system

Abstract

The system according to this embodiment aims to analyze the movements of athletes and provide appropriate feedback. [Solution] The system according to the embodiment comprises a tracking unit, an analysis unit, a commentary unit, a generation unit, and a provision unit. The tracking unit tracks the movements of the players. The analysis unit analyzes the movements tracked by the tracking unit. The commentary unit generates commentary based on the movements analyzed by the analysis unit. The generation unit generates highlights based on the commentary generated by the commentary unit. The provision unit provides feedback based on the highlights and analysis results generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the movement of sports players is not sufficiently analyzed in real time to provide appropriate feedback, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze the movement of sports players and provide appropriate feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a tracking unit, an analysis unit, a commentary unit, a generation unit, and a provision unit. The tracking unit tracks the movements of the players. The analysis unit analyzes the movements tracked by the tracking unit. The commentary unit generates commentary based on the movements analyzed by the analysis unit. The generation unit generates highlights based on the commentary generated by the commentary unit. The provision unit provides feedback based on the highlights and analysis results generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the movements of athletes and provide appropriate feedback. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The sports commentary system according to an embodiment of the present invention is a system equipped with an AI agent function, providing a means of remote viewing for families who cannot attend to support their children, and a system that provides content useful for growth and skill improvement. This system has the function of tracking and analyzing the movements of players in real time, generating commentary, automatically generating highlights, and providing feedback. For example, the sports commentary system installs an AI camera to track the player (child) in real time. Next, the sports commentary system analyzes the movements of the player and provides real-time commentary according to the progress of the game and the movements of the player. Families can cheer using smartphones or tablets at home. Furthermore, the sports commentary system automatically generates highlights of the game and provides feedback by analyzing skills and plays. This service can be used not only for supporting children's sports but also in a wide range of situations such as school events and community events, and will play a role as a next-generation platform that connects families and communities more deeply. For example, the sports commentary system allows families to watch and cheer in real time even when they cannot attend school sports days or local festivals. As a result, the sports commentary system can allow families to cheer on their children from a distance and provide feedback useful for the growth and skill improvement of players.

[0029] The sports commentary system according to this embodiment comprises a tracking unit, an analysis unit, a commentary unit, a generation unit, and a provision unit. The tracking unit tracks the movements of players. The tracking unit tracks the movements of players in real time, for example, using an AI camera. The tracking unit can automatically track players with a camera and record their movements as they move around the field. The analysis unit analyzes the movements tracked by the tracking unit. The analysis unit analyzes the movements of players in detail, for example, using AI. The analysis unit can analyze the patterns of players' movements and evaluate the progress of the match and the players' performance. The commentary unit generates commentary based on the movements analyzed by the analysis unit. The commentary unit generates commentary in accordance with the progress of the match and the movements of players, for example, using AI. The commentary unit can provide detailed commentary when a player scores a goal or when an important play occurs. The generation unit generates highlights based on the commentary generated by the commentary unit. The generation unit automatically generates match highlights, for example, using AI. The generation unit can generate highlight videos focusing on the players' plays after the match ends. The provision unit provides feedback based on the highlights and analysis results generated by the generation unit. For example, the provision unit can use AI to analyze the players' skills and plays and provide feedback that helps improve their technique. The provision unit can also provide suggestions for improving the players' movements and advice for the next match. As a result, the sports commentary system according to this embodiment can efficiently track, analyze, commentate on, generate highlights, and provide feedback on the players' movements.

[0030] The tracking unit tracks the movements of the players. For example, the tracking unit uses an AI camera to track the players' movements in real time. Specifically, the AI ​​camera captures high-resolution video, allowing it to accurately capture the players' positions and movements. The camera is equipped with a function to automatically track players' movements, so when a player moves around the field, the camera automatically tracks the player and records their movements. Furthermore, the AI ​​camera can recognize players' uniforms and jersey numbers, allowing it to identify specific players. This makes it possible to accurately track the movements of a specific player even when multiple players are moving simultaneously. The tracking unit transmits this data to a central database in real time, making it accessible to other departments. For example, it collects data such as players' position, speed, and direction during a match, making it available to the analysis and commentary departments. This allows the tracking unit to efficiently and accurately track player movements and improve the overall performance of the system.

[0031] The analysis unit analyzes the movements tracked by the tracking unit. For example, the analysis unit uses AI to analyze the players' movements in detail. Specifically, the AI ​​can analyze the patterns of the players' movements and evaluate the progress of the match and the players' performance. The AI ​​uses machine learning algorithms to extract the characteristics of the players' movements and compare them with past data to understand changes and trends in the players' performance. For example, it analyzes data such as the distance covered by the players, the number of sprints, acceleration, and deceleration to evaluate the players' physical fitness and tactical adaptability. The AI ​​can also analyze the coordination of the players' movements and the effectiveness of the team's overall tactics. This allows the analysis unit to evaluate not only the individual performance of the players but also the team's overall tactics and strategies. Furthermore, the analysis unit can process data in real time and make rapid evaluations according to the situation during the match. This allows the analysis unit to analyze the progress of the match and the players' performance in detail, improving the reliability and accuracy of the entire system.

[0032] The commentary department generates commentary based on movements analyzed by the analysis department. For example, the commentary department uses AI to generate commentary that matches the progress of the match and the players' movements. Specifically, the AI ​​analyzes the players' movements and the match situation in real time and generates commentary in natural language based on that information. The AI ​​recognizes players' names, positions, movement characteristics, and important moments in the match, and provides appropriate commentary accordingly. For example, it can provide detailed commentary when a player scores a goal or when an important play occurs. The AI ​​learns from past commentary data and incorporates natural phrasing and emotional expressions to provide immersive commentary. Furthermore, the AI ​​supports multiple languages ​​and can generate commentary in different languages. This allows the commentary department to provide immersive, real-time commentary that matches the progress of the match and the players' movements. In addition, the commentary department can customize the style and content of the commentary according to user preferences. For example, it can provide diverse commentary tailored to user needs, such as commentary focusing on specific players or teams, or commentary including explanations of tactics and strategies. This allows the commentary team to provide users with more engaging and immersive commentary, improving the overall match-watching experience.

[0033] The generation unit generates highlights based on the commentary generated by the commentary unit. The generation unit can, for example, automatically generate match highlights using AI. Specifically, the AI ​​analyzes important moments and player actions during the match and generates highlight videos based on them. The AI ​​automatically extracts goal scenes, important plays, and moments that change the flow of the game, and edits them into a single highlight video. The AI ​​performs video transitions, sound effects, and text insertion to create visually appealing highlight videos. Furthermore, the AI ​​can customize the content and length of the highlight videos according to the user's preferences. For example, it can provide a variety of highlight videos that meet user needs, such as highlights focusing on specific players or teams, or highlights specializing in specific plays. As a result, the generation unit can automatically generate and provide users with attractive highlight videos centered on player actions after the match ends. In addition, the generation unit can generate highlight videos in real time, instantly generating and providing highlight videos when important moments occur during the match. As a result, the generation unit can provide users with quick and attractive highlight videos, improving the match viewing experience.

[0034] The service provider provides feedback based on the highlights and analysis results generated by the generation unit. For example, the service provider uses AI to analyze players' skills and play and provide feedback that helps improve their technique. Specifically, the AI ​​analyzes data on players' movements and play to identify their strengths and areas for improvement. The AI ​​analyzes the patterns of players' movements and the details of their techniques, specifically indicating which areas the player should improve. For example, it analyzes players' shooting form, passing accuracy, and defensive movements to provide specific advice for improving their technique. The AI ​​can also evaluate a player's growth and progress by comparing it with past data and data from other players. This allows the service provider to provide players with specific and practical feedback to support their technique improvement. Furthermore, the service provider can provide feedback in real time, allowing for immediate advice during matches and practices. This enables the service provider to quickly support players' technique improvement and effectively prepare for the next match. In addition, the service provider can collect player feedback and continuously improve the overall system performance. This allows the service provider to provide players with high-quality feedback and support their technique improvement.

[0035] The spectator unit is a component that allows families to watch games at home using smartphones and tablets. For example, the spectator unit allows families to watch games in real time at home using smartphones and tablets. The spectator unit allows families to cheer on their teams at home using smartphones and tablets. For example, the spectator unit allows families to watch live game commentary at home using smartphones and tablets. The spectator unit allows families to watch game highlights at home using smartphones and tablets. This enables families to watch games at home using smartphones and tablets.

[0036] The Events section is a component that supports use in school events and community events. For example, the Events section can support use in school events and community events. The Events section has functions to support use in school events and community events. For example, the Events section can provide information to support use in school events and community events. The Events section can provide an interface to support use in school events and community events. This allows for support of use in school events and community events.

[0037] The tracking unit can track the player's movements in real time. For example, the tracking unit uses an AI camera to track the player's movements in real time. The tracking unit can automatically track the player and record their movements as they move around the field. The tracking unit can use sensors to track the player's movements in real time. The tracking unit can use algorithms to track the player's movements in real time. This allows for real-time tracking of the player's movements.

[0038] The analysis unit can analyze players' movements in detail. For example, the analysis unit can use AI to analyze players' movements in detail. The analysis unit can analyze patterns of players' movements and evaluate the progress of the game and players' performance. The analysis unit can use algorithms to analyze players' movements in detail. The analysis unit can collect data to analyze players' movements in detail. This allows for a detailed analysis of players' movements.

[0039] The commentary team can generate commentary in accordance with the progress of the match and the movements of the players. For example, the commentary team can use AI to generate commentary in accordance with the progress of the match and the movements of the players. The commentary team can provide detailed commentary when a player scores a goal or when an important play occurs. The commentary team can use algorithms to generate commentary in accordance with the progress of the match and the movements of the players. The commentary team can collect data to generate commentary in accordance with the progress of the match and the movements of the players. This allows them to generate commentary in accordance with the progress of the match and the movements of the players.

[0040] The generation unit can automatically generate match highlights. For example, the generation unit can use AI to automatically generate match highlights. After the match ends, the generation unit can generate highlight videos focusing on player performance. The generation unit can use algorithms for automatically generating match highlights. The generation unit can collect data for automatically generating match highlights. This enables the automatic generation of match highlights.

[0041] The service provider can analyze skills and play and provide feedback. For example, the service provider can use AI to analyze a player's skills and play and provide feedback to help improve their technique. The service provider can provide suggestions for improving a player's movements and advice for the next match. The service provider can use algorithms to analyze skills and play and provide feedback. The service provider can collect data to analyze skills and play and provide feedback. This allows them to analyze skills and play and provide feedback.

[0042] The tracking unit can learn the player's past movement patterns and optimize tracking based on predictions. For example, the tracking unit can analyze the player's past match data, learn specific movement patterns, and track while predicting the next movement. Based on the player's training data, the tracking unit can understand movement tendencies and predict and track movements during a match. The tracking unit can cluster the player's past movement patterns and predict and track similar movements. This allows the unit to learn the player's past movement patterns and optimize tracking based on predictions.

[0043] The tracking unit can adjust its tracking method while considering the athlete's physical condition and fatigue level. For example, the tracking unit can monitor the athlete's heart rate and respiratory rate to estimate their physical condition and fatigue level and adjust the tracking method. The tracking unit can analyze the athlete's movement speed and acceleration to estimate their fatigue level and adjust the tracking method. The tracking unit can estimate their fatigue level from their facial expressions and movements and adjust the tracking method. In this way, the tracking method can be adjusted while considering the athlete's physical condition and fatigue level.

[0044] The tracking unit can record a player's position information as a relative position to other players or objects during tracking. For example, the tracking unit can record a player's position information as a relative position to other players and analyze the interaction of movements during a match. The tracking unit can record a player's position information as a relative position to specific objects on the field (goal, bench, etc.) and analyze movement patterns. The tracking unit can record a player's position information as a relative position to the ball and analyze its relationship to the ball's movement. This allows the tracking unit to record a player's position information as a relative position to other players or objects.

[0045] The tracking unit can improve tracking accuracy by integrating data from wearable devices worn by the athlete during tracking. For example, the tracking unit can improve tracking accuracy by integrating data from a heart rate monitor worn by the athlete. The tracking unit can improve tracking accuracy by integrating data from an accelerometer worn by the athlete to capture details of movement. The tracking unit can improve the accuracy of location information by integrating data from a GPS device worn by the athlete. In this way, the tracking accuracy is improved by integrating data from wearable devices worn by the athlete.

[0046] The analysis unit can perform a detailed analysis of the player's movement speed and acceleration during the analysis. For example, the analysis unit can analyze the player's movement speed to evaluate their performance during a match. The analysis unit can analyze the player's movement acceleration to evaluate the intensity and explosiveness of their movements. The analysis unit can combine the player's movement speed and acceleration to evaluate the efficiency of their movements. This allows for a detailed analysis of the player's movement speed and acceleration.

[0047] The analysis unit can cluster the movement patterns of players during analysis and extract features. For example, the analysis unit can cluster the movement patterns of players and extract characteristic movements. The analysis unit can cluster the movement patterns of players and analyze the differences in movements in different matches. The analysis unit can cluster the movement patterns of players and compare them with other players. This allows for the clustering of movement patterns of players and the extraction of features.

[0048] The analysis unit can analyze player movements in relation to the progress of the game. For example, the analysis unit can analyze player movements in relation to the progress of the game and evaluate the impact of important plays. The analysis unit can analyze player movements in relation to the progress of the game and understand the flow of the game. The analysis unit can analyze player movements in relation to the progress of the game and evaluate the effectiveness of tactics. This allows for the analysis of player movements in relation to the progress of the game.

[0049] The analysis unit can analyze the interaction between a player's movements and the movements of other players during the analysis. For example, the analysis unit can analyze the interaction between a player's movements and the movements of other players to evaluate the effectiveness of team play. The analysis unit can analyze the interaction between a player's movements and the movements of other players to evaluate the success rate of coordinated plays. The analysis unit can analyze the interaction between a player's movements and the movements of other players to evaluate the role of individual players. This allows for the analysis of the interaction between a player's movements and the movements of other players.

[0050] The commentators can highlight important moments in the match during their commentary. For example, they can emphasize goals and scoring moments to convey the excitement to the audience. They can highlight important plays and tactical turning points to convey the flow of the game. They can highlight the individual performances of players to convey their efforts to the audience. This allows them to highlight important moments in the match during their commentary.

[0051] The commentators can enhance their commentary by referring to the players' past performance data. For example, they can refer to a player's past match data and compare it to their current performance. They can refer to a player's past statistics and emphasize their improvement in the current match. They can refer to a player's past playing style and comment on changes in the current match. In this way, they can enhance their commentary by referring to a player's past performance data.

[0052] The commentary team can display match statistics in real time during the broadcast. For example, they can display statistics on goals scored and assists during the match in real time. They can also display player performance data (distance covered, number of shots, etc.) in real time. Furthermore, they can display overall team statistics (ball possession rate, pass completion rate, etc.) in real time. This allows for the display of match statistics in real time.

[0053] The commentators can insert player profile information during live commentary. For example, they can insert basic player information (name, age, position, etc.). They can also insert players' past performance and career history. They can also insert players' personal anecdotes and background information. This allows them to insert player profile information during live commentary.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The tracking unit can further improve tracking accuracy by integrating data from wearable devices worn by the athlete. For example, the tracking unit can improve tracking accuracy by integrating data from a heart rate monitor worn by the athlete. It can improve tracking accuracy by integrating data from an accelerometer worn by the athlete to capture detailed movement information. It can improve the accuracy of location information by integrating data from a GPS device worn by the athlete. In this way, tracking accuracy is improved by integrating data from wearable devices worn by the athlete.

[0056] The analysis unit can further cluster the players' movement patterns and extract features. For example, the analysis unit can cluster the players' movement patterns and extract characteristic movements. By clustering the players' movement patterns, it can analyze the differences in movements across different matches. By clustering the players' movement patterns, it can compare them with those of other players. This allows for the clustering of players' movement patterns and the extraction of features.

[0057] The tracking unit can further learn the player's past movement patterns and optimize tracking based on predictions. For example, the tracking unit can analyze the player's past match data, learn specific movement patterns, and track while predicting the next movement. Based on the player's training data, it can understand movement tendencies and predict and track movements during matches. It can cluster the player's past movement patterns and predict and track similar movements. This allows the unit to learn the player's past movement patterns and optimize tracking based on predictions.

[0058] The analysis unit can further analyze the relationship between player movements and the progress of the match. For example, the analysis unit can analyze player movements in relation to the progress of the match and evaluate the impact of important plays. By analyzing player movements in relation to the progress of the match, it is possible to grasp the flow of the match. By analyzing player movements in relation to the progress of the match, it is possible to evaluate the effectiveness of tactics. In this way, it is possible to analyze player movements in relation to the progress of the match.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The tracking unit tracks the players' movements. For example, it uses an AI camera to track players' movements in real time, with the camera automatically following the players as they move around the field and recording their movements. Step 2: The analysis unit analyzes the movements tracked by the tracking unit. For example, it uses AI to analyze the players' movements in detail, analyzes the patterns of the players' movements, and evaluates the progress of the match and the players' performance. Step 3: The commentary unit generates commentary based on the movements analyzed by the analysis unit. For example, it uses AI to generate commentary according to the progress of the match and the movements of the players, and provides detailed commentary when a player scores a goal or when an important play occurs. Step 4: The generation unit generates highlights based on the commentary generated by the commentary unit. For example, it uses AI to automatically generate match highlights and, after the match ends, generates a highlight video focusing on the players' plays. Step 5: The providing unit provides feedback based on the highlights and analysis results generated by the generating unit. For example, it uses AI to analyze the players' skills and play, providing feedback that helps improve their technique, and offering suggestions for improving the players' movements and advice for the next match.

[0061] (Example of form 2) The sports commentary system according to an embodiment of the present invention is a system equipped with an AI agent function, providing a means of remote viewing for families who cannot attend to support their children, and a system that provides content useful for growth and skill improvement. This system has the function of tracking and analyzing the movements of players in real time, generating commentary, automatically generating highlights, and providing feedback. For example, the sports commentary system installs an AI camera to track the player (child) in real time. Next, the sports commentary system analyzes the movements of the player and provides real-time commentary according to the progress of the game and the movements of the player. Families can cheer using smartphones or tablets at home. Furthermore, the sports commentary system automatically generates highlights of the game and provides feedback by analyzing skills and plays. This service can be used not only for supporting children's sports but also in a wide range of situations such as school events and community events, and will play a role as a next-generation platform that connects families and communities more deeply. For example, the sports commentary system allows families to watch and cheer in real time even when they cannot attend school sports days or local festivals. As a result, the sports commentary system can allow families to cheer on their children from a distance and provide feedback useful for the growth and skill improvement of players.

[0062] The sports commentary system according to this embodiment comprises a tracking unit, an analysis unit, a commentary unit, a generation unit, and a provision unit. The tracking unit tracks the movements of players. The tracking unit tracks the movements of players in real time, for example, using an AI camera. The tracking unit can automatically track players with a camera and record their movements as they move around the field. The analysis unit analyzes the movements tracked by the tracking unit. The analysis unit analyzes the movements of players in detail, for example, using AI. The analysis unit can analyze the patterns of players' movements and evaluate the progress of the match and the players' performance. The commentary unit generates commentary based on the movements analyzed by the analysis unit. The commentary unit generates commentary in accordance with the progress of the match and the movements of players, for example, using AI. The commentary unit can provide detailed commentary when a player scores a goal or when an important play occurs. The generation unit generates highlights based on the commentary generated by the commentary unit. The generation unit automatically generates match highlights, for example, using AI. The generation unit can generate highlight videos focusing on the players' plays after the match ends. The provision unit provides feedback based on the highlights and analysis results generated by the generation unit. For example, the provision unit can use AI to analyze the players' skills and plays and provide feedback that helps improve their technique. The provision unit can also provide suggestions for improving the players' movements and advice for the next match. As a result, the sports commentary system according to this embodiment can efficiently track, analyze, commentate on, generate highlights, and provide feedback on the players' movements.

[0063] The tracking unit tracks the movements of the players. For example, the tracking unit uses an AI camera to track the players' movements in real time. Specifically, the AI ​​camera captures high-resolution video, allowing it to accurately capture the players' positions and movements. The camera is equipped with a function to automatically track players' movements, so when a player moves around the field, the camera automatically tracks the player and records their movements. Furthermore, the AI ​​camera can recognize players' uniforms and jersey numbers, allowing it to identify specific players. This makes it possible to accurately track the movements of a specific player even when multiple players are moving simultaneously. The tracking unit transmits this data to a central database in real time, making it accessible to other departments. For example, it collects data such as players' position, speed, and direction during a match, making it available to the analysis and commentary departments. This allows the tracking unit to efficiently and accurately track player movements and improve the overall performance of the system.

[0064] The analysis unit analyzes the movements tracked by the tracking unit. For example, the analysis unit uses AI to analyze the players' movements in detail. Specifically, the AI ​​can analyze the patterns of the players' movements and evaluate the progress of the match and the players' performance. The AI ​​uses machine learning algorithms to extract the characteristics of the players' movements and compare them with past data to understand changes and trends in the players' performance. For example, it analyzes data such as the distance covered by the players, the number of sprints, acceleration, and deceleration to evaluate the players' physical fitness and tactical adaptability. The AI ​​can also analyze the coordination of the players' movements and the effectiveness of the team's overall tactics. This allows the analysis unit to evaluate not only the individual performance of the players but also the team's overall tactics and strategies. Furthermore, the analysis unit can process data in real time and make rapid evaluations according to the situation during the match. This allows the analysis unit to analyze the progress of the match and the players' performance in detail, improving the reliability and accuracy of the entire system.

[0065] The commentary department generates commentary based on movements analyzed by the analysis department. For example, the commentary department uses AI to generate commentary that matches the progress of the match and the players' movements. Specifically, the AI ​​analyzes the players' movements and the match situation in real time and generates commentary in natural language based on that information. The AI ​​recognizes players' names, positions, movement characteristics, and important moments in the match, and provides appropriate commentary accordingly. For example, it can provide detailed commentary when a player scores a goal or when an important play occurs. The AI ​​learns from past commentary data and incorporates natural phrasing and emotional expressions to provide immersive commentary. Furthermore, the AI ​​supports multiple languages ​​and can generate commentary in different languages. This allows the commentary department to provide immersive, real-time commentary that matches the progress of the match and the players' movements. In addition, the commentary department can customize the style and content of the commentary according to user preferences. For example, it can provide diverse commentary tailored to user needs, such as commentary focusing on specific players or teams, or commentary including explanations of tactics and strategies. This allows the commentary team to provide users with more engaging and immersive commentary, improving the overall match-watching experience.

[0066] The generation unit generates highlights based on the commentary generated by the commentary unit. The generation unit can, for example, automatically generate match highlights using AI. Specifically, the AI ​​analyzes important moments and player actions during the match and generates highlight videos based on them. The AI ​​automatically extracts goal scenes, important plays, and moments that change the flow of the game, and edits them into a single highlight video. The AI ​​performs video transitions, sound effects, and text insertion to create visually appealing highlight videos. Furthermore, the AI ​​can customize the content and length of the highlight videos according to the user's preferences. For example, it can provide a variety of highlight videos that meet user needs, such as highlights focusing on specific players or teams, or highlights specializing in specific plays. As a result, the generation unit can automatically generate and provide users with attractive highlight videos centered on player actions after the match ends. In addition, the generation unit can generate highlight videos in real time, instantly generating and providing highlight videos when important moments occur during the match. As a result, the generation unit can provide users with quick and attractive highlight videos, improving the match viewing experience.

[0067] The service provider provides feedback based on the highlights and analysis results generated by the generation unit. For example, the service provider uses AI to analyze players' skills and play and provide feedback that helps improve their technique. Specifically, the AI ​​analyzes data on players' movements and play to identify their strengths and areas for improvement. The AI ​​analyzes the patterns of players' movements and the details of their techniques, specifically indicating which areas the player should improve. For example, it analyzes players' shooting form, passing accuracy, and defensive movements to provide specific advice for improving their technique. The AI ​​can also evaluate a player's growth and progress by comparing it with past data and data from other players. This allows the service provider to provide players with specific and practical feedback to support their technique improvement. Furthermore, the service provider can provide feedback in real time, allowing for immediate advice during matches and practices. This enables the service provider to quickly support players' technique improvement and effectively prepare for the next match. In addition, the service provider can collect player feedback and continuously improve the overall system performance. This allows the service provider to provide players with high-quality feedback and support their technique improvement.

[0068] The spectator unit is a component that allows families to watch games at home using smartphones and tablets. For example, the spectator unit allows families to watch games in real time at home using smartphones and tablets. The spectator unit allows families to cheer on their teams at home using smartphones and tablets. For example, the spectator unit allows families to watch live game commentary at home using smartphones and tablets. The spectator unit allows families to watch game highlights at home using smartphones and tablets. This enables families to watch games at home using smartphones and tablets.

[0069] The Events section is a component that supports use in school events and community events. For example, the Events section can support use in school events and community events. The Events section has functions to support use in school events and community events. For example, the Events section can provide information to support use in school events and community events. The Events section can provide an interface to support use in school events and community events. This allows for support of use in school events and community events.

[0070] The tracking unit can track the player's movements in real time. For example, the tracking unit uses an AI camera to track the player's movements in real time. The tracking unit can automatically track the player and record their movements as they move around the field. The tracking unit can use sensors to track the player's movements in real time. The tracking unit can use algorithms to track the player's movements in real time. This allows for real-time tracking of the player's movements.

[0071] The analysis unit can analyze players' movements in detail. For example, the analysis unit can use AI to analyze players' movements in detail. The analysis unit can analyze patterns of players' movements and evaluate the progress of the game and players' performance. The analysis unit can use algorithms to analyze players' movements in detail. The analysis unit can collect data to analyze players' movements in detail. This allows for a detailed analysis of players' movements.

[0072] The commentary team can generate commentary in accordance with the progress of the match and the movements of the players. For example, the commentary team can use AI to generate commentary in accordance with the progress of the match and the movements of the players. The commentary team can provide detailed commentary when a player scores a goal or when an important play occurs. The commentary team can use algorithms to generate commentary in accordance with the progress of the match and the movements of the players. The commentary team can collect data to generate commentary in accordance with the progress of the match and the movements of the players. This allows them to generate commentary in accordance with the progress of the match and the movements of the players.

[0073] The generation unit can automatically generate match highlights. For example, the generation unit can use AI to automatically generate match highlights. After the match ends, the generation unit can generate highlight videos focusing on player performance. The generation unit can use algorithms for automatically generating match highlights. The generation unit can collect data for automatically generating match highlights. This enables the automatic generation of match highlights.

[0074] The service provider can analyze skills and play and provide feedback. For example, the service provider can use AI to analyze a player's skills and play and provide feedback to help improve their technique. The service provider can provide suggestions for improving a player's movements and advice for the next match. The service provider can use algorithms to analyze skills and play and provide feedback. The service provider can collect data to analyze skills and play and provide feedback. This allows them to analyze skills and play and provide feedback.

[0075] The tracking unit can estimate the athlete's emotions and adjust the tracking accuracy based on the estimated emotions. For example, if the athlete is nervous, the tracking unit can adjust to track the athlete's movements more precisely, capturing even subtle movements. If the athlete is relaxed, the tracking unit can adjust to track the athlete's movements more broadly, focusing on overall movement. If the athlete is tired, the tracking unit can adjust to track the athlete's movements more slowly, taking fatigue into account. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the tracking accuracy to be adjusted based on the athlete's emotions.

[0076] The tracking unit can learn the player's past movement patterns and optimize tracking based on predictions. For example, the tracking unit can analyze the player's past match data, learn specific movement patterns, and track while predicting the next movement. Based on the player's training data, the tracking unit can understand movement tendencies and predict and track movements during a match. The tracking unit can cluster the player's past movement patterns and predict and track similar movements. This allows the unit to learn the player's past movement patterns and optimize tracking based on predictions.

[0077] The tracking unit can adjust its tracking method while considering the athlete's physical condition and fatigue level. For example, the tracking unit can monitor the athlete's heart rate and respiratory rate to estimate their physical condition and fatigue level and adjust the tracking method. The tracking unit can analyze the athlete's movement speed and acceleration to estimate their fatigue level and adjust the tracking method. The tracking unit can estimate their fatigue level from their facial expressions and movements and adjust the tracking method. In this way, the tracking method can be adjusted while considering the athlete's physical condition and fatigue level.

[0078] The tracking unit can estimate the emotions of the athletes and determine tracking priorities based on the estimated emotions. For example, if an athlete is excited, the tracking unit will prioritize tracking that athlete's movements. If an athlete is calm, the tracking unit can prioritize tracking the movements of other athletes. If an athlete is tired, the tracking unit can prioritize tracking that athlete's movements and adjust the tracking method to take fatigue into account. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows tracking priorities to be determined based on the athletes' emotions.

[0079] The tracking unit can record a player's position information as a relative position to other players or objects during tracking. For example, the tracking unit can record a player's position information as a relative position to other players and analyze the interaction of movements during a match. The tracking unit can record a player's position information as a relative position to specific objects on the field (goal, bench, etc.) and analyze movement patterns. The tracking unit can record a player's position information as a relative position to the ball and analyze its relationship to the ball's movement. This allows the tracking unit to record a player's position information as a relative position to other players or objects.

[0080] The tracking unit can improve tracking accuracy by integrating data from wearable devices worn by the athlete during tracking. For example, the tracking unit can improve tracking accuracy by integrating data from a heart rate monitor worn by the athlete. The tracking unit can improve tracking accuracy by integrating data from an accelerometer worn by the athlete to capture details of movement. The tracking unit can improve the accuracy of location information by integrating data from a GPS device worn by the athlete. In this way, the tracking accuracy is improved by integrating data from wearable devices worn by the athlete.

[0081] The analysis unit can estimate the athlete's emotions and adjust the analysis method based on the estimated emotions. For example, if the athlete is tense, the analysis unit can analyze subtle changes in movement in detail. If the athlete is relaxed, the analysis unit can analyze the overall movement pattern. If the athlete is tired, the analysis unit can focus on analyzing the speed and acceleration of movement. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the analysis method to be adjusted based on the athlete's emotions.

[0082] The analysis unit can perform a detailed analysis of the player's movement speed and acceleration during the analysis. For example, the analysis unit can analyze the player's movement speed to evaluate their performance during a match. The analysis unit can analyze the player's movement acceleration to evaluate the intensity and explosiveness of their movements. The analysis unit can combine the player's movement speed and acceleration to evaluate the efficiency of their movements. This allows for a detailed analysis of the player's movement speed and acceleration.

[0083] The analysis unit can cluster the movement patterns of players during analysis and extract features. For example, the analysis unit can cluster the movement patterns of players and extract characteristic movements. The analysis unit can cluster the movement patterns of players and analyze the differences in movements in different matches. The analysis unit can cluster the movement patterns of players and compare them with other players. This allows for the clustering of movement patterns of players and the extraction of features.

[0084] The analysis unit can estimate the athlete's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the athlete is nervous, the analysis unit can display the analysis results in a simple and easy-to-read format. If the athlete is relaxed, the analysis unit can display detailed analysis results. If the athlete is tired, the analysis unit can highlight important points in the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the display method of the analysis results to be adjusted based on the athlete's emotions.

[0085] The analysis unit can analyze player movements in relation to the progress of the game. For example, the analysis unit can analyze player movements in relation to the progress of the game and evaluate the impact of important plays. The analysis unit can analyze player movements in relation to the progress of the game and understand the flow of the game. The analysis unit can analyze player movements in relation to the progress of the game and evaluate the effectiveness of tactics. This allows for the analysis of player movements in relation to the progress of the game.

[0086] The analysis unit can analyze the interaction between a player's movements and the movements of other players during the analysis. For example, the analysis unit can analyze the interaction between a player's movements and the movements of other players to evaluate the effectiveness of team play. The analysis unit can analyze the interaction between a player's movements and the movements of other players to evaluate the success rate of coordinated plays. The analysis unit can analyze the interaction between a player's movements and the movements of other players to evaluate the role of individual players. This allows for the analysis of the interaction between a player's movements and the movements of other players.

[0087] The commentary team can estimate the audience's emotions and adjust their tone based on those estimates. For example, if the audience is excited, the commentary team can use an energetic tone. If the audience is relaxed, the commentary team can use a calm tone. If the audience is tense, the commentary team can use a reassuring tone. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the commentary team to adjust their tone based on the audience's emotions.

[0088] The commentators can highlight important moments in the match during their commentary. For example, they can emphasize goals and scoring moments to convey the excitement to the audience. They can highlight important plays and tactical turning points to convey the flow of the game. They can highlight the individual performances of players to convey their efforts to the audience. This allows them to highlight important moments in the match during their commentary.

[0089] The commentators can enhance their commentary by referring to the players' past performance data. For example, they can refer to a player's past match data and compare it to their current performance. They can refer to a player's past statistics and emphasize their improvement in the current match. They can refer to a player's past playing style and comment on changes in the current match. In this way, they can enhance their commentary by referring to a player's past performance data.

[0090] The commentary team can estimate the audience's emotions and customize the content of the commentary based on those emotions. For example, if the audience is excited, the commentary team can provide energetic commentary. If the audience is relaxed, the commentary team can provide detailed explanations. If the audience is tense, the commentary team can provide reassuring commentary. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the content of the commentary to be customized based on the audience's emotions.

[0091] The commentary team can display match statistics in real time during the broadcast. For example, they can display statistics on goals scored and assists during the match in real time. They can also display player performance data (distance covered, number of shots, etc.) in real time. Furthermore, they can display overall team statistics (ball possession rate, pass completion rate, etc.) in real time. This allows for the display of match statistics in real time.

[0092] The commentators can insert player profile information during live commentary. For example, they can insert basic player information (name, age, position, etc.). They can also insert players' past performance and career history. They can also insert players' personal anecdotes and background information. This allows them to insert player profile information during live commentary.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The sports commentary system may also include a feedback unit that estimates the emotions of the athletes and provides feedback based on those estimated emotions. For example, if an athlete is nervous during a match, the feedback unit can provide advice on how to relax. If an athlete is excited during a match, it can provide advice on how to stay calm. If an athlete is tired during a match, it can provide feedback emphasizing the importance of rest. This allows for the provision of appropriate feedback based on the athletes' emotions.

[0095] The spectator system can further estimate the audience's emotions and customize the viewing experience based on those estimates. For example, if the audience is excited, the system can provide energetic commentary. If the audience is relaxed, it can provide commentary in a calm tone. If the audience is tense, it can provide reassuring commentary. This allows the viewing experience to be customized based on the audience's emotions.

[0096] The event team can further estimate the emotions of event participants and adjust the event's progress based on those estimates. For example, if participants are excited, the event team can speed up the pace. If participants are relaxed, they can provide a more relaxed pace. If participants are nervous, they can provide a reassuring pace. This allows the event's progress to be adjusted based on the participants' emotions.

[0097] The tracking unit can further improve tracking accuracy by integrating data from wearable devices worn by the athlete. For example, the tracking unit can improve tracking accuracy by integrating data from a heart rate monitor worn by the athlete. It can improve tracking accuracy by integrating data from an accelerometer worn by the athlete to capture detailed movement information. It can improve the accuracy of location information by integrating data from a GPS device worn by the athlete. In this way, tracking accuracy is improved by integrating data from wearable devices worn by the athlete.

[0098] The analysis unit can further cluster the players' movement patterns and extract features. For example, the analysis unit can cluster the players' movement patterns and extract characteristic movements. By clustering the players' movement patterns, it can analyze the differences in movements across different matches. By clustering the players' movement patterns, it can compare them with those of other players. This allows for the clustering of players' movement patterns and the extraction of features.

[0099] The commentators can further estimate the audience's emotions and adjust their tone of commentary based on those estimates. For example, if the audience is excited, they can use an energetic tone. If the audience is relaxed, they can use a calm tone. If the audience is tense, they can use a reassuring tone. This allows them to adjust the tone of commentary based on the audience's emotions.

[0100] The generation unit can further adjust the content of match highlights by taking into account the players' emotions when automatically generating them. For example, if a player is excited, the generation unit can generate highlights that focus on energetic plays. If a player is relaxed, the highlights can focus on calm plays. If a player is nervous, the highlights can focus on tense plays. In this way, the content of the highlights can be adjusted based on the players' emotions.

[0101] The system can further estimate the athlete's emotions and adjust the content of the feedback based on those estimates. For example, if an athlete is nervous, the system can provide advice on how to relax. If an athlete is excited, it can provide advice on how to stay calm. If an athlete is tired, it can provide feedback emphasizing the importance of rest. This allows the system to provide appropriate feedback based on the athlete's emotions.

[0102] The tracking unit can further learn the player's past movement patterns and optimize tracking based on predictions. For example, the tracking unit can analyze the player's past match data, learn specific movement patterns, and track while predicting the next movement. Based on the player's training data, it can understand movement tendencies and predict and track movements during matches. It can cluster the player's past movement patterns and predict and track similar movements. This allows the unit to learn the player's past movement patterns and optimize tracking based on predictions.

[0103] The analysis unit can further analyze the relationship between player movements and the progress of the match. For example, the analysis unit can analyze player movements in relation to the progress of the match and evaluate the impact of important plays. By analyzing player movements in relation to the progress of the match, it is possible to grasp the flow of the match. By analyzing player movements in relation to the progress of the match, it is possible to evaluate the effectiveness of tactics. In this way, it is possible to analyze player movements in relation to the progress of the match.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The tracking unit tracks the players' movements. For example, it uses an AI camera to track players' movements in real time, with the camera automatically following the players as they move around the field and recording their movements. Step 2: The analysis unit analyzes the movements tracked by the tracking unit. For example, it uses AI to analyze the players' movements in detail, analyzes the patterns of the players' movements, and evaluates the progress of the match and the players' performance. Step 3: The commentary unit generates commentary based on the movements analyzed by the analysis unit. For example, it uses AI to generate commentary according to the progress of the match and the movements of the players, and provides detailed commentary when a player scores a goal or when an important play occurs. Step 4: The generation unit generates highlights based on the commentary generated by the commentary unit. For example, it uses AI to automatically generate match highlights and, after the match ends, generates a highlight video focusing on the players' plays. Step 5: The providing unit provides feedback based on the highlights and analysis results generated by the generating unit. For example, it uses AI to analyze the players' skills and play, providing feedback that helps improve their technique, and offering suggestions for improving the players' movements and advice for the next match.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the tracking unit, analysis unit, commentary unit, generation unit, provision unit, viewing unit, and event unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the tracking unit tracks the movements of players in real time using the camera 42 of the smart device 14. The analysis unit analyzes the movements of players in detail using the specific processing unit 290 of the data processing unit 12. The commentary unit generates commentary according to the progress of the match and the movements of the players using the specific processing unit 290 of the data processing unit 12. The generation unit automatically generates match highlights using the specific processing unit 290 of the data processing unit 12. The provision unit analyzes the skills and play of players using the specific processing unit 290 of the data processing unit 12 and provides feedback that helps improve skills. The viewing unit enables families to watch the match at home using the output device 40 of the smart device 14. The event unit supports use at school events and community events using the communication I / F 44 of the smart device 14. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the tracking unit, analysis unit, commentary unit, generation unit, provision unit, viewing unit, and event unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the tracking unit tracks the movements of players in real time using the camera 42 of the smart glasses 214. The analysis unit analyzes the movements of players in detail using the specific processing unit 290 of the data processing unit 12. The commentary unit generates commentary according to the progress of the match and the movements of players using the specific processing unit 290 of the data processing unit 12. The generation unit automatically generates match highlights using the specific processing unit 290 of the data processing unit 12. The provision unit analyzes the skills and play of players using the specific processing unit 290 of the data processing unit 12 and provides feedback that helps improve skills. The viewing unit enables families to watch the match at home using the speaker 240 of the smart glasses 214. The event unit supports use at school events and community events using the communication I / F 44 of the smart glasses 214. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the tracking unit, analysis unit, commentary unit, generation unit, provision unit, viewing unit, and event unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the tracking unit tracks the movements of players in real time using the camera 42 of the headset terminal 314. The analysis unit analyzes the movements of players in detail using the specific processing unit 290 of the data processing unit 12. The commentary unit generates commentary according to the progress of the match and the movements of the players using the specific processing unit 290 of the data processing unit 12. The generation unit automatically generates match highlights using the specific processing unit 290 of the data processing unit 12. The provision unit analyzes the skills and play of players using the specific processing unit 290 of the data processing unit 12 and provides feedback that helps improve skills. The viewing unit allows families to watch the match at home using the display 343 of the headset terminal 314. The event unit supports use at school events and community events using the communication I / F 44 of the headset terminal 314. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the tracking unit, analysis unit, commentary unit, generation unit, provision unit, viewing unit, and event unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the tracking unit tracks the movements of the players in real time using the camera 42 of the robot 414. The analysis unit analyzes the movements of the players in detail using the specific processing unit 290 of the data processing unit 12. The commentary unit generates commentary according to the progress of the match and the movements of the players using the specific processing unit 290 of the data processing unit 12. The generation unit automatically generates match highlights using the specific processing unit 290 of the data processing unit 12. The provision unit analyzes the skills and play of the players using the specific processing unit 290 of the data processing unit 12 and provides feedback that helps improve their skills. The viewing unit allows families to watch the match at home using the speaker 240 of the robot 414. The event unit supports use at school events and community events using the communication I / F 44 of the robot 414. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) Tracking unit that tracks the player's movements, An analysis unit analyzes the movement tracked by the aforementioned tracking unit, A commentary unit that generates commentary based on the motion analyzed by the aforementioned analysis unit, A generation unit that generates highlights based on the commentary generated by the commentary unit, The system includes a providing unit that provides feedback based on the highlights and analysis results generated by the generation unit. A system characterized by the following features. (Note 2) It includes a viewing area for families to watch the game at home using their smartphones and tablets. The system described in Appendix 1, characterized by the features described herein. (Note 3) We have an events department to support use for school events and community events. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned tracking unit is Track the players' movements in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze the players' movements in detail. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned commentary unit, The system generates commentary that matches the progress of the match and the players' movements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is Automatically generate match highlights. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, Analyze skills and gameplay and provide feedback. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned tracking unit is The system estimates the players' emotions and adjusts the tracking accuracy based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned tracking unit is Learn the player's past movement patterns and optimize tracking based on predictions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned tracking unit is During tracking, the tracking method is adjusted taking into account the athlete's physical condition and fatigue level. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned tracking unit is The system estimates the players' emotions and determines tracking priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned tracking unit is During tracking, the player's location information is recorded as their relative position to other players or objects. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned tracking unit is During tracking, data from wearable devices worn by athletes is integrated to improve tracking accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the players' emotions and adjusts the analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the speed and acceleration of the players' movements are analyzed in detail. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the patterns of the players' movements are clustered to extract features. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the players' emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the movements of the players are correlated with the progress of the match. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During the analysis, the interaction between the movements of one player and the movements of other players is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned commentary unit, The system estimates the audience's emotions and adjusts the tone of the commentary based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned commentary unit, During live commentary, emphasize important moments in the game. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned commentary unit, During live commentary, the commentary will be enhanced by referencing past performance data of the players. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned commentary unit, It estimates the audience's emotions and customizes the commentary based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned commentary unit, During live commentary, match statistics will be displayed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned commentary unit, During live commentary, player profile information will be inserted into the commentary. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0178] 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. Tracking unit that tracks the player's movements, An analysis unit analyzes the movement tracked by the aforementioned tracking unit, A commentary unit that generates commentary based on the motion analyzed by the aforementioned analysis unit, A generation unit that generates highlights based on the commentary generated by the commentary unit, The system includes a providing unit that provides feedback based on the highlights and analysis results generated by the generation unit. A system characterized by the following features.

2. It includes a viewing area for families to watch the game at home using their smartphones and tablets. The system according to feature 1.

3. We have an events department to support use for school events and community events. The system according to feature 1.

4. The aforementioned tracking unit is Track the players' movements in real time. The system according to feature 1.

5. The aforementioned analysis unit, Analyze the players' movements in detail. The system according to feature 1.

6. The aforementioned commentary unit, The system generates commentary that matches the progress of the match and the players' movements. The system according to feature 1.

7. The generating unit is Automatically generate match highlights. The system according to feature 1.

8. The aforementioned supply unit is, Analyze skills and gameplay and provide feedback. The system according to feature 1.

Images