For example, the service provider provides text summarizing what the user has learned. The service provider provides text summarizing what the user has learned. For example, the service provider provides text summarizing what the user has learned. The service provider provides text summarizing what the user has learned. This allows the service provider to generate summary texts and review questions, enabling the user to learn efficiently.

[0037] The analysis unit can analyze information about the shogi position and candidate moves, and determine the state of the game and the next candidate move. The analysis unit can, for example, analyze information about the shogi position and candidate moves, and determine the state of the game and the next candidate move. The analysis unit can, for example, analyze information about the shogi position and candidate moves, and determine the state of the game and the next candidate move. The analysis unit can, for example, analyze information about the shogi position and candidate moves, and determine the state of the game and the next candidate move. By doing so, the analysis unit can provide the user with useful information by analyzing information about the shogi position and candidate moves, and determining the state of the game and the next candidate move.

[0038] The data collection unit can instruct the Shogi GUI to think about the game position and candidate moves. The data collection unit can, for example, instruct the Shogi GUI to think about the game position and candidate moves. The data collection unit can, for example, instruct the Shogi GUI to think about the game position and candidate moves. The data collection unit can, for example, instruct the Shogi GUI to think about the game position and candidate moves. This allows the data collection unit to efficiently collect information by instructing the Shogi GUI to think about the game position and candidate moves.

[0039] The data collection unit can analyze the user's past game history and select the optimal data collection method. For example, the data collection unit can select a data collection method based on the tactics the user has frequently used in the past. The data collection unit can select a data collection method based on the tactics the user has frequently used in the past. For example, the data collection unit can select a data collection method based on the tactics the user has frequently used in the past. The data collection unit can select a data collection method based on the tactics the user has frequently used in the past. This allows the data collection unit to select the optimal data collection method by analyzing the user's past game history.

[0040] The data collection unit can filter the collected game positions and candidate moves based on the user's current skill level and areas of interest. For example, the data collection unit collects game positions and candidate moves of appropriate difficulty according to the user's skill level. The data collection unit collects game positions and candidate moves of appropriate difficulty according to the user's skill level. For example, the data collection unit collects game positions and candidate moves of appropriate difficulty according to the user's skill level. The data collection unit collects game positions and candidate moves of appropriate difficulty according to the user's skill level. This allows the data collection unit to provide appropriate information by filtering based on the user's skill level and areas of interest.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting game positions and candidate moves. For example, if the user is in a specific region, the data collection unit will prioritize the collection of game positions and candidate moves related to that region. The data collection unit will prioritize the collection of game positions and candidate moves related to that region. For example, if the user is in a specific region, the data collection unit will prioritize the collection of game positions and candidate moves related to that region. The data collection unit will prioritize the collection of game positions and candidate moves related to that region if the user is in a specific region. This allows the data collection unit to provide highly relevant information by considering the user's geographical location.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information when collecting game positions and candidate moves. For example, the data collection unit collects game positions and candidate moves that the user has shown interest in on social media. The data collection unit collects game positions and candidate moves that the user has shown interest in on social media. For example, the data collection unit collects game positions and candidate moves that the user has shown interest in on social media. The data collection unit collects game positions and candidate moves that the user has shown interest in on social media. By doing so, the data collection unit can provide relevant information by analyzing the user's social media activity.

[0043] The analysis unit can adjust the level of detail of its analysis based on the importance of the positions and candidate moves during the analysis. For example, the analysis unit performs a detailed analysis on important positions and candidate moves. The analysis unit performs a detailed analysis on important positions and candidate moves. For example, the analysis unit performs a detailed analysis on important positions and candidate moves. The analysis unit performs a detailed analysis on important positions and candidate moves. As a result, the analysis unit can provide appropriate analysis results by adjusting the level of detail of its analysis based on the importance of the positions and candidate moves.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the position and candidate moves during analysis. For example, the analysis unit applies an attack-oriented analysis algorithm to an attacking position. The analysis unit applies an attack-oriented analysis algorithm to an attacking position. For example, the analysis unit applies an attack-oriented analysis algorithm to an attacking position. The analysis unit applies an attack-oriented analysis algorithm to an attacking position. In this way, the analysis unit can provide appropriate analysis results by applying different analysis algorithms depending on the category of the position and candidate moves.

[0045] The analysis unit can determine the priority of analysis based on the timing of submissions of positions and candidate moves during the analysis. For example, the analysis unit prioritizes the analysis of recently submitted positions and candidate moves. The analysis unit prioritizes the analysis of recently submitted positions and candidate moves. For example, the analysis unit prioritizes the analysis of recently submitted positions and candidate moves. The analysis unit prioritizes the analysis of recently submitted positions and candidate moves. This allows the analysis unit to provide appropriate analysis results by determining the priority of analysis based on the timing of submissions of positions and candidate moves.

[0046] The analysis unit can adjust the order of analysis based on the relationships between positions and candidate moves during the analysis. For example, the analysis unit prioritizes analyzing positions and candidate moves that are highly related. The analysis unit prioritizes analyzing positions and candidate moves that are highly related. For example, the analysis unit prioritizes analyzing positions and candidate moves that are highly related. The analysis unit prioritizes analyzing positions and candidate moves that are highly related. In this way, the analysis unit can provide appropriate analysis results by adjusting the order of analysis based on the relationships between positions and candidate moves.

[0047] The generation unit can adjust the level of detail in the explanatory text based on the importance of the position and candidate moves when generating the explanatory text. For example, the generation unit generates detailed explanatory text for important positions and candidate moves. The generation unit generates detailed explanatory text for important positions and candidate moves. For example, the generation unit generates detailed explanatory text for important positions and candidate moves. The generation unit generates detailed explanatory text for important positions and candidate moves. In this way, the generation unit can provide appropriate explanatory text by adjusting the level of detail in the explanatory text based on the importance of the position and candidate moves.

[0048] The generation unit can apply different generation algorithms depending on the category of the position and candidate moves when generating explanatory text. For example, the generation unit generates an attack-focused explanatory text for an attacking position. The generation unit generates an attack-focused explanatory text for an attacking position. For example, the generation unit generates an attack-focused explanatory text for an attacking position. The generation unit generates an attack-focused explanatory text for an attacking position. In this way, the generation unit can provide appropriate explanatory text by applying different generation algorithms depending on the category of the position and candidate moves.

[0049] The generation unit can determine the priority of explanatory texts based on the timing of the submission of the positions and candidate moves when generating explanatory texts. For example, the generation unit will prioritize explaining recently submitted positions and candidate moves. The generation unit will prioritize explaining recently submitted positions and candidate moves. For example, the generation unit will prioritize explaining recently submitted positions and candidate moves. The generation unit will prioritize explaining recently submitted positions and candidate moves. In this way, the generation unit can provide appropriate explanatory texts by determining the priority of explanatory texts based on the timing of the submission of the positions and candidate moves.

[0050] The generation unit can adjust the order of explanatory texts based on the relationships between positions and candidate moves when generating explanatory texts. For example, the generation unit prioritizes explaining positions and candidate moves that are highly relevant. The generation unit prioritizes explaining positions and candidate moves that are highly relevant. For example, the generation unit prioritizes explaining positions and candidate moves that are highly relevant. The generation unit prioritizes explaining positions and candidate moves that are highly relevant. In this way, the generation unit can provide appropriate explanatory texts by adjusting the order of explanatory texts based on the relationships between positions and candidate moves.

[0051] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can propose the optimal display method based on the display methods the user has used in the past. The service provider can propose the optimal display method based on the display methods the user has used in the past. For example, the service provider can propose the optimal display method based on the display methods the user has used in the past. The service provider can propose the optimal display method based on the display methods the user has used in the past. In this way, the service provider can provide the optimal display method by referring to the user's past operation history.

[0052] The service provider can customize the content of the explanatory text according to the user's skill level at the time of delivery. For example, the service provider can provide basic explanations for beginners. The service provider can provide basic explanations for beginners. For example, the service provider can provide basic explanations for beginners. The service provider can provide basic explanations for beginners. This allows the service provider to provide appropriate explanatory texts by customizing the content of the explanatory text according to the user's skill level.

[0053] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. The service provider will provide a display method that matches the screen size if the user is using a smartphone. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. The service provider will provide a display method that matches the screen size if the user is using a smartphone. This allows the service provider to provide the optimal display method by taking into account the user's device information.

[0054] The service provider can generate summary texts and review questions at the time of delivery, enabling users to learn efficiently. For example, the service provider provides a text summarizing what the user has learned. The service provider provides a text summarizing what the user has learned. For example, the service provider provides a text summarizing what the user has learned. The service provider provides a text summarizing what the user has learned. This allows the service provider to generate summary texts and review questions, enabling users to learn efficiently.

[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 analysis unit can adjust its analysis algorithm by referring to the user's past game history when analyzing game positions and candidate moves. For example, it can select an analysis algorithm based on strategies the user has frequently used in the past. It can perform special analysis on positions the user has struggled with in the past. It can perform detailed analysis on strategies the user has succeeded with in the past. In this way, the analysis unit can provide appropriate analysis results by referring to the user's past game history.

[0057] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location when gathering game positions and candidate moves. For example, if the user is in a specific region, it will prioritize collecting game positions and candidate moves related to that region. If the user is in a specific region, it will collect information based on the strategies used by shogi players in that region. If the user is in a specific region, it will prioritize collecting information on shogi tournaments in that region. In this way, the data collection unit can provide highly relevant information by considering the user's geographical location.

[0058] The generation unit can apply different generation algorithms depending on the position and candidate move category when generating explanatory text. For example, it can generate an offensive-focused explanatory text for an offensive position, a defensive-focused explanatory text for a defensive position, and an explanatory text specializing in mid-game strategy for a mid-game position. In this way, the generation unit can provide appropriate explanatory text by applying different generation algorithms depending on the position and candidate move category.

[0059] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, it will provide a display method that matches the screen size. If the user is using a tablet, it will provide a display method optimized for tablets. If the user is using a desktop, it will provide a display method optimized for large screens. In this way, the service provider can provide the optimal display method by taking into account the user's device information.

[0060] The service provider can generate summary texts and review questions at the time of delivery, enabling users to learn efficiently. For example, it can provide text summarizing what the user has learned, review questions based on what the user has learned, and quizzes to help the user review what they have learned. In this way, the service provider can enable users to learn efficiently by generating summary texts and review questions.

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

[0062] Step 1: The data collection unit collects information about the shogi position and candidate moves. For example, the data collection unit instructs the shogi GUI to think about the position and candidate moves, and transmits the shogi AI's thinking results to the shogi analyst. The data collection unit also inputs information about the position and candidate moves into the shogi GUI and instructs the shogi AI to think based on that information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes information about the shogi position and candidate moves to determine the state of the game and the next candidate move. The analysis unit analyzes the state of the game and the next candidate move based on the thinking results of the shogi AI. Furthermore, the analysis unit uses an evaluation function for the shogi position to determine the state of the game and selects the next candidate move. Step 3: The generation unit generates explanatory text based on the results analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate explanatory text based on the analysis results. The generation unit generates explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. The generation unit uses a generation AI to generate explanatory text using shogi-specific expressions. Step 4: The provider unit provides the explanatory text generated by the generator unit. For example, the provider unit has a customization function that can be tailored to the user's skill level and generates summary texts and review problems. The provider unit provides the generated explanatory text to the user, enabling the user to learn efficiently.

[0063] (Example of form 2) The Shogi commentary system according to an embodiment of the present invention is a system that uses a generating AI to provide commentary on the position of a Shogi game, indicating which side has the advantage, and on candidate moves to play next in that position. The Shogi commentary system collects and analyzes information about the Shogi game position and candidate moves, generates and provides commentary, thereby providing commentary that is easy for the user to understand. For example, the Shogi commentary system instructs the Shogi GUI to think about the position and candidate moves. The Shogi commentary system transmits the thinking results of the Shogi AI to a Shogi analyst, and the generating AI outputs commentary. Furthermore, the Shogi commentary system stores Shogi-specific expressions from previously published Shogi books and journalists' game reports in a database, and the generating AI refers to this to provide commentary that is easy for the user to understand. The Shogi commentary system generates summary texts and review problems, allowing users to review the analysis and commentary so far. As a result, the Shogi commentary system can provide even greater value to Shogi AI for people of various skill levels, enabling a wide range of users, from beginners to professional players, to efficiently utilize Shogi AI and improve their Shogi skills. Furthermore, the shogi commentary system has the potential to be expanded to shogi enthusiasts overseas in the future, and can also be applied to other games such as chess and go. This will allow the shogi commentary system to provide users with easy-to-understand explanations and enable the efficient use of shogi AI.

[0064] The shogi commentary system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information about the shogi position and candidate moves. The collection unit, for example, instructs the shogi GUI to think about the position and candidate moves. The collection unit transmits the thinking results of the shogi AI to a shogi analyst. The collection unit, for example, inputs information about the position and candidate moves into the shogi GUI and instructs the shogi AI to think based on that information. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, analyzes information about the shogi position and candidate moves and determines the state of the game and the candidate move to play next. The analysis unit analyzes the state of the game and the candidate move to play next based on the thinking results of the shogi AI. The analysis unit, for example, uses an evaluation function for the shogi position to determine the state of the game and selects the candidate move to play next. The generation unit generates commentary text based on the results analyzed by the analysis unit. The generation unit, for example, uses a generation AI to generate commentary text based on the analysis results. The generation unit generates explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. The generation unit, for example, uses a generation AI to generate explanatory text using shogi-specific expressions. The provision unit provides the explanatory text generated by the generation unit. The provision unit includes, for example, a customization function according to the user's shogi skill level. The provision unit generates summary text and review questions. The provision unit provides, for example, the generated explanatory text to the user, enabling the user to learn efficiently. As a result, the shogi commentary system according to this embodiment can efficiently collect and analyze information about shogi positions and candidate moves, generate explanatory text, and provide it.

[0065] The data collection unit gathers information about shogi positions and candidate moves. Specifically, it has a function to instruct the shogi GUI to think about positions and candidate moves, and transmits the information about positions and candidate moves entered by the user to the shogi AI. The shogi AI thinks based on this information and returns the results to the data collection unit. The data collection unit also plays a role in transmitting the shogi AI's thinking results to shogi analysts. For example, the data collection unit inputs information about positions and candidate moves into the shogi GUI and instructs the shogi AI to think based on that information. This allows the data collection unit to efficiently collect information about shogi positions and candidate moves and provide it to the analysis unit. The data collection unit also has a function to acquire the shogi AI's thinking results in real time and save them to a database as needed. This allows users to refer to past positions and candidate move information, which can be used for shogi research and analysis. Furthermore, the data collection unit provides an interface to simplify user operation and is designed to be intuitive. For example, it supports various input methods such as drag-and-drop and voice input to improve user convenience. This allows the data collection unit to quickly and accurately collect information about shogi positions and candidate moves, thereby improving the overall performance of the system.

[0066] The analysis unit analyzes the information collected by the collection unit. Specifically, it analyzes information about the shogi position and candidate moves to determine the state of the game and the next candidate move. The analysis unit analyzes the state of the game and the next candidate move based on the thinking results of the shogi AI. For example, it uses an evaluation function for the shogi position to determine the state of the game and select the next candidate move. The evaluation function comprehensively evaluates and quantifies factors such as the arrangement of pieces in the position, the status of captured pieces, and the control of the board. The analysis unit uses this evaluation function to determine the superiority or inferiority of the position and select the candidate move to play next. In addition, the analysis unit compares multiple candidate moves based on the thinking results of the shogi AI and selects the most advantageous move. This allows the analysis unit to quickly and accurately analyze the collected information and determine the next candidate move. Furthermore, the analysis unit can perform more accurate analysis by referring to past game data and moves of professional shogi players. For example, based on past game data, the system analyzes win rates and move tendencies in specific situations to select candidate moves for the next move. The analysis unit can also refer to databases of shogi openings and strategies to select the optimal move in a given situation. This allows the analysis unit to analyze information about shogi positions and candidate moves with high accuracy, improving the overall system performance.

[0067] The generation unit generates explanatory text based on the results analyzed by the analysis unit. Specifically, it uses a generation AI to generate explanatory text based on the analysis results. The generation AI generates explanatory text using a database that references expressions specific to shogi from previously published shogi books and journalists' game commentaries. For example, the generation AI generates explanatory text using expressions specific to shogi. The generation AI generates appropriate explanatory text based on the position and candidate moves provided by the analysis unit. The generation AI has learned shogi terminology and expressions, and can generate explanatory text in natural language. For example, the generation AI generates explanatory text such as, "In this position, White has the advantage. The candidate move to play next is △△." The generation unit also has a function to customize the generated explanatory text according to the user's shogi skill level. For example, for beginners, it generates explanatory text that includes explanations of basic terminology and strategies, and for advanced players, it generates explanatory text that includes more specialized content and detailed analysis results. In this way, the generation unit can provide appropriate explanatory text according to the user's shogi skill level. Furthermore, the generation unit also has a function to save the generated explanatory texts in a database so that they can be referenced in the future. This allows users to refer to past explanatory texts to aid in learning and studying shogi. As a result, the generation unit can generate high-quality explanatory texts based on the analysis results, improving the overall performance of the system.

[0068] The service provider provides explanatory texts generated by the generation unit. Specifically, it has a customization function that adapts to the user's skill level and provides the generated explanatory texts to the user. The service provider has functions to generate summary texts and review problems, for example. The generated explanatory texts are provided in a way that allows the user to learn efficiently. For example, the service provider customizes the generated explanatory texts according to the user's skill level, providing explanatory texts that include basic terminology and strategies for beginners, and explanatory texts that include more specialized content and detailed analysis results for advanced players. In addition to providing the generated explanatory texts to the user, the service provider also has a function to collect user feedback and continuously improve the accuracy and effectiveness of the explanatory texts. For example, by providing feedback after the user has read the explanatory text, the service provider can improve the content and expression of the explanatory text and support more effective learning. Furthermore, the service provider also has a function to provide the generated explanatory texts in various formats. For example, by providing explanatory texts not only in text format but also in audio and video format, it can support flexible learning according to the user's learning style. In this way, the service provider can efficiently provide the generated explanatory texts to the user and support the learning and study of shogi.

[0069] The generation unit can generate explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. For example, the generation unit's AI generates explanatory text using shogi-specific expressions. The generation unit's AI generates explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. For example, the generation unit's AI generates explanatory text using shogi-specific expressions. The generation unit's AI generates explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. As a result, the generation unit can provide users with easy-to-understand explanations by generating explanatory text using shogi-specific expressions.

[0070] The service provider can include customization features that can be tailored to the user's skill level. For example, the service provider can customize the content of the explanatory text according to the user's skill level. The service provider can customize the content of the explanatory text according to the user's skill level. For example, the service provider can customize the content of the explanatory text according to the user's skill level. The service provider can customize the content of the explanatory text according to the user's skill level. This allows the service provider to cater to a wide range of users, from beginners to professional players, by providing customization features that can be tailored to the user's skill level.

[0071] The service provider can generate summary texts and review questions. For example, the service provider provides text summarizing what the user has learned. The service provider provides text summarizing what the user has learned. For example, the service provider provides text summarizing what the user has learned. The service provider provides text summarizing what the user has learned. This allows the service provider to generate summary texts and review questions, enabling the user to learn efficiently.

[0072] The analysis unit can analyze information about the shogi position and candidate moves, and determine the state of the game and the next candidate move. The analysis unit can, for example, analyze information about the shogi position and candidate moves, and determine the state of the game and the next candidate move. The analysis unit can, for example, analyze information about the shogi position and candidate moves, and determine the state of the game and the next candidate move. The analysis unit can, for example, analyze information about the shogi position and candidate moves, and determine the state of the game and the next candidate move. By doing so, the analysis unit can provide the user with useful information by analyzing information about the shogi position and candidate moves, and determining the state of the game and the next candidate move.

[0073] The data collection unit can instruct the Shogi GUI to think about the game position and candidate moves. The data collection unit can, for example, instruct the Shogi GUI to think about the game position and candidate moves. The data collection unit can, for example, instruct the Shogi GUI to think about the game position and candidate moves. The data collection unit can, for example, instruct the Shogi GUI to think about the game position and candidate moves. This allows the data collection unit to efficiently collect information by instructing the Shogi GUI to think about the game position and candidate moves.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of collecting game states and candidate moves based on the estimated emotions. For example, if the user is focused, the data collection unit can increase the frequency of collection to provide more detailed information. The data collection unit can increase the frequency of collection to provide more detailed information if the user is focused. For example, if the user is focused, the data collection unit can increase the frequency of collection to provide more detailed information if the user is focused. The data collection unit can increase the frequency of collection to provide more detailed information if the user is focused. This allows the data collection unit to provide information at the appropriate time by adjusting the collection timing based on 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.

[0075] The data collection unit can analyze the user's past game history and select the optimal data collection method. For example, the data collection unit can select a data collection method based on the tactics the user has frequently used in the past. The data collection unit can select a data collection method based on the tactics the user has frequently used in the past. For example, the data collection unit can select a data collection method based on the tactics the user has frequently used in the past. The data collection unit can select a data collection method based on the tactics the user has frequently used in the past. This allows the data collection unit to select the optimal data collection method by analyzing the user's past game history.

[0076] The data collection unit can filter the collected game positions and candidate moves based on the user's current skill level and areas of interest. For example, the data collection unit collects game positions and candidate moves of appropriate difficulty according to the user's skill level. The data collection unit collects game positions and candidate moves of appropriate difficulty according to the user's skill level. For example, the data collection unit collects game positions and candidate moves of appropriate difficulty according to the user's skill level. The data collection unit collects game positions and candidate moves of appropriate difficulty according to the user's skill level. This allows the data collection unit to provide appropriate information by filtering based on the user's skill level and areas of interest.

[0077] The data collection unit can estimate the user's emotions and determine the priority of the positions and candidate moves to collect based on the estimated user emotions. For example, if the user is focused, the data collection unit will prioritize collecting important positions and candidate moves. For example, if the user is focused, the data collection unit will prioritize collecting important positions and candidate moves. For example, if the user is focused, the data collection unit will prioritize collecting important positions and candidate moves. For example, if the user is focused, the data collection unit will prioritize collecting important positions and candidate moves. This allows the data collection unit to provide appropriate information by determining the priority of the positions and candidate moves to collect based on 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.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting game positions and candidate moves. For example, if the user is in a specific region, the data collection unit will prioritize the collection of game positions and candidate moves related to that region. The data collection unit will prioritize the collection of game positions and candidate moves related to that region. For example, if the user is in a specific region, the data collection unit will prioritize the collection of game positions and candidate moves related to that region. The data collection unit will prioritize the collection of game positions and candidate moves related to that region if the user is in a specific region. This allows the data collection unit to provide highly relevant information by considering the user's geographical location.

[0079] The data collection unit can analyze the user's social media activity and collect relevant information when collecting game positions and candidate moves. For example, the data collection unit collects game positions and candidate moves that the user has shown interest in on social media. The data collection unit collects game positions and candidate moves that the user has shown interest in on social media. For example, the data collection unit collects game positions and candidate moves that the user has shown interest in on social media. The data collection unit collects game positions and candidate moves that the user has shown interest in on social media. By doing so, the data collection unit can provide relevant information by analyzing the user's social media activity.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and easy-to-understand analysis result. The analysis unit provides a simple and easy-to-understand analysis result if the user is nervous. For example, if the user is nervous, the analysis unit provides a simple and easy-to-understand analysis result. The analysis unit provides a simple and easy-to-understand analysis result if the user is nervous. This allows the analysis unit to provide appropriate analysis results by adjusting the presentation of the analysis based on 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.

[0081] The analysis unit can adjust the level of detail of its analysis based on the importance of the positions and candidate moves during the analysis. For example, the analysis unit performs a detailed analysis on important positions and candidate moves. The analysis unit performs a detailed analysis on important positions and candidate moves. For example, the analysis unit performs a detailed analysis on important positions and candidate moves. The analysis unit performs a detailed analysis on important positions and candidate moves. As a result, the analysis unit can provide appropriate analysis results by adjusting the level of detail of its analysis based on the importance of the positions and candidate moves.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the position and candidate moves during analysis. For example, the analysis unit applies an attack-oriented analysis algorithm to an attacking position. The analysis unit applies an attack-oriented analysis algorithm to an attacking position. For example, the analysis unit applies an attack-oriented analysis algorithm to an attacking position. The analysis unit applies an attack-oriented analysis algorithm to an attacking position. In this way, the analysis unit can provide appropriate analysis results by applying different analysis algorithms depending on the category of the position and candidate moves.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. The analysis unit will provide a short, concise analysis if the user is in a hurry. The analysis unit will provide a short, concise analysis if the user is in a hurry. The analysis unit will provide a short, concise analysis if the user is in a hurry. This allows the analysis unit to provide appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can determine the priority of analysis based on the timing of submissions of positions and candidate moves during the analysis. For example, the analysis unit prioritizes the analysis of recently submitted positions and candidate moves. The analysis unit prioritizes the analysis of recently submitted positions and candidate moves. For example, the analysis unit prioritizes the analysis of recently submitted positions and candidate moves. The analysis unit prioritizes the analysis of recently submitted positions and candidate moves. This allows the analysis unit to provide appropriate analysis results by determining the priority of analysis based on the timing of submissions of positions and candidate moves.

[0085] The analysis unit can adjust the order of analysis based on the relationships between positions and candidate moves during the analysis. For example, the analysis unit prioritizes analyzing positions and candidate moves that are highly related. The analysis unit prioritizes analyzing positions and candidate moves that are highly related. For example, the analysis unit prioritizes analyzing positions and candidate moves that are highly related. The analysis unit prioritizes analyzing positions and candidate moves that are highly related. In this way, the analysis unit can provide appropriate analysis results by adjusting the order of analysis based on the relationships between positions and candidate moves.

[0086] The generation unit can estimate the user's emotions and adjust the way the explanatory text is expressed based on the estimated emotions. For example, if the user is nervous, the generation unit will provide a simple and easy-to-read explanatory text. The generation unit will provide a simple and easy-to-read explanatory text if the user is nervous. For example, if the user is nervous, the generation unit will provide a simple and easy-to-read explanatory text. The generation unit will provide a simple and easy-to-read explanatory text if the user is nervous. In this way, the generation unit can provide an appropriate explanatory text by adjusting the way the explanatory text is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The generation unit can adjust the level of detail in the explanatory text based on the importance of the position and candidate moves when generating the explanatory text. For example, the generation unit generates detailed explanatory text for important positions and candidate moves. The generation unit generates detailed explanatory text for important positions and candidate moves. For example, the generation unit generates detailed explanatory text for important positions and candidate moves. The generation unit generates detailed explanatory text for important positions and candidate moves. In this way, the generation unit can provide appropriate explanatory text by adjusting the level of detail in the explanatory text based on the importance of the position and candidate moves.

[0088] The generation unit can apply different generation algorithms depending on the category of the position and candidate moves when generating explanatory text. For example, the generation unit generates an attack-focused explanatory text for an attacking position. The generation unit generates an attack-focused explanatory text for an attacking position. For example, the generation unit generates an attack-focused explanatory text for an attacking position. The generation unit generates an attack-focused explanatory text for an attacking position. In this way, the generation unit can provide appropriate explanatory text by applying different generation algorithms depending on the category of the position and candidate moves.

[0089] The generation unit can estimate the user's emotions and adjust the length of the explanatory text based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise explanatory text. The generation unit will generate a short, concise explanatory text if the user is in a hurry. The generation unit will generate a short, concise explanatory text if the user is in a hurry. The generation unit will generate a short, concise explanatory text if the user is in a hurry. In this way, the generation unit can provide an appropriate explanatory text by adjusting the length of the explanatory text based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The generation unit can determine the priority of explanatory texts based on the timing of the submission of the positions and candidate moves when generating explanatory texts. For example, the generation unit will prioritize explaining recently submitted positions and candidate moves. The generation unit will prioritize explaining recently submitted positions and candidate moves. For example, the generation unit will prioritize explaining recently submitted positions and candidate moves. The generation unit will prioritize explaining recently submitted positions and candidate moves. In this way, the generation unit can provide appropriate explanatory texts by determining the priority of explanatory texts based on the timing of the submission of the positions and candidate moves.

[0091] The generation unit can adjust the order of explanatory texts based on the relationships between positions and candidate moves when generating explanatory texts. For example, the generation unit prioritizes explaining positions and candidate moves that are highly relevant. The generation unit prioritizes explaining positions and candidate moves that are highly relevant. For example, the generation unit prioritizes explaining positions and candidate moves that are highly relevant. The generation unit prioritizes explaining positions and candidate moves that are highly relevant. In this way, the generation unit can provide appropriate explanatory texts by adjusting the order of explanatory texts based on the relationships between positions and candidate moves.

[0092] The service provider can estimate the user's emotions and adjust the display method of the explanatory text based on the estimated user emotions. For example, if the user is nervous, the service provider will provide a simple and highly visible display method. The service provider will provide a simple and highly visible display method if the user is nervous. For example, if the user is nervous, the service provider will provide a simple and highly visible display method. The service provider will provide a simple and highly visible display method if the user is nervous. This allows the service provider to provide an appropriate display method by adjusting the display method of the explanatory text based on 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can propose the optimal display method based on the display methods the user has used in the past. The service provider can propose the optimal display method based on the display methods the user has used in the past. For example, the service provider can propose the optimal display method based on the display methods the user has used in the past. The service provider can propose the optimal display method based on the display methods the user has used in the past. In this way, the service provider can provide the optimal display method by referring to the user's past operation history.

[0094] The service provider can customize the content of the explanatory text according to the user's skill level at the time of delivery. For example, the service provider can provide basic explanations for beginners. The service provider can provide basic explanations for beginners. For example, the service provider can provide basic explanations for beginners. The service provider can provide basic explanations for beginners. This allows the service provider to provide appropriate explanatory texts by customizing the content of the explanatory text according to the user's skill level.

[0095] The service provider can estimate the user's emotions and adjust the instructions for the explanatory text provided based on the estimated emotions. For example, if the user is nervous, the service provider will provide simple and intuitive instructions. For example, if the user is nervous, the service provider will provide simple and intuitive instructions. For example, if the user is nervous, the service provider will provide simple and intuitive instructions. For example, if the user is nervous, the service provider will provide simple and intuitive instructions. This allows the service provider to provide appropriate instructions by adjusting the instructions for the explanatory text provided based on 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.

[0096] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. The service provider will provide a display method that matches the screen size if the user is using a smartphone. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. The service provider will provide a display method that matches the screen size if the user is using a smartphone. This allows the service provider to provide the optimal display method by taking into account the user's device information.

[0097] The service provider can generate summary texts and review questions at the time of delivery, enabling users to learn efficiently. For example, the service provider provides a text summarizing what the user has learned. The service provider provides a text summarizing what the user has learned. For example, the service provider provides a text summarizing what the user has learned. The service provider provides a text summarizing what the user has learned. This allows the service provider to generate summary texts and review questions, enabling users to learn efficiently.

[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 analysis unit can estimate the user's emotions and determine the priority of the analysis based on those emotions. For example, if the user is anxious, it will prioritize analyzing important situations and candidate moves. If the user is relaxed, it will perform a detailed analysis. If the user is focused, it will prioritize analyzing complex situations and candidate moves. In this way, the analysis unit can provide appropriate analysis results by adjusting the priority of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0100] The service provider can estimate the user's emotions and adjust the display method of the explanatory text based on the estimated emotions. For example, if the user is tense, a simple and easy-to-read display method is provided. If the user is relaxed, a detailed explanatory text is provided. If the user is focused, a detailed explanatory text about complex situations and candidate moves is provided. In this way, the service provider can provide an appropriate display method by adjusting the display method of the explanatory text based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0101] The data collection unit can estimate the user's emotions and adjust the type of information it collects based on those emotions. For example, if the user is focused, it collects detailed information about the game situation and potential moves. If the user is relaxed, it collects basic information about the game situation and potential moves. If the user is anxious, it prioritizes collecting information about important situations and potential moves. In this way, the data collection unit can provide appropriate information by adjusting the type of information it collects based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0102] The generation unit can estimate the user's emotions and adjust the content of the explanatory text based on those emotions. For example, if the user is tense, it generates a simple, concise explanatory text. If the user is relaxed, it generates a detailed explanatory text. If the user is focused, it generates a detailed explanatory text about complex situations and candidate moves. In this way, the generation unit can provide appropriate explanatory text by adjusting the content based on the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI, etc.

[0103] The service provider can estimate the user's emotions and adjust the instructions provided based on those emotions. For example, if the user is tense, it can provide simple and intuitive instructions. If the user is relaxed, it can provide detailed instructions. If the user is focused, it can provide complex instructions. In this way, the service provider can provide appropriate instructions by adjusting the instructions based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0104] The analysis unit can adjust its analysis algorithm by referring to the user's past game history when analyzing game positions and candidate moves. For example, it can select an analysis algorithm based on strategies the user has frequently used in the past. It can perform special analysis on positions the user has struggled with in the past. It can perform detailed analysis on strategies the user has succeeded with in the past. In this way, the analysis unit can provide appropriate analysis results by referring to the user's past game history.

[0105] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location when gathering game positions and candidate moves. For example, if the user is in a specific region, it will prioritize collecting game positions and candidate moves related to that region. If the user is in a specific region, it will collect information based on the strategies used by shogi players in that region. If the user is in a specific region, it will prioritize collecting information on shogi tournaments in that region. In this way, the data collection unit can provide highly relevant information by considering the user's geographical location.

[0106] The generation unit can apply different generation algorithms depending on the position and candidate move category when generating explanatory text. For example, it can generate an offensive-focused explanatory text for an offensive position, a defensive-focused explanatory text for a defensive position, and an explanatory text specializing in mid-game strategy for a mid-game position. In this way, the generation unit can provide appropriate explanatory text by applying different generation algorithms depending on the position and candidate move category.

[0107] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, it will provide a display method that matches the screen size. If the user is using a tablet, it will provide a display method optimized for tablets. If the user is using a desktop, it will provide a display method optimized for large screens. In this way, the service provider can provide the optimal display method by taking into account the user's device information.

[0108] The service provider can generate summary texts and review questions at the time of delivery, enabling users to learn efficiently. For example, it can provide text summarizing what the user has learned, review questions based on what the user has learned, and quizzes to help the user review what they have learned. In this way, the service provider can enable users to learn efficiently by generating summary texts and review questions.

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

[0110] Step 1: The data collection unit collects information about the shogi position and candidate moves. For example, the data collection unit instructs the shogi GUI to think about the position and candidate moves, and transmits the shogi AI's thinking results to the shogi analyst. The data collection unit also inputs information about the position and candidate moves into the shogi GUI and instructs the shogi AI to think based on that information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes information about the shogi position and candidate moves to determine the state of the game and the next candidate move. The analysis unit analyzes the state of the game and the next candidate move based on the thinking results of the shogi AI. Furthermore, the analysis unit uses an evaluation function for the shogi position to determine the state of the game and selects the next candidate move. Step 3: The generation unit generates explanatory text based on the results analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate explanatory text based on the analysis results. The generation unit generates explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. The generation unit uses a generation AI to generate explanatory text using shogi-specific expressions. Step 4: The provider unit provides the explanatory text generated by the generator unit. For example, the provider unit has a customization function that can be tailored to the user's skill level and generates summary texts and review problems. The provider unit provides the generated explanatory text to the user, enabling the user to learn efficiently.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects information about the shogi position and candidate moves. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates an explanatory text based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated explanatory text to the user. 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 signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

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

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

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects information about the shogi position and candidate moves. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an explanatory text based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated explanatory text to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

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

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

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects information about the shogi position and candidate moves. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an explanatory text based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated explanatory text to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects information about the shogi position and candidate moves. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an explanatory text based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated explanatory text to the user. 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.

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

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

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

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

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

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0182] (Note 1) A collection department that gathers information about shogi positions and candidate moves, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates explanatory text based on the results of the analysis performed by the aforementioned analysis unit, A providing unit that provides the explanatory text generated by the generation unit, Equipped with A system characterized by the following features. (Note 2) The generating unit is This system generates explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Features customization options tailored to the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Generate summary texts and review questions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It analyzes information about the shogi position and potential moves to determine the state of the game and the next potential move. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Instruct the Shogi GUI to think about the current position and potential moves. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting game situations and candidate moves based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past game history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting game positions and candidate moves, 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 collection unit is The system estimates the user's emotions and determines the priority of the situations and candidate moves to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting game states and candidate moves, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting game situations and candidate moves, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the position and candidate moves. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the position and the category of candidate moves. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on the position and the timing of the submission of candidate moves. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between positions and candidate moves. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the user's emotions and adjusts the wording of the explanatory text based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating explanatory text, the level of detail in the text is adjusted based on the importance of the position and candidate moves. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating explanatory text, different generation algorithms are applied depending on the category of the position and candidate moves. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the explanatory text based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating explanatory texts, the priority of the explanatory texts is determined based on the position and the timing of the submission of candidate moves. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating explanatory text, the order of the explanatory text is adjusted based on the relationship between the position and candidate moves. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the explanatory text is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the game, the content of the explanatory text will be customized according to the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the instructions provided in the explanatory text based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When the materials are provided, summary texts and review questions are generated to enable users to learn efficiently. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A collection department that gathers information about shogi positions and candidate moves, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates explanatory text based on the results of the analysis performed by the aforementioned analysis unit, A providing unit that provides the explanatory text generated by the generation unit, Equipped with A system characterized by the following features.

2. The generating unit is This system generates explanatory text using a database that references shogi-specific expressions from previously published shogi books and journalists' game commentaries. The system according to feature 1.

3. The aforementioned supply unit is, Features customization options tailored to the user's skill level. The system according to feature 1.

4. The aforementioned supply unit is, Generate summary texts and review questions. The system according to feature 1.

5. The aforementioned analysis unit, It analyzes information about the shogi position and potential moves to determine the state of the game and the next potential move. The system according to feature 1.

6. The aforementioned collection unit is Instruct the Shogi GUI to think about the current position and potential moves. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting game situations and candidate moves based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past game history and select the optimal data collection method. The system according to feature 1.

Images