system

Abstract

The system according to this embodiment aims to enable viewers to gain a deep, real-time understanding of tactical points and player movements while watching a soccer match. [Solution] The system according to this embodiment comprises an explanation unit, an evaluation unit, and a question answering unit. The explanation unit immediately explains the team's formation and tactical changes in accordance with the progress of the match. The evaluation unit evaluates the movements, roles, and performance of each player based on data. The question answering unit allows users to ask questions on the spot, and the AI ​​provides immediate answers.

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 and includes 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to deeply understand tactical points and player movements in real time during a soccer game.

[0005] The system according to the embodiment aims to enable a deep understanding of tactical points and player movements in real time during a soccer game.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an explanation unit, an evaluation unit, and a question-answering unit. The explanation unit immediately explains the team's formation and tactical changes in accordance with the progress of the match. The evaluation unit evaluates the movements, roles, and performance of each player based on data. The question-answering unit allows users to ask questions on the spot, and the AI ​​provides immediate answers. [Effects of the Invention]

[0007] The system according to this embodiment can enable viewers to gain a deep, real-time understanding of tactical points and player movements while watching a soccer match. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 three or more matters are connected and expressed 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 reception 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 reception 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 TactiFoot system according to an embodiment of the present invention is an AI agent that provides real-time tactical commentary and live analysis of overseas soccer matches. This TactiFoot system provides advanced analysis and easy-to-understand commentary so that users can deeply understand tactical points and player movements while watching a match. This makes watching soccer a more intellectually stimulating and enjoyable experience, and can be enjoyed by a wide range of people, from fans interested in tactics to beginners. For example, while the user is watching a match, the TactiFoot system immediately explains the team's formation and tactical changes in real time as the match progresses. For example, if the team changes its formation during the match, the TactiFoot system will explain the intention and effect of that change. Next, the TactiFoot system evaluates the movements, roles, and performance of each player based on data. For example, it will explain in real time what kind of movements a particular player is making and how their role is changing. Furthermore, the TactiFoot system has an interactive question-answering function that allows users to ask questions on the spot. For example, when a user enters a question, the TactiFoot system will answer it immediately. For example, in response to a question such as "What is this player's role?", the TactiFoot system will explain the player's role and performance. Furthermore, the TactiFoot system compares past match data with current matches to analyze tactical changes and trends. For example, it compares tactics used in past matches with those used in current matches, explaining the differences and evolution. In addition, the TactiFoot system supports multiple languages, providing commentary in multiple languages, including Japanese, even for matches in overseas leagues. This allows many users to enjoy the matches despite language barriers. Thus, the TactiFoot system, with its features such as real-time tactical commentary, individual player analysis, interactive question and answer, comparison with past data, and multi-language support, makes watching soccer a more intellectually stimulating and enjoyable experience. In this way, the TactiFoot system enables users to gain a deeper understanding of tactical points and player movements while watching matches, making soccer a more intellectually stimulating and enjoyable experience.

[0029] The TactiFoot system according to this embodiment comprises an explanation unit, an evaluation unit, and a question answering unit. The explanation unit immediately explains changes in the team's formation and tactics in accordance with the progress of the match. For example, if the team changes its formation during the match, the explanation unit explains the intention and effect of that change. For example, the explanation unit can explain changes in formation and tactics in real time according to the progress of the match. For example, the explanation unit can also emphasize and explain changes in tactics when the flow of the match changes. For example, if an important play occurs, the explanation unit can immediately explain the tactical significance of that play. The evaluation unit evaluates the movements, roles, and performance of each player based on data. For example, the evaluation unit explains in real time what kind of movements a particular player is making and how their role is changing. For example, the evaluation unit can collect player performance data in real time and update the evaluation. For example, the evaluation unit can also change the evaluation of players in real time according to the progress of the match. For example, if an important play occurs, the evaluation unit can also reflect the impact of that play in the evaluation. The question-answering unit allows users to ask questions on the spot, and the AI ​​provides immediate answers. For example, in response to a user's question, "What is this player's role?", the question-answering unit will explain the player's role and performance. For example, if a user inputs a question during a match, the AI ​​can provide an immediate answer. For example, if a user asks a question about past match data, the question-answering unit can provide an answer based on that data. As a result, the TactiFoot system according to this embodiment can provide commentary, evaluation, and question-answering in real time as the match progresses.

[0030] The commentary team will provide immediate explanations of team formation and tactical changes as the match progresses. Specifically, if a team changes formation during a match, they will provide a detailed explanation of the intention and effect of that change. For example, if the formation changes from 4-4-2 to 3-5-2, the commentary team will explain whether the change is aimed at strengthening defense or diversifying attack. They will also provide specific explanations of changes in player positions and roles, detailing which players move to which positions and what roles they take on. Furthermore, when the flow of the match changes, they can highlight and explain tactical changes. For example, if the opposing team goes on the offensive, they will explain how their own team will strengthen their defense, or conversely, how they will change tactics to switch to offense. When an important play occurs, they can also provide immediate explanations of its tactical significance. For example, if a counter-attack is successful, they will explain in detail how the play was prepared and how each player moved. To provide this information in real time, the commentary team constantly monitors the progress of the match and updates the commentary as needed. This allows viewers to gain a deeper understanding of the tactical aspects of the match and enjoy it more. The commentary team can utilize AI technology to automatically generate appropriate commentary based on the progress of the match. The AI ​​learns from past match data and player performance data to provide optimal commentary according to the match situation. As a result, the commentary team can provide real-time commentary in accordance with the progress of the match, offering high added value to viewers.

[0031] The evaluation department assesses each player's movements, roles, and performance based on data. Specifically, it collects player location and movement data in real time to evaluate each player's role and performance. For example, it provides detailed explanations of how a particular player is moving and how their role is changing. The evaluation department can collect player performance data in real time and update evaluations. For example, it collects data such as distance covered, number of sprints, pass success rate, and number of shots, and evaluates player performance based on this data. It is also possible to change player evaluations in real time as the match progresses. For example, if a player successfully executes an important play, the impact of that play can be reflected in the evaluation. The evaluation department utilizes AI technology to analyze player performance data and automatically update each player's evaluation. The AI ​​learns from past data and statistics to build a model for accurately evaluating player performance. This allows the evaluation department to evaluate players in real time as the match progresses, providing high added value to viewers. Furthermore, the evaluation department can also conduct long-term evaluations and trend analyses based on player performance data. For example, by analyzing how a particular athlete's performance is changing and what trends are emerging, it's possible to predict future performance and suggest areas for improvement. This allows the evaluation department to comprehensively assess athletes' performance and provide high added value to viewers.

[0032] The question-answering unit allows users to ask questions on the spot, and the AI ​​provides instant answers. Specifically, in response to a user's question such as "What is this player's role?", the AI ​​will provide a detailed explanation of that player's role and performance. For example, if a user inputs a question they have during a match, the AI ​​can provide an immediate answer. The AI ​​generates appropriate answers based on the progress of the match and player performance data. For example, if a user asks, "Why is this player in this position?", the AI ​​will explain the player's role and tactical intentions. It can also answer questions about past match data based on that data. For example, if a user asks, "What tactics has this team used in the past?", the AI ​​will analyze past match data and explain the team's tactics. The question-answering unit utilizes AI technology to provide quick and accurate answers to user questions. The AI ​​uses natural language processing technology to understand user questions and generate appropriate answers. As a result, the question-answering unit can provide instant answers to user questions and deliver high added value to viewers. Furthermore, the question-answering unit can collect user feedback and continuously improve the accuracy and effectiveness of its answers. For example, the system can revise and improve its responses based on user feedback. Furthermore, the question-answering unit can reliably transmit information using multiple communication methods. For instance, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the question-answering unit to provide information quickly and reliably to users and deliver high added value to viewers.

[0033] The comparison unit compares past match data with current matches to analyze tactical changes and trends. For example, the comparison unit compares tactics used in past matches with those used in current matches and explains the differences and evolution. For example, the comparison unit can analyze the tactics of current matches based on past match data. For example, the comparison unit can compare formation changes in past matches with formation changes in current matches and explain their effects. For example, the comparison unit can refer to past match data in real time and highlight and explain tactical changes in current matches. This allows for the analysis of tactical changes and trends by comparing past match data with current matches. Tactical changes include, but are not limited to, changes in formation or changes in player positioning. Trend analysis includes, but are not limited to, methods for analyzing past match data or methods for extracting trends. Some or all of the above-described processes in the comparison unit may be performed using, for example, generative AI, or without using generative AI. For example, the comparison unit can input past and current match data into the generating AI, allowing the AI ​​to perform analysis of tactical changes and trends.

[0034] The multilingual support unit provides commentary in multiple languages, including Japanese, even for matches in overseas leagues. The multilingual support unit can, for example, translate commentary into multiple languages ​​in real time as the match progresses. The multilingual support unit can also, for example, automatically switch commentary based on the user's language settings. The multilingual support unit can also, for example, provide commentary in multiple languages ​​at important moments in the match. This allows many users to enjoy the match, overcoming language barriers, by providing commentary in multiple languages. The multiple languages ​​include, for example, Japanese, English, and Spanish, but are not limited to these examples. Some or all of the above processing in the multilingual support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the multilingual support unit can input commentary into a generative AI and have the generative AI perform translation into multiple languages.

[0035] The commentary unit can explain the intent and effect of any formation changes made by the team during a match. For example, the commentary unit can explain the intent and effect of the formation change in real time. The commentary unit can also explain the background and purpose of the formation change and evaluate its effect. The commentary unit can also explain the impact of the formation change on the flow of the match. This allows users to gain a deeper understanding of the match's tactics by explaining the intent and effect of the formation change. Intent and effect include, but are not limited to, tactical intent and the effect of the formation change. Some or all of the above processing in the commentary unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the commentary unit can input formation change data into a generative AI and have the generative AI perform the explanation of intent and effect.

[0036] The evaluation unit can provide real-time commentary on how a particular player is moving and how their role is changing. For example, the evaluation unit can provide real-time commentary on changes in a player's movements and role. The evaluation unit can also, for example, explain changes in a player's position or role and evaluate their impact. The evaluation unit can also, for example, explain the impact a player's movements have on the game's tactics. This allows users to gain a deeper understanding of a player's performance by providing real-time commentary on changes in their movements and roles. Movements and roles include, but are not limited to, changes in a player's position or role. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or not. For example, the evaluation unit can input data on a player's movements and role into a generative AI and have the generative AI perform the commentary.

[0037] The question-answering unit can explain a player's role and performance in response to a user's question, such as "What is this player's role?". For example, if a user asks a question about a specific player, the question-answering unit will explain that player's role and performance. For example, if a user inputs something they were wondering about during a match, the AI ​​can provide an immediate answer. For example, if a user asks a question about past match data, the question-answering unit can provide an answer based on that data. This allows users to resolve their questions while watching a match by providing immediate answers to their questions. Roles and performances include, but are not limited to, methods for evaluating a player's role and performance. Some or all of the above-described processes in the question-answering unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the question-answering unit can input a user's question into a generative AI and have the generative AI produce the answer.

[0038] The commentary unit can update its commentary in real time according to the progress of the match and highlight important tactical changes. For example, if a team changes its formation during a match, the commentary unit can explain the intention and effect of that change in real time. For example, the commentary unit can also highlight and explain tactical changes when the flow of the match changes. For example, if an important play occurs, the commentary unit can immediately explain the tactical significance of that play. In this way, by updating the commentary content according to the progress of the match, important tactical changes can be highlighted. The progress of the match includes, but is not limited to, at what stage of the match the commentary should be updated, and what events should be used to update the commentary. Important tactical changes include, but are not limited to, changes in formation or changes in player positioning. Some or all of the above processing in the commentary unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the commentary unit can input match progress data into a generative AI and have the generative AI update the commentary content.

[0039] The commentary section can explain the effects of a team formation change by comparing it to similar changes in the past. For example, the commentary section can explain the effects by comparing them to the results of the same formation change in past matches. For example, the commentary section can explain the effects by citing examples of successful and unsuccessful formation changes. For example, the commentary section can predict and explain the impact of the current formation change based on past data. This allows for a deeper understanding of the effects of the formation change by explaining it in comparison to similar changes in the past. Examples of similar changes in the past include, but are not limited to, methods for analyzing past match data and methods for evaluating the effects of changes. Some or all of the above processing in the commentary section may be performed using, for example, a generative AI, or not using a generative AI. For example, the commentary section can input past match data into a generative AI and have the generative AI perform an explanation of the effects of the formation change.

[0040] The commentary section can customize its commentary content according to the user's level of soccer knowledge as the match progresses. For example, it can explain basic tactics and rules to soccer beginners. For example, it can explain more advanced tactics and the intentions behind plays to experienced soccer players. For example, it can explain detailed tactical analyses and the intentions behind players' movements to soccer coaches. By customizing the commentary content according to the user's level of soccer knowledge, it is possible to provide more appropriate commentary. Soccer knowledge levels include, but are not limited to, methods of categorizing players into levels such as beginner, intermediate, and advanced. Some or all of the above processing in the commentary section may be performed using, for example, a generative AI, or not using a generative AI. For example, the commentary section can input data on the user's soccer knowledge level into a generative AI and have the generative AI customize the commentary content.

[0041] The commentary section can display individual player performance data in real time during commentary to supplement the commentary. For example, the commentary section can display a player's distance covered and number of sprints in real time and use this information in the commentary. The commentary section can also display a player's pass success rate and number of shots to explain that player's performance. The commentary section can also display a player's heart rate and fatigue level to explain that player's condition. In this way, the commentary can be supplemented by displaying individual player performance data in real time. Performance data includes, but is not limited to, a player's distance covered and number of shots. Some or all of the above processing in the commentary section may be performed using, for example, a generative AI, or not using a generative AI. For example, the commentary section can input player performance data into a generative AI and have the generative AI perform the real-time display.

[0042] The evaluation unit can assess each player's performance in real time and update the evaluation as the match progresses. For example, the evaluation unit can collect player performance data in real time and update the evaluation. The evaluation unit can also change player evaluations in real time as the match progresses. For example, if an important play occurs, the evaluation unit can reflect the impact of that play in the evaluation. This allows for more accurate evaluations by assessing each player's performance in real time and updating the evaluation as the match progresses. Real-time evaluation includes, but is not limited to, the method of acquiring data during the match and the frequency of evaluation updates. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation unit can input player performance data into a generative AI and have the generative AI perform real-time evaluations.

[0043] The evaluation unit can assess the impact of a player role change on the game. For example, the evaluation unit can assess the impact of a player role change on the flow of the game. The evaluation unit can also compare and evaluate a player's performance before and after a role change. The evaluation unit can also assess the impact of a role change on the entire team. This allows for a deeper understanding of the flow of the game by evaluating the impact of a player role change on the game. Role changes include, but are not limited to, changes in a player's position or role. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input player role change data into a generative AI and have the generative AI perform an evaluation of the impact on the game.

[0044] The evaluation unit can evaluate a player's current performance by comparing it with past performance data. For example, the evaluation unit can evaluate current performance based on past match data. The evaluation unit can also evaluate current performance by comparing it with past performance. For example, the evaluation unit can evaluate areas for improvement in current performance based on past data. This allows for understanding a player's growth and changes by evaluating current performance in comparison with past performance data. Past performance data includes, but is not limited to, past match data and player performance data. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input past performance data into a generative AI and have the generative AI perform the evaluation of current performance.

[0045] The evaluation unit can display evaluation results graphically, making them easy for users to understand visually. For example, the evaluation unit can display player evaluation results in graphs or charts. The evaluation unit can also visualize and display the evaluation results for the entire team. For example, the evaluation unit can visually display player performance data to complement the evaluation results. This makes it easier for users to understand visually by displaying evaluation results graphically. Graphical display includes, but is not limited to, the types of graphs and display interfaces. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation unit can input evaluation results into a generative AI and have the generative AI perform the processing of displaying them graphically.

[0046] The question-answering unit can analyze the user's question history and provide the best answer based on past questions. For example, the question-answering unit can provide relevant answers based on the content of questions the user has asked in the past. The question-answering unit can also analyze the user's question history and suggest the best answer. For example, the question-answering unit can compare past questions with current questions and provide the best answer. In this way, by analyzing the user's question history, the best answer based on past questions can be provided. The best answer includes, but is not limited to, a method for selecting an answer based on past question history. Some or all of the above processing in the question-answering unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the question-answering unit can input the user's question history data into a generative AI and have the generative AI perform the task of providing the best answer.

[0047] The question-answering unit can improve the accuracy of its answers by referring to relevant past match data when answering questions. For example, the question-answering unit can provide answers to questions by referring to past match data. The question-answering unit can also improve the accuracy of its answers based on relevant past match data. The question-answering unit can also provide the best possible answer to the current question by referring to past data. This allows for improved accuracy of answers by referring to relevant past match data. Relevant past match data includes, but is not limited to, statistical data from past matches and player performance data. Some or all of the above processing in the question-answering unit may be performed using, for example, a generative AI, or without a generative AI. For example, the question-answering unit can input past match data into a generative AI and have the generative AI perform the task of improving the accuracy of its answers.

[0048] The question-answering unit can adjust the level of detail in its answers according to the user's level of soccer knowledge. For example, the question-answering unit can provide basic information to soccer beginners, provide detailed information to experienced soccer players, and provide specialized information to soccer coaches. By adjusting the level of detail in the answers according to the user's level of soccer knowledge, more appropriate answers can be provided. The level of soccer knowledge includes, but is not limited to, classifications such as beginner, intermediate, and advanced. Some or all of the above processing in the question-answering unit may be performed using, for example, a generative AI, or without a generative AI. For example, the question-answering unit can input data on the user's soccer knowledge level into a generative AI and have the generative AI adjust the level of detail in the answers.

[0049] The question-answering unit can display relevant player performance data when answering a question to complete the answer. For example, the question-answering unit can display player performance data when answering a question. The question-answering unit can also display relevant data to complete the answer to the question, for example. The question-answering unit can also display relevant data to improve the accuracy of the answer to the question, for example. By displaying relevant player performance data, the answer can be completed and the accuracy of the answer can be improved. Performance data includes, but is not limited to, player distance covered and number of shots taken. Some or all of the above processing in the question-answering unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the question-answering unit can input player performance data into a generative AI and have the generative AI complete the answer.

[0050] The comparison unit can compare past match data with current match data in real time and highlight changes in tactics. For example, the comparison unit compares past match data with current match data in real time. The comparison unit can also display the comparison results highlighting changes in tactics. For example, the comparison unit can explain changes in current tactics based on past data. This allows for the highlighting and explanation of changes in tactics by comparing past match data with current match data in real time. Changes in tactics include, but are not limited to, changes in formation or changes in player positioning. Some or all of the above processing in the comparison unit may be performed using, for example, a generating AI, or without a generating AI. For example, the comparison unit can input past match data and current match data into a generating AI and have the generating AI perform the highlighting of changes in tactics.

[0051] The comparison unit can analyze in detail changes in the performance of specific players or teams during the comparison process. For example, the comparison unit can analyze in detail changes in the performance of a specific player. The comparison unit can also analyze in detail changes in the performance of the entire team. For example, the comparison unit can analyze changes in current performance based on past data. This allows for a deeper understanding of the flow of the game by analyzing in detail changes in the performance of specific players or teams. Changes in performance include, but are not limited to, changes in player statistics or changes in team tactics. Some or all of the above processing in the comparison unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the comparison unit can input performance data of specific players or teams into a generative AI and have the generative AI perform the change analysis.

[0052] The comparison unit can perform comparisons focusing on specific matches or players of interest to the user. For example, the comparison unit can perform comparisons based on specific matches that the user is interested in. The comparison unit can also compare the performance of specific players that the user is interested in. For example, the comparison unit can perform comparisons focusing on specific matches or players based on the user's interests. This allows for more interesting comparison results by focusing on specific matches or players of interest to the user. Specific matches or players of interest include, but are not limited to, methods for identifying the user's interests or methods for selecting specific matches or players. Some or all of the above processing in the comparison unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the comparison unit can input user interest data into a generative AI and have the generative AI perform comparisons focused on specific matches or players.

[0053] The comparison unit can display the comparison results graphically, making them easy for the user to understand visually. The comparison unit can display the comparison results in graphs or charts, for example. The comparison unit can also visualize and display past and present data, for example. The comparison unit can also display the comparison results visually, making them easy for the user to understand. This makes it easier for the user to understand visually by displaying the comparison results graphically. Graphical display includes, but is not limited to, the types of graphs and display interfaces. Some or all of the above-described processing in the comparison unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the comparison unit can input the comparison results into a generative AI and have the generative AI perform the processing of displaying them graphically.

[0054] The multilingual support unit can switch commentary in multiple languages ​​in real time in accordance with the progress of the match. For example, the multilingual support unit can switch the commentary language in real time according to the progress of the match. The multilingual support unit can also, for example, automatically switch the commentary language based on the user's language settings. The multilingual support unit can also, for example, provide commentary in multiple languages ​​at important moments in the match. This makes it easier for the user to understand by switching commentary in multiple languages ​​in real time in accordance with the progress of the match. Switching in real time includes, but is not limited to, the timing and method of switching. Some or all of the above processing in the multilingual support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the multilingual support unit can input match progress data into a generative AI and have the generative AI perform the switching of commentary in multiple languages.

[0055] The multilingual support unit can apply the optimal translation algorithm based on the user's language settings. For example, the multilingual support unit can apply the optimal translation algorithm based on the user's language settings. The multilingual support unit can also select the optimal translation algorithm when the user uses multiple languages. The multilingual support unit can also improve translation accuracy according to the user's language settings. This improves translation accuracy by applying the optimal translation algorithm based on the user's language settings. The optimal translation algorithm includes, but is not limited to, the translation techniques used and the method for selecting the algorithm. Some or all of the above processing in the multilingual support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the multilingual support unit can input the user's language setting data into a generative AI and have the generative AI apply the optimal translation algorithm.

[0056] The multilingual support unit can provide customized explanations tailored to the user's region and culture when providing multilingual support. For example, the multilingual support unit can provide customized explanations tailored to the user's region. The multilingual support unit can also provide customized explanations tailored to the user's culture. The multilingual support unit can also provide optimal explanations based on the user's region and culture. By providing customized explanations tailored to the user's region and culture, more appropriate explanations can be provided. Customization tailored to region and culture includes, but is not limited to, the use of region-specific terminology and explanations based on cultural background. Some or all of the above processing in the multilingual support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the multilingual support unit can input the user's region and culture data into a generative AI and have the generative AI execute customized explanations.

[0057] The multilingual support unit can display commentary content in multiple languages ​​simultaneously, allowing users to switch languages ​​while watching. The multilingual support unit can, for example, display commentary content in multiple languages ​​simultaneously. The multilingual support unit can, for example, allow users to switch languages ​​while watching. The multilingual support unit can, for example, provide commentary in multiple languages ​​and allow users to freely select a language. This allows users to switch languages ​​while watching by displaying commentary content in multiple languages ​​simultaneously. Simultaneous display in multiple languages ​​includes, but is not limited to, the display interface and language switching method. Some or all of the above processing in the multilingual support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the multilingual support unit can input commentary content into a generative AI and have the generative AI perform simultaneous display in multiple languages.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The evaluation unit can consider a player's past injury history when assessing their performance. For example, if a player has suffered a major injury in the past, the evaluation can assess how that injury is affecting their current performance. It can also reflect the recovery status from the injury in the evaluation. Furthermore, for players with a high risk of injury, that risk can be included in the evaluation. By providing evaluations that take into account a player's injury history, a more accurate performance assessment becomes possible.

[0060] The comparison unit can take environmental factors into account when comparing past and current match data. For example, it can reflect the impact of weather and stadium conditions on the match. It can also consider the impact of spectator numbers and cheering. Furthermore, it can evaluate the impact of differences in time of day and season on performance. By providing comparisons that take environmental factors into account, more accurate tactical analysis becomes possible.

[0061] The evaluation unit can consider a player's training data when assessing their performance. For example, it can reflect the impact of a player's training intensity and frequency on their match performance in its evaluation. It can also evaluate how the content and methods of training affect performance. Furthermore, it can include training outcomes and progress in its evaluation. By providing evaluations that take into account a player's training data, it becomes possible to perform more accurate performance assessments.

[0062] The comparison section can take into account the players' condition when comparing past and current match data. For example, it can reflect the impact of a player's fatigue level and physical condition on their performance in a match. It can also consider whether a player has been injured and their recovery status. Furthermore, it can evaluate the impact of a player's mental condition on their performance. By providing comparisons that take into account the players' condition, more accurate tactical analysis becomes possible.

[0063] The evaluation unit can consider athlete nutrition data when assessing athlete performance. For example, it can reflect the impact of an athlete's diet and nutritional intake on performance in its evaluation. It can also evaluate how nutritional balance and supplement use affect performance. Furthermore, changes in an athlete's weight and body fat percentage can be included in the evaluation. By providing evaluations that take into account athletes' nutritional data, a more accurate performance assessment becomes possible.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The commentary team will immediately explain any changes in the team's formation or tactics as the match progresses. For example, if the team changes formation during the match, they will explain the intention and effect of that change. They can also highlight and explain tactical changes when the flow of the match shifts. If an important play occurs, they can immediately explain the tactical significance of that play. Step 2: The evaluation unit assesses each player's movements, roles, and performance based on data. For example, it provides real-time commentary on how a particular player is moving and how their role is changing. It can collect player performance data in real time and update evaluations. It can also change player evaluations in real time as the game progresses. If an important play occurs, its impact can be reflected in the evaluation. Step 3: The question-answering section allows users to ask questions on the spot, and the AI ​​provides instant answers. For example, if a user asks, "What is this player's role?", the AI ​​will explain the player's role and performance. If a user inputs a question they have during a match, the AI ​​can provide an immediate answer. If a user asks a question about past match data, the AI ​​can also provide an answer based on that data.

[0066] (Example of form 2) The TactiFoot system according to an embodiment of the present invention is an AI agent that provides real-time tactical commentary and live analysis of overseas soccer matches. This TactiFoot system provides advanced analysis and easy-to-understand commentary so that users can deeply understand tactical points and player movements while watching a match. This makes watching soccer a more intellectually stimulating and enjoyable experience, and can be enjoyed by a wide range of people, from fans interested in tactics to beginners. For example, while the user is watching a match, the TactiFoot system immediately explains the team's formation and tactical changes in real time as the match progresses. For example, if the team changes its formation during the match, the TactiFoot system will explain the intention and effect of that change. Next, the TactiFoot system evaluates the movements, roles, and performance of each player based on data. For example, it will explain in real time what kind of movements a particular player is making and how their role is changing. Furthermore, the TactiFoot system has an interactive question-answering function that allows users to ask questions on the spot. For example, when a user enters a question, the TactiFoot system will answer it immediately. For example, in response to a question such as "What is this player's role?", the TactiFoot system will explain the player's role and performance. Furthermore, the TactiFoot system compares past match data with current matches to analyze tactical changes and trends. For example, it compares tactics used in past matches with those used in current matches, explaining the differences and evolution. In addition, the TactiFoot system supports multiple languages, providing commentary in multiple languages, including Japanese, even for matches in overseas leagues. This allows many users to enjoy the matches despite language barriers. Thus, the TactiFoot system, with its features such as real-time tactical commentary, individual player analysis, interactive question and answer, comparison with past data, and multi-language support, makes watching soccer a more intellectually stimulating and enjoyable experience. In this way, the TactiFoot system enables users to gain a deeper understanding of tactical points and player movements while watching matches, making soccer a more intellectually stimulating and enjoyable experience.

[0067] The TactiFoot system according to this embodiment comprises an explanation unit, an evaluation unit, and a question answering unit. The explanation unit immediately explains changes in the team's formation and tactics in accordance with the progress of the match. For example, if the team changes its formation during the match, the explanation unit explains the intention and effect of that change. For example, the explanation unit can explain changes in formation and tactics in real time according to the progress of the match. For example, the explanation unit can also emphasize and explain changes in tactics when the flow of the match changes. For example, if an important play occurs, the explanation unit can immediately explain the tactical significance of that play. The evaluation unit evaluates the movements, roles, and performance of each player based on data. For example, the evaluation unit explains in real time what kind of movements a particular player is making and how their role is changing. For example, the evaluation unit can collect player performance data in real time and update the evaluation. For example, the evaluation unit can also change the evaluation of players in real time according to the progress of the match. For example, if an important play occurs, the evaluation unit can also reflect the impact of that play in the evaluation. The question-answering unit allows users to ask questions on the spot, and the AI ​​provides immediate answers. For example, in response to a user's question, "What is this player's role?", the question-answering unit will explain the player's role and performance. For example, if a user inputs a question during a match, the AI ​​can provide an immediate answer. For example, if a user asks a question about past match data, the question-answering unit can provide an answer based on that data. As a result, the TactiFoot system according to this embodiment can provide commentary, evaluation, and question-answering in real time as the match progresses.

[0068] The commentary team will provide immediate explanations of team formation and tactical changes as the match progresses. Specifically, if a team changes formation during a match, they will provide a detailed explanation of the intention and effect of that change. For example, if the formation changes from 4-4-2 to 3-5-2, the commentary team will explain whether the change is aimed at strengthening defense or diversifying attack. They will also provide specific explanations of changes in player positions and roles, detailing which players move to which positions and what roles they take on. Furthermore, when the flow of the match changes, they can highlight and explain tactical changes. For example, if the opposing team goes on the offensive, they will explain how their own team will strengthen their defense, or conversely, how they will change tactics to switch to offense. When an important play occurs, they can also provide immediate explanations of its tactical significance. For example, if a counter-attack is successful, they will explain in detail how the play was prepared and how each player moved. To provide this information in real time, the commentary team constantly monitors the progress of the match and updates the commentary as needed. This allows viewers to gain a deeper understanding of the tactical aspects of the match and enjoy it more. The commentary team can utilize AI technology to automatically generate appropriate commentary based on the progress of the match. The AI ​​learns from past match data and player performance data to provide optimal commentary according to the match situation. As a result, the commentary team can provide real-time commentary in accordance with the progress of the match, offering high added value to viewers.

[0069] The evaluation department assesses each player's movements, roles, and performance based on data. Specifically, it collects player location and movement data in real time to evaluate each player's role and performance. For example, it provides detailed explanations of how a particular player is moving and how their role is changing. The evaluation department can collect player performance data in real time and update evaluations. For example, it collects data such as distance covered, number of sprints, pass success rate, and number of shots, and evaluates player performance based on this data. It is also possible to change player evaluations in real time as the match progresses. For example, if a player successfully executes an important play, the impact of that play can be reflected in the evaluation. The evaluation department utilizes AI technology to analyze player performance data and automatically update each player's evaluation. The AI ​​learns from past data and statistics to build a model for accurately evaluating player performance. This allows the evaluation department to evaluate players in real time as the match progresses, providing high added value to viewers. Furthermore, the evaluation department can also conduct long-term evaluations and trend analyses based on player performance data. For example, by analyzing how a particular athlete's performance is changing and what trends are emerging, it's possible to predict future performance and suggest areas for improvement. This allows the evaluation department to comprehensively assess athletes' performance and provide high added value to viewers.

[0070] The question-answering unit allows users to ask questions on the spot, and the AI ​​provides instant answers. Specifically, in response to a user's question such as "What is this player's role?", the AI ​​will provide a detailed explanation of that player's role and performance. For example, if a user inputs a question they have during a match, the AI ​​can provide an immediate answer. The AI ​​generates appropriate answers based on the progress of the match and player performance data. For example, if a user asks, "Why is this player in this position?", the AI ​​will explain the player's role and tactical intentions. It can also answer questions about past match data based on that data. For example, if a user asks, "What tactics has this team used in the past?", the AI ​​will analyze past match data and explain the team's tactics. The question-answering unit utilizes AI technology to provide quick and accurate answers to user questions. The AI ​​uses natural language processing technology to understand user questions and generate appropriate answers. As a result, the question-answering unit can provide instant answers to user questions and deliver high added value to viewers. Furthermore, the question-answering unit can collect user feedback and continuously improve the accuracy and effectiveness of its answers. For example, the system can revise and improve its responses based on user feedback. Furthermore, the question-answering unit can reliably transmit information using multiple communication methods. For instance, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the question-answering unit to provide information quickly and reliably to users and deliver high added value to viewers.

[0071] The comparison unit compares past match data with current matches to analyze tactical changes and trends. For example, the comparison unit compares tactics used in past matches with those used in current matches and explains the differences and evolution. For example, the comparison unit can analyze the tactics of current matches based on past match data. For example, the comparison unit can compare formation changes in past matches with formation changes in current matches and explain their effects. For example, the comparison unit can refer to past match data in real time and highlight and explain tactical changes in current matches. This allows for the analysis of tactical changes and trends by comparing past match data with current matches. Tactical changes include, but are not limited to, changes in formation or changes in player positioning. Trend analysis includes, but are not limited to, methods for analyzing past match data or methods for extracting trends. Some or all of the above-described processes in the comparison unit may be performed using, for example, generative AI, or without using generative AI. For example, the comparison unit can input past and current match data into the generating AI, allowing the AI ​​to perform analysis of tactical changes and trends.

[0072] The multilingual support unit provides commentary in multiple languages, including Japanese, even for matches in overseas leagues. The multilingual support unit can, for example, translate commentary into multiple languages ​​in real time as the match progresses. The multilingual support unit can also, for example, automatically switch commentary based on the user's language settings. The multilingual support unit can also, for example, provide commentary in multiple languages ​​at important moments in the match. This allows many users to enjoy the match, overcoming language barriers, by providing commentary in multiple languages. The multiple languages ​​include, for example, Japanese, English, and Spanish, but are not limited to these examples. Some or all of the above processing in the multilingual support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the multilingual support unit can input commentary into a generative AI and have the generative AI perform translation into multiple languages.

[0073] The commentary unit can explain the intent and effect of any formation changes made by the team during a match. For example, the commentary unit can explain the intent and effect of the formation change in real time. The commentary unit can also explain the background and purpose of the formation change and evaluate its effect. The commentary unit can also explain the impact of the formation change on the flow of the match. This allows users to gain a deeper understanding of the match's tactics by explaining the intent and effect of the formation change. Intent and effect include, but are not limited to, tactical intent and the effect of the formation change. Some or all of the above processing in the commentary unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the commentary unit can input formation change data into a generative AI and have the generative AI perform the explanation of intent and effect.

[0074] The evaluation unit can provide real-time commentary on how a particular player is moving and how their role is changing. For example, the evaluation unit can provide real-time commentary on changes in a player's movements and role. The evaluation unit can also, for example, explain changes in a player's position or role and evaluate their impact. The evaluation unit can also, for example, explain the impact a player's movements have on the game's tactics. This allows users to gain a deeper understanding of a player's performance by providing real-time commentary on changes in their movements and roles. Movements and roles include, but are not limited to, changes in a player's position or role. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or not. For example, the evaluation unit can input data on a player's movements and role into a generative AI and have the generative AI perform the commentary.

[0075] The question-answering unit can explain a player's role and performance in response to a user's question, such as "What is this player's role?". For example, if a user asks a question about a specific player, the question-answering unit will explain that player's role and performance. For example, if a user inputs something they were wondering about during a match, the AI ​​can provide an immediate answer. For example, if a user asks a question about past match data, the question-answering unit can provide an answer based on that data. This allows users to resolve their questions while watching a match by providing immediate answers to their questions. Roles and performances include, but are not limited to, methods for evaluating a player's role and performance. Some or all of the above-described processes in the question-answering unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the question-answering unit can input a user's question into a generative AI and have the generative AI produce the answer.

[0076] The commentary unit can estimate the user's emotions and adjust the tone and level of detail of the commentary based on the estimated emotions. For example, if the user is excited, the commentary unit can calm the tone of the commentary and provide a detailed tactical explanation. For example, if the user is bored, the commentary unit can brighten the tone of the commentary and make it concise and engaging. For example, if the user is nervous, the commentary unit can calm the tone of the commentary and provide a reassuring explanation. In this way, by adjusting the tone and level of detail of the commentary according to the user's emotions, a more appropriate commentary can be provided. Emotion estimation includes, but is not limited to, techniques such as facial recognition and voice analysis. The tone and level of detail of the commentary include, but is not limited to, the tone of the commentary and the way detailed information is provided. Some or all of the above processing in the commentary unit may be performed using, for example, generative AI, or not using generative AI. For example, the commentary unit can input user emotion data into generative AI and have the generative AI perform the adjustment of the tone and level of detail of the commentary.

[0077] The commentary unit can update its commentary in real time according to the progress of the match and highlight important tactical changes. For example, if a team changes its formation during a match, the commentary unit can explain the intention and effect of that change in real time. For example, the commentary unit can also highlight and explain tactical changes when the flow of the match changes. For example, if an important play occurs, the commentary unit can immediately explain the tactical significance of that play. In this way, by updating the commentary content according to the progress of the match, important tactical changes can be highlighted. The progress of the match includes, but is not limited to, at what stage of the match the commentary should be updated, and what events should be used to update the commentary. Important tactical changes include, but are not limited to, changes in formation or changes in player positioning. Some or all of the above processing in the commentary unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the commentary unit can input match progress data into a generative AI and have the generative AI update the commentary content.

[0078] The commentary section can explain the effects of a team formation change by comparing it to similar changes in the past. For example, the commentary section can explain the effects by comparing them to the results of the same formation change in past matches. For example, the commentary section can explain the effects by citing examples of successful and unsuccessful formation changes. For example, the commentary section can predict and explain the impact of the current formation change based on past data. This allows for a deeper understanding of the effects of the formation change by explaining it in comparison to similar changes in the past. Examples of similar changes in the past include, but are not limited to, methods for analyzing past match data and methods for evaluating the effects of changes. Some or all of the above processing in the commentary section may be performed using, for example, a generative AI, or not using a generative AI. For example, the commentary section can input past match data into a generative AI and have the generative AI perform an explanation of the effects of the formation change.

[0079] The commentary unit can estimate the user's emotions and adjust the order of the commentary based on those emotions. For example, if the user is excited, the commentary unit will explain important tactical points first. If the user is bored, the commentary unit may include interesting anecdotes in its explanation. If the user is nervous, the commentary unit may explain in a reassuring order. By adjusting the order of the commentary according to the user's emotions, a more appropriate order of explanation can be provided. The order of the commentary includes, but is not limited to, methods such as prioritizing the explanation of important information or adjusting the order according to the user's interests. Some or all of the above processing in the commentary unit may be performed using, for example, generative AI, or without generative AI. For example, the commentary unit can input user emotion data into generative AI and have the generative AI adjust the order of the commentary.

[0080] The commentary section can customize its commentary content according to the user's level of soccer knowledge as the match progresses. For example, it can explain basic tactics and rules to soccer beginners. For example, it can explain more advanced tactics and the intentions behind plays to experienced soccer players. For example, it can explain detailed tactical analyses and the intentions behind players' movements to soccer coaches. By customizing the commentary content according to the user's level of soccer knowledge, it is possible to provide more appropriate commentary. Soccer knowledge levels include, but are not limited to, methods of categorizing players into levels such as beginner, intermediate, and advanced. Some or all of the above processing in the commentary section may be performed using, for example, a generative AI, or not using a generative AI. For example, the commentary section can input data on the user's soccer knowledge level into a generative AI and have the generative AI customize the commentary content.

[0081] The commentary section can display individual player performance data in real time during commentary to supplement the commentary. For example, the commentary section can display a player's distance covered and number of sprints in real time and use this information in the commentary. The commentary section can also display a player's pass success rate and number of shots to explain that player's performance. The commentary section can also display a player's heart rate and fatigue level to explain that player's condition. In this way, the commentary can be supplemented by displaying individual player performance data in real time. Performance data includes, but is not limited to, a player's distance covered and number of shots. Some or all of the above processing in the commentary section may be performed using, for example, a generative AI, or not using a generative AI. For example, the commentary section can input player performance data into a generative AI and have the generative AI perform the real-time display.

[0082] The evaluation unit can estimate the user's emotions and adjust the level of detail in the evaluation based on the estimated emotions. For example, if the user is excited, the evaluation unit can provide a detailed evaluation. For example, if the user is bored, the evaluation unit can provide a concise evaluation. For example, if the user is nervous, the evaluation unit can provide a reassuring evaluation. In this way, by adjusting the level of detail in the evaluation according to the user's emotions, a more appropriate evaluation can be provided. The level of detail in the evaluation includes, but is not limited to, detailed evaluation items or the depth of the evaluation. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the level of detail in the evaluation.

[0083] The evaluation unit can assess each player's performance in real time and update the evaluation as the match progresses. For example, the evaluation unit can collect player performance data in real time and update the evaluation. The evaluation unit can also change player evaluations in real time as the match progresses. For example, if an important play occurs, the evaluation unit can reflect the impact of that play in the evaluation. This allows for more accurate evaluations by assessing each player's performance in real time and updating the evaluation as the match progresses. Real-time evaluation includes, but is not limited to, the method of acquiring data during the match and the frequency of evaluation updates. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation unit can input player performance data into a generative AI and have the generative AI perform real-time evaluations.

[0084] The evaluation unit can assess the impact of a player role change on the game. For example, the evaluation unit can assess the impact of a player role change on the flow of the game. The evaluation unit can also compare and evaluate a player's performance before and after a role change. The evaluation unit can also assess the impact of a role change on the entire team. This allows for a deeper understanding of the flow of the game by evaluating the impact of a player role change on the game. Role changes include, but are not limited to, changes in a player's position or role. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input player role change data into a generative AI and have the generative AI perform an evaluation of the impact on the game.

[0085] The evaluation unit can estimate the user's emotions and adjust how the evaluation results are displayed based on the estimated emotions. For example, if the user is excited, the evaluation unit can display detailed evaluation results. For example, if the user is bored, the evaluation unit can also display concise evaluation results. For example, if the user is nervous, the evaluation unit can also display reassuring evaluation results. By adjusting how the evaluation results are displayed according to the user's emotions, more appropriate evaluation results can be provided. The methods for displaying evaluation results include, but are not limited to, graph displays and text displays. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust how the evaluation results are displayed.

[0086] The evaluation unit can evaluate a player's current performance by comparing it with past performance data. For example, the evaluation unit can evaluate current performance based on past match data. The evaluation unit can also evaluate current performance by comparing it with past performance. For example, the evaluation unit can evaluate areas for improvement in current performance based on past data. This allows for understanding a player's growth and changes by evaluating current performance in comparison with past performance data. Past performance data includes, but is not limited to, past match data and player performance data. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input past performance data into a generative AI and have the generative AI perform the evaluation of current performance.

[0087] The evaluation unit can display evaluation results graphically, making them easy for users to understand visually. For example, the evaluation unit can display player evaluation results in graphs or charts. The evaluation unit can also visualize and display the evaluation results for the entire team. For example, the evaluation unit can visually display player performance data to complement the evaluation results. This makes it easier for users to understand visually by displaying evaluation results graphically. Graphical display includes, but is not limited to, the types of graphs and display interfaces. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation unit can input evaluation results into a generative AI and have the generative AI perform the processing of displaying them graphically.

[0088] The question-answering unit can estimate the user's emotions and adjust the tone and level of detail of its response based on the estimated emotions. For example, if the user is excited, the question-answering unit can provide a detailed response in a calm tone. If the user is bored, the question-answering unit can also provide a concise response in a cheerful tone. If the user is nervous, the question-answering unit can also provide a reassuring response in a calm tone. By adjusting the tone and level of detail of the response according to the user's emotions, a more appropriate response can be provided. The tone and level of detail of the response include, but are not limited to, the tone of voice and the way detailed information is provided. Some or all of the processing described above in the question-answering unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the question-answering unit can input user emotion data into a generative AI and have the generative AI adjust the tone and level of detail of the response.

[0089] The question-answering unit can analyze the user's question history and provide the best answer based on past questions. For example, the question-answering unit can provide relevant answers based on the content of questions the user has asked in the past. The question-answering unit can also analyze the user's question history and suggest the best answer. For example, the question-answering unit can compare past questions with current questions and provide the best answer. In this way, by analyzing the user's question history, the best answer based on past questions can be provided. The best answer includes, but is not limited to, a method for selecting an answer based on past question history. Some or all of the above processing in the question-answering unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the question-answering unit can input the user's question history data into a generative AI and have the generative AI perform the task of providing the best answer.

[0090] The question-answering unit can improve the accuracy of its answers by referring to relevant past match data when answering questions. For example, the question-answering unit can provide answers to questions by referring to past match data. The question-answering unit can also improve the accuracy of its answers based on relevant past match data. The question-answering unit can also provide the best possible answer to the current question by referring to past data. This allows for improved accuracy of answers by referring to relevant past match data. Relevant past match data includes, but is not limited to, statistical data from past matches and player performance data. Some or all of the above processing in the question-answering unit may be performed using, for example, a generative AI, or without a generative AI. For example, the question-answering unit can input past match data into a generative AI and have the generative AI perform the task of improving the accuracy of its answers.

[0091] The question-answering unit can estimate the user's emotions and adjust the order of answers based on the estimated emotions. For example, if the user is excited, the question-answering unit may answer important points first. For example, if the user is bored, the question-answering unit may answer interesting content first. For example, if the user is nervous, the question-answering unit may answer reassuring content first. In this way, by adjusting the order of answers according to the user's emotions, answers can be provided in a more appropriate order. The order of answers includes, but is not limited to, methods of prioritizing important information or adjusting the order according to the user's interests. Some or all of the above processing in the question-answering unit may be performed using, for example, generative AI, or not using generative AI. For example, the question-answering unit can input user emotion data into generative AI and have the generative AI perform the adjustment of the order of answers.

[0092] The question-answering unit can adjust the level of detail in its answers according to the user's level of soccer knowledge. For example, the question-answering unit can provide basic information to soccer beginners, provide detailed information to experienced soccer players, and provide specialized information to soccer coaches. By adjusting the level of detail in the answers according to the user's level of soccer knowledge, more appropriate answers can be provided. The level of soccer knowledge includes, but is not limited to, classifications such as beginner, intermediate, and advanced. Some or all of the above processing in the question-answering unit may be performed using, for example, a generative AI, or without a generative AI. For example, the question-answering unit can input data on the user's soccer knowledge level into a generative AI and have the generative AI adjust the level of detail in the answers.

[0093] The question-answering unit can display relevant player performance data when answering a question to complete the answer. For example, the question-answering unit can display player performance data when answering a question. The question-answering unit can also display relevant data to complete the answer to the question, for example. The question-answering unit can also display relevant data to improve the accuracy of the answer to the question, for example. By displaying relevant player performance data, the answer can be completed and the accuracy of the answer can be improved. Performance data includes, but is not limited to, player distance covered and number of shots taken. Some or all of the above processing in the question-answering unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the question-answering unit can input player performance data into a generative AI and have the generative AI complete the answer.

[0094] The comparison unit can estimate the user's emotions and adjust how the comparison results are displayed based on the estimated emotions. For example, if the user is excited, the comparison unit can display detailed comparison results. For example, if the user is bored, the comparison unit can also display concise comparison results. For example, if the user is nervous, the comparison unit can also display reassuring comparison results. By adjusting how the comparison results are displayed according to the user's emotions, more appropriate comparison results can be provided. The methods for displaying the comparison results include, but are not limited to, graph displays and text displays. Some or all of the above processing in the comparison unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the comparison unit can input user emotion data into a generative AI and have the generative AI adjust how the comparison results are displayed.

[0095] The comparison unit can compare past match data with current match data in real time and highlight changes in tactics. For example, the comparison unit compares past match data with current match data in real time. The comparison unit can also display the comparison results highlighting changes in tactics. For example, the comparison unit can explain changes in current tactics based on past data. This allows for the highlighting and explanation of changes in tactics by comparing past match data with current match data in real time. Changes in tactics include, but are not limited to, changes in formation or changes in player positioning. Some or all of the above processing in the comparison unit may be performed using, for example, a generating AI, or without a generating AI. For example, the comparison unit can input past match data and current match data into a generating AI and have the generating AI perform the highlighting of changes in tactics.

[0096] The comparison unit can analyze in detail changes in the performance of specific players or teams during the comparison process. For example, the comparison unit can analyze in detail changes in the performance of a specific player. The comparison unit can also analyze in detail changes in the performance of the entire team. For example, the comparison unit can analyze changes in current performance based on past data. This allows for a deeper understanding of the flow of the game by analyzing in detail changes in the performance of specific players or teams. Changes in performance include, but are not limited to, changes in player statistics or changes in team tactics. Some or all of the above processing in the comparison unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the comparison unit can input performance data of specific players or teams into a generative AI and have the generative AI perform the change analysis.

[0097] The comparison unit can estimate the user's emotions and adjust the order of the comparison results based on the estimated emotions. For example, if the user is excited, the comparison unit can display important comparison points first. For example, if the user is bored, the comparison unit can also display interesting content first. For example, if the user is nervous, the comparison unit can also display reassuring content first. By adjusting the order of the comparison results according to the user's emotions, the comparison results can be provided in a more appropriate order. The order of the comparison results includes, but is not limited to, methods of prioritizing the display of important information or adjusting the order according to the user's interests. Some or all of the above processing in the comparison unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the comparison unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the order of the comparison results.

[0098] The comparison unit can perform comparisons focusing on specific matches or players of interest to the user. For example, the comparison unit can perform comparisons based on specific matches that the user is interested in. The comparison unit can also compare the performance of specific players that the user is interested in. For example, the comparison unit can perform comparisons focusing on specific matches or players based on the user's interests. This allows for more interesting comparison results by focusing on specific matches or players of interest to the user. Specific matches or players of interest include, but are not limited to, methods for identifying the user's interests or methods for selecting specific matches or players. Some or all of the above processing in the comparison unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the comparison unit can input user interest data into a generative AI and have the generative AI perform comparisons focused on specific matches or players.

[0099] The comparison unit can display the comparison results graphically, making them easy for the user to understand visually. The comparison unit can display the comparison results in graphs or charts, for example. The comparison unit can also visualize and display past and present data, for example. The comparison unit can also display the comparison results visually, making them easy for the user to understand. This makes it easier for the user to understand visually by displaying the comparison results graphically. Graphical display includes, but is not limited to, the types of graphs and display interfaces. Some or all of the above-described processing in the comparison unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the comparison unit can input the comparison results into a generative AI and have the generative AI perform the processing of displaying them graphically.

[0100] The multilingual support unit can estimate the user's emotions and adjust the language selection of the explanation based on the estimated user emotions. For example, if the user is excited, the multilingual support unit will prioritize explanations in the user's native language. For example, if the user is bored, the multilingual support unit can also provide explanations in an interesting language. For example, if the user is nervous, the multilingual support unit can also provide explanations in a reassuring language. In this way, by adjusting the language selection of the explanation according to the user's emotions, explanations can be provided in a more appropriate language. Language selection of the explanation includes, but is not limited to, methods of selection based on the user's language settings. Some or all of the above processing in the multilingual support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the multilingual support unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the language selection of the explanation.

[0101] The multilingual support unit can switch commentary in multiple languages ​​in real time in accordance with the progress of the match. For example, the multilingual support unit can switch the commentary language in real time according to the progress of the match. The multilingual support unit can also, for example, automatically switch the commentary language based on the user's language settings. The multilingual support unit can also, for example, provide commentary in multiple languages ​​at important moments in the match. This makes it easier for the user to understand by switching commentary in multiple languages ​​in real time in accordance with the progress of the match. Switching in real time includes, but is not limited to, the timing and method of switching. Some or all of the above processing in the multilingual support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the multilingual support unit can input match progress data into a generative AI and have the generative AI perform the switching of commentary in multiple languages.

[0102] The multilingual support unit can apply the optimal translation algorithm based on the user's language settings. For example, the multilingual support unit can apply the optimal translation algorithm based on the user's language settings. The multilingual support unit can also select the optimal translation algorithm when the user uses multiple languages. The multilingual support unit can also improve translation accuracy according to the user's language settings. This improves translation accuracy by applying the optimal translation algorithm based on the user's language settings. The optimal translation algorithm includes, but is not limited to, the translation techniques used and the method for selecting the algorithm. Some or all of the above processing in the multilingual support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the multilingual support unit can input the user's language setting data into a generative AI and have the generative AI apply the optimal translation algorithm.

[0103] The multilingual support unit can estimate the user's emotions and select the language of the explanation based on the estimated emotions. For example, if the user is excited, the multilingual support unit will prioritize explanations in the user's native language. For example, if the user is bored, the multilingual support unit can also provide explanations in an interesting language. For example, if the user is nervous, the multilingual support unit can also provide explanations in a reassuring language. By selecting the language of the explanation according to the user's emotions, explanations can be provided in a more appropriate language. The languages ​​of the explanation include, but are not limited to, Japanese, English, and Spanish. Some or all of the processing described above in the multilingual support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the multilingual support unit can input user emotion data into a generative AI and have the generative AI perform the language selection of the explanation.

[0104] The multilingual support unit can provide customized explanations tailored to the user's region and culture when providing multilingual support. For example, the multilingual support unit can provide customized explanations tailored to the user's region. The multilingual support unit can also provide customized explanations tailored to the user's culture. The multilingual support unit can also provide optimal explanations based on the user's region and culture. By providing customized explanations tailored to the user's region and culture, more appropriate explanations can be provided. Customization tailored to region and culture includes, but is not limited to, the use of region-specific terminology and explanations based on cultural background. Some or all of the above processing in the multilingual support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the multilingual support unit can input the user's region and culture data into a generative AI and have the generative AI execute customized explanations.

[0105] The multilingual support unit can display commentary content in multiple languages ​​simultaneously, allowing users to switch languages ​​while watching. The multilingual support unit can, for example, display commentary content in multiple languages ​​simultaneously. The multilingual support unit can, for example, allow users to switch languages ​​while watching. The multilingual support unit can, for example, provide commentary in multiple languages ​​and allow users to freely select a language. This allows users to switch languages ​​while watching by displaying commentary content in multiple languages ​​simultaneously. Simultaneous display in multiple languages ​​includes, but is not limited to, the display interface and language switching method. Some or all of the above processing in the multilingual support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the multilingual support unit can input commentary content into a generative AI and have the generative AI perform simultaneous display in multiple languages.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The commentary section can estimate the players' psychological state as the match progresses and adjust the commentary based on that state. For example, if a player is feeling pressure, the commentary can reflect that psychological state and explain the effects of that pressure. Also, if a player is focused, the commentary can explain how that focus is affecting their play. Furthermore, if a player is fatigued, the commentary can explain the impact of that fatigue on their performance. By providing commentary tailored to the players' psychological state, users can gain a deeper understanding of the players' performance.

[0108] The evaluation unit can consider a player's past injury history when assessing their performance. For example, if a player has suffered a major injury in the past, the evaluation can assess how that injury is affecting their current performance. It can also reflect the recovery status from the injury in the evaluation. Furthermore, for players with a high risk of injury, that risk can be included in the evaluation. By providing evaluations that take into account a player's injury history, a more accurate performance assessment becomes possible.

[0109] The question-answering unit can estimate the user's emotions and adjust the content of the response based on those emotions. For example, if the user is excited, the response can be delivered in a calm tone. If the user is bored, the response can be delivered with an interesting anecdote. Furthermore, if the user is nervous, the response can be delivered in a reassuring manner. By providing responses that match the user's emotions, more appropriate information can be conveyed.

[0110] The comparison unit can take environmental factors into account when comparing past and current match data. For example, it can reflect the impact of weather and stadium conditions on the match. It can also consider the impact of spectator numbers and cheering. Furthermore, it can evaluate the impact of differences in time of day and season on performance. By providing comparisons that take environmental factors into account, more accurate tactical analysis becomes possible.

[0111] The explanation section can estimate the user's emotions and adjust the visual elements of the explanation based on those emotions. For example, if the user is excited, the graphics and animations used in the explanation can be simplified. If the user is bored, visually engaging graphics can be used. Furthermore, if the user is nervous, graphics with calming colors can be used. This allows the explanation to be understood by providing visual elements that match the user's emotions.

[0112] The evaluation unit can consider a player's training data when assessing their performance. For example, it can reflect the impact of a player's training intensity and frequency on their match performance in its evaluation. It can also evaluate how the content and methods of training affect performance. Furthermore, it can include training outcomes and progress in its evaluation. By providing evaluations that take into account a player's training data, it becomes possible to perform more accurate performance assessments.

[0113] The question-answering unit can estimate the user's emotions and adjust the format of the response based on those emotions. For example, if the user is excited, the response can be provided in text format. If the user is bored, the response can be provided in audio format. Furthermore, if the user is nervous, the response can be provided in video format. By providing a response format that matches the user's emotions, more appropriate information can be conveyed.

[0114] The comparison section can take into account the players' condition when comparing past and current match data. For example, it can reflect the impact of a player's fatigue level and physical condition on their performance in a match. It can also consider whether a player has been injured and their recovery status. Furthermore, it can evaluate the impact of a player's mental condition on their performance. By providing comparisons that take into account the players' condition, more accurate tactical analysis becomes possible.

[0115] The commentary section can estimate the user's emotions and adjust the audio elements of the commentary based on those emotions. For example, if the user is excited, the commentary's voice tone can be made calmer. If the user is bored, the voice tone can be made brighter. Furthermore, if the user is nervous, the voice tone can be made gentler. This allows the commentary to be understood by providing audio elements that match the user's emotions.

[0116] The evaluation unit can consider athlete nutrition data when assessing athlete performance. For example, it can reflect the impact of an athlete's diet and nutritional intake on performance in its evaluation. It can also evaluate how nutritional balance and supplement use affect performance. Furthermore, changes in an athlete's weight and body fat percentage can be included in the evaluation. By providing evaluations that take into account athletes' nutritional data, a more accurate performance assessment becomes possible.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The commentary team will immediately explain any changes in the team's formation or tactics as the match progresses. For example, if the team changes formation during the match, they will explain the intention and effect of that change. They can also highlight and explain tactical changes when the flow of the match shifts. If an important play occurs, they can immediately explain the tactical significance of that play. Step 2: The evaluation unit assesses each player's movements, roles, and performance based on data. For example, it provides real-time commentary on how a particular player is moving and how their role is changing. It can collect player performance data in real time and update evaluations. It can also change player evaluations in real time as the game progresses. If an important play occurs, its impact can be reflected in the evaluation. Step 3: The question-answering section allows users to ask questions on the spot, and the AI ​​provides instant answers. For example, if a user asks, "What is this player's role?", the AI ​​will explain the player's role and performance. If a user inputs a question they have during a match, the AI ​​can provide an immediate answer. If a user asks a question about past match data, the AI ​​can also provide an answer based on that data.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the commentary unit, evaluation unit, question answering unit, comparison unit, and multilingual support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the commentary unit is implemented by the control unit 46A of the smart device 14 and provides immediate commentary on team formations and tactical changes in accordance with the progress of the match. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the movements, roles, and performance of each player based on data. The question answering unit is implemented by the control unit 46A of the smart device 14 and allows users to ask questions on the spot. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares past match data with the current match to analyze changes in tactics and trends. The multilingual support unit is implemented by the control unit 46A of the smart device 14 and provides commentary in multiple languages, including Japanese, even for matches in overseas leagues. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the commentary unit, evaluation unit, question answering unit, comparison unit, and multilingual support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the commentary unit is implemented by the control unit 46A of the smart glasses 214 and provides immediate commentary on team formations and tactical changes in accordance with the progress of the match. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the movements, roles, and performance of each player based on data. The question answering unit is implemented by the control unit 46A of the smart glasses 214 and allows users to ask questions on the spot. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares past match data with the current match to analyze changes in tactics and trends. The multilingual support unit is implemented by the control unit 46A of the smart glasses 214 and provides commentary in multiple languages, including Japanese, even for matches in overseas leagues. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the commentary unit, evaluation unit, question answering unit, comparison unit, and multilingual support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the commentary unit is implemented by the control unit 46A of the headset terminal 314 and provides immediate commentary on team formations and tactical changes in accordance with the progress of the match. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the movements, roles, and performance of each player based on data. The question answering unit is implemented by the control unit 46A of the headset terminal 314 and allows users to ask questions on the spot. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares past match data with the current match to analyze changes in tactics and trends. The multilingual support unit is implemented by the control unit 46A of the headset terminal 314 and provides commentary in multiple languages, including Japanese, even for matches in overseas leagues. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the commentary unit, evaluation unit, question answering unit, comparison unit, and multilingual support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the commentary unit is implemented by the control unit 46A of the robot 414 and provides immediate commentary on team formations and tactical changes in accordance with the progress of the match. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the movements, roles, and performance of each player based on data. The question answering unit is implemented by the control unit 46A of the robot 414 and allows users to ask questions on the spot. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares past match data with the current match to analyze changes in tactics and trends. The multilingual support unit is implemented by the control unit 46A of the robot 414 and provides commentary in multiple languages, including Japanese, even for matches in overseas leagues. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) The commentary team provides immediate explanations of team formations and tactical changes as the match progresses, The evaluation department evaluates each player's movements, roles, and performance based on data, It includes a question-answering unit that allows users to ask questions on the spot and for the AI ​​to provide immediate answers. A system characterized by the following features. (Note 2) It includes a comparison section that compares past match data with current matches to analyze tactical changes and trends. The system described in Appendix 1, characterized by the features described herein. (Note 3) Even for overseas league matches, it features a multilingual support section that provides commentary in multiple languages, including Japanese. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned explanatory section is, If a team changes its formation during a match, explain the intention and effect of that change. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, This provides real-time commentary on the movements of specific players and how their roles are changing. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned question answering unit is In response to user questions such as "What is this player's role?", the system explains the player's role and performance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned explanatory section is, It estimates the user's emotions and adjusts the tone and level of detail of the explanation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned explanatory section is, The commentary will be updated in real time according to the progress of the match, highlighting important tactical changes. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned explanatory section is, When changing the team's formation, explain its effects by comparing it to similar changes in the past. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned explanatory section is, It estimates the user's emotions and adjusts the order of explanations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned explanatory section is, The commentary is customized according to the user's level of soccer knowledge as the match progresses. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned explanatory section is, During commentary, individual player performance data is displayed in real time to supplement the commentary. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, It estimates the user's emotions and adjusts the level of detail in the rating based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, The system evaluates each player's performance in real time and updates the evaluation as the match progresses. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, When changing a player's role, evaluate the impact that change will have on the game. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, When evaluating players, their current performance is compared to past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, Display evaluation results graphically to make them easier for users to understand visually. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned question answering unit is It estimates the user's emotions and adjusts the tone and level of detail of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned question answering unit is It analyzes the user's question history and provides the best answer based on past questions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned question answering unit is When answering questions, we refer to relevant past match data to improve the accuracy of our answers. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned question answering unit is It estimates the user's emotions and adjusts the order of responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned question answering unit is When answering questions, adjust the level of detail in the answers according to the user's level of soccer knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned question answering unit is When answering a question, relevant player performance data is displayed to supplement the answer. The system described in Appendix 1, characterized by the features described herein. (Note 25) The comparison unit is, It estimates the user's emotions and adjusts how comparison results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The comparison unit is, By comparing past and current match data in real time, changes in tactics are highlighted. The system described in Appendix 2, characterized by the features described herein. (Note 27) The comparison unit is, When making comparisons, we analyze in detail the changes in the performance of specific players or teams. The system described in Appendix 2, characterized by the features described herein. (Note 28) The comparison unit is, It estimates the user's emotions and adjusts the order of comparison results based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The comparison unit is, When making comparisons, the comparisons are focused on specific matches or players that the user is interested in. The system described in Appendix 2, characterized by the features described herein. (Note 30) The comparison unit is, Display the comparison results graphically to make them easier for users to understand visually. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned multilingual support unit is It estimates the user's emotions and adjusts the language selection of the explanations based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned multilingual support unit is The commentary switches between multiple languages ​​in real time, in accordance with the progress of the match. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned multilingual support unit is The optimal translation algorithm is applied based on the user's language settings. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned multilingual support unit is It estimates the user's emotions and selects the language of the explanation based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned multilingual support unit is When supporting multiple languages, provide customized explanations tailored to the user's region and culture. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned multilingual support unit is The commentary will be displayed in multiple languages ​​simultaneously, allowing users to switch languages ​​while watching the game. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0191] 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. The commentary team provides immediate explanations of team formations and tactical changes as the match progresses, The evaluation department evaluates each player's movements, roles, and performance based on data, It includes a question-answering unit that allows users to ask questions on the spot and for AI to provide immediate answers. A system characterized by the following features.

2. It includes a comparison section that compares past match data with current matches to analyze tactical changes and trends. The system according to feature 1.

3. Even for overseas league matches, it features a multilingual support section that provides commentary in multiple languages, including Japanese. The system according to feature 1.

4. The aforementioned explanatory section is, If a team changes its formation during a match, explain the intention and effect of that change. The system according to feature 1.

5. The evaluation unit, This provides real-time commentary on the movements of specific players and how their roles are changing. The system according to feature 1.

6. The aforementioned explanatory section is, It estimates the user's emotions and adjusts the tone and level of detail of the explanation based on those estimated emotions. The system according to feature 1.

7. The aforementioned explanatory section is, The commentary will be updated in real time according to the progress of the match, highlighting important tactical changes. The system according to feature 1.

Images