System

Abstract

An object of the system according to the embodiment is to efficiently extract an excellent play of a player from a game video and create a highlight reel.SOLUTION: A system according to an embodiment includes a video analysis unit, a performance evaluation unit, a play extraction unit, and a highlight editing unit. The video analysis unit analyzes the game video. The performance evaluation unit evaluates movements and technical performances of the players based on the game video analyzed by the video analysis unit. The play extractor extracts an excellent play evaluated by the performance evaluator. The highlight editing unit edits a highlight reel by connecting the excellent plays extracted by the play extracting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to efficiently extract players' best plays from game footage and create highlight reels.

[0005] The system according to the embodiment aims to efficiently extract excellent plays by players from game footage and create a highlight reel. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a performance evaluation unit, a play extraction unit, and a highlight editing unit. The video analysis unit analyzes game video. The performance evaluation unit evaluates player movements and technical performance based on the game video analyzed by the video analysis unit. The play extraction unit extracts excellent plays evaluated by the performance evaluation unit. The highlight editing unit compiles a highlight reel by connecting the excellent plays extracted by the play extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently extract excellent plays by players from game footage and create a highlight reel. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A video editing system according to an embodiment of the present invention is a system that automatically evaluates player performances from game footage and compiles and provides a collection of clips of good plays. This allows the video editing system to automatically extract excellent plays from game footage and edit them into a highlight reel.

[0029] A video editing system according to an embodiment includes a video analysis unit, a performance evaluation unit, a play extraction unit, and a highlight editing unit. The video analysis unit analyzes game footage. For example, the video analysis unit analyzes game footage using image recognition technology. The video analysis unit can also analyze player movements using a motion analysis algorithm. The video analysis unit can also extract specific scenes from game footage. For example, the video analysis unit extracts scenes of players dribbling and passing from soccer game footage. The performance evaluation unit evaluates player movements and technical performance based on the game footage analyzed by the video analysis unit. For example, the performance evaluation unit evaluates the speed and acceleration of players' movements. The performance evaluation unit can also evaluate players' technical performance. The performance evaluation unit can also analyze players' movement patterns. For example, the performance evaluation unit evaluates players' shooting accuracy and pass success rate from soccer game footage. The play extraction unit extracts excellent plays evaluated by the performance evaluation unit. For example, the play extraction unit extracts scoring scenes and important defensive plays. The play extraction unit can also extract excellent technical plays by players. The play extraction unit can also extract plays taking into account the flow of the game. For example, the play extraction unit extracts scenes of consecutive passes and shots from soccer game footage. The highlight editing unit edits a highlight reel by connecting the excellent plays extracted by the play extraction unit. For example, the highlight editing unit performs editing taking into account the order of plays. The highlight editing unit can also adjust the length of the edits. The highlight editing unit can also add visual effects. For example, the highlight editing unit adds a slow-motion effect to goal scenes in soccer game footage. In this way, the video editing system according to the embodiment can automatically evaluate player performance from game footage and compile good plays into a collection of clips to provide. For example, coaches can use highlight reels as a feedback tool for player growth and skill improvement, and fans can enjoy watching players' excellent plays.Sports media can also distribute highlight reels as content that captivates viewers.

[0030] The performance evaluation unit analyzes subtle changes in a player's movements and body balance, allowing for a more detailed evaluation of the quality of their performance. The performance evaluation unit, for example, uses a generative AI to analyze subtle changes in a player's movements. For example, the performance evaluation unit analyzes in detail the foot movements and body balance of a soccer player when dribbling to evaluate the technical merits and demerits of the player. The performance evaluation unit also uses a generative AI to analyze the balance of a player's body to evaluate the quality of their performance. For example, the performance evaluation unit analyzes the tilt of a basketball player's body and the position of their center of gravity when shooting to evaluate the success rate of the shot. The performance evaluation unit also uses a generative AI to analyze subtle changes in a player's movements and evaluate the quality of their performance. For example, the performance evaluation unit analyzes in detail the arm movements and body balance of a tennis player when serving to evaluate the accuracy of the serve. This allows for a detailed evaluation of subtle changes in a player's movements and body balance.

[0031] The performance evaluation unit can compare the data with the player's past performance data and automatically identify areas for growth and improvement. In the performance evaluation unit, for example, the generation AI compares the player's past performance data with their current performance and identifies areas for growth and improvement. For example, a soccer player's past dribbling data is compared with their current data to evaluate their improvement in technique. In addition, the performance evaluation unit can analyze the player's past performance data and identify areas for growth and improvement. For example, a basketball player's past shooting success rate is compared with their current success rate to evaluate their improvement in technique. In addition, the performance evaluation unit can compare the player's past performance data with their current data and identify areas for growth and improvement. For example, a tennis player's past serving success rate is compared with their current serving success rate to evaluate their improvement in technique. This makes it possible to automatically identify a player's growth and areas for improvement.

[0032] The performance evaluation unit can analyze game footage of different sports and evaluate the technical similarities and differences between the sports. For example, the performance evaluation unit uses generative AI to analyze game footage of soccer and basketball and evaluate the technical similarities and differences. For example, it analyzes the differences in dribbling techniques and shooting techniques. The performance evaluation unit also uses generative AI to analyze game footage of tennis and badminton and evaluate the technical similarities and differences. For example, it analyzes the differences in serving techniques and rally techniques. The performance evaluation unit also uses generative AI to analyze game footage of baseball and cricket and evaluate the technical similarities and differences. For example, it analyzes the differences in batting techniques and pitching techniques. This makes it possible to evaluate the technical similarities and differences between different sports.

[0033] When analyzing a player's performance, the performance evaluation unit can analyze the audience's reactions and cheers, and evaluate the correlation between the audience's emotions and the player's performance. In the performance evaluation unit, for example, the generation AI analyzes the audience's reactions and cheers, and evaluates the correlation between the audience's emotions and the player's performance. For example, in a soccer game, the performance evaluation unit analyzes the impact of the audience's cheers on the player's performance. In addition, the performance evaluation unit analyzes the audience's reactions and cheers, and evaluates the correlation between the audience's emotions and the player's performance. For example, in a basketball game, the performance evaluation unit analyzes the impact of the audience's reactions on the player's shooting success rate. In addition, the performance evaluation unit analyzes the audience's reactions and cheers, and evaluates the correlation between the audience's emotions and the player's performance. For example, in a tennis game, the performance evaluation unit analyzes the impact of the audience's reactions on the player's serve success rate. This makes it possible to evaluate the correlation between the audience's emotions and the player's performance.

[0034] The play extraction unit evaluates the continuity and consistency of a player's movements and is able to extract particularly excellent consecutive plays. The play extraction unit, for example, uses a generation AI to evaluate the continuity and consistency of a player's movements and extracts excellent consecutive plays. For example, consecutive dribbling and passing plays are extracted in a soccer game. The play extraction unit also uses a generation AI to evaluate the continuity and consistency of a player's movements and extracts excellent consecutive plays. For example, consecutive dribbling and shooting plays are extracted in a basketball game. The play extraction unit also uses a generation AI to evaluate the continuity and consistency of a player's movements and extracts excellent consecutive plays. For example, consecutive rallies and serving plays are extracted in a tennis game. This makes it possible to extract excellent consecutive plays.

[0035] The highlight editing unit can integrate footage from different games and seasons to create a highlight reel that shows the long-term growth and evolution of a player. For example, the highlight editing unit uses generative AI to integrate footage from different games and seasons to create a highlight reel that shows the long-term growth of a player. For example, it compiles a soccer player's goal scenes for each season. The highlight editing unit also uses generative AI to integrate footage from different games and seasons to create a highlight reel that shows the long-term growth of a player. For example, it compiles a basketball player's shooting scenes for each season. The highlight editing unit also uses generative AI to integrate footage from different games and seasons to create a highlight reel that shows the long-term growth of a player. For example, it compiles a tennis player's serving scenes for each season. In this way, it is possible to create a highlight reel that shows the long-term growth and evolution of a player.

[0036] When analyzing player performance, the highlight editing unit can evaluate the team's teamwork and create a highlight reel of the team's play. In the highlight editing unit, for example, the generation AI analyzes the team's teamwork and creates a highlight reel. For example, in a soccer game, the passing and teamwork of the entire team is evaluated and reflected in the highlight reel. In addition, in the highlight editing unit, the generation AI analyzes the team's teamwork and creates a highlight reel. For example, in a basketball game, the team's defensive and offensive teamwork is evaluated and reflected in the highlight reel. In addition, in the highlight editing unit, the generation AI analyzes the team's teamwork and creates a highlight reel. For example, in a tennis doubles game, the pair's teamwork is evaluated and reflected in the highlight reel. In this way, the team's teamwork can be evaluated and a highlight reel of the team's play can be created.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The video editing system can also be equipped with a health management unit that monitors the health of players. The health management unit collects biometric data, such as a player's heart rate and oxygen saturation, in real time and evaluates the player's physical condition in cooperation with the performance evaluation unit. For example, it can monitor a soccer player's heart rate during a match and recommend rest if the player is under excessive strain. The health management unit can also analyze a player's fatigue level and propose an appropriate training plan. For example, it can evaluate a basketball player's fatigue level after a match and provide a recovery plan. Furthermore, the health management unit can monitor a player's nutritional status and suggest the timing of nutritional supplementation. For example, it can evaluate a tennis player's nutritional status before and after a match and recommend appropriate nutritional supplementation. This allows for comprehensive management of the player's health and supports performance improvement.

[0039] The video editing system can further include a tactical evaluation unit that evaluates a player's level of tactical understanding. The tactical evaluation unit analyzes the player's movements during a game to evaluate the player's level of tactical understanding. For example, in a soccer game, it evaluates whether a player is moving according to the tactics instructed. The tactical evaluation unit can also analyze the player's positioning and movement coordination to evaluate the degree of tactical execution. For example, in a basketball game, it evaluates whether a player is moving according to the team's tactics. Furthermore, the tactical evaluation unit can propose a training plan based on the player's level of tactical understanding. For example, in a tennis doubles game, it evaluates the coordination between pairs and suggests areas for improvement in tactics. This allows for a comprehensive evaluation of a player's level of tactical understanding and improves team performance.

[0040] The video editing system can further include a prediction unit that predicts a player's performance. The prediction unit predicts a player's future performance based on past performance data. For example, it analyzes a soccer player's past match data to predict his or her performance in the next match. The prediction unit can also predict future performance based on a player's training data. For example, it analyzes a basketball player's training data to predict his or her shooting success rate in the next match. The prediction unit can also predict future performance based on a player's health data. For example, it analyzes a tennis player's health data to predict his or her performance in the next match. This makes it possible to predict a player's future performance and use it to improve training and tactics.

[0041] The video editing system may further include a comparison unit that compares a player's performance with other players. The comparison unit compares the player's performance data with the data of other players to make a relative evaluation. For example, the dribbling technique of a soccer player may be compared with that of other players to evaluate the superiority or inferiority of the technique. The comparison unit may also compare the player's training data with the data of other players to evaluate the effectiveness of the training. For example, the shooting success rate of a basketball player may be compared with that of other players to evaluate the effectiveness of the training. The comparison unit may also compare the player's health data with that of other players to make a relative evaluation of the health condition. For example, the heart rate of a tennis player may be compared with that of other players to evaluate the health condition. In this way, the player's performance can be compared with that of other players to make a relative evaluation.

[0042] The video editing system can further include a strategy unit for improving player performance. The strategy unit analyzes game data and proposes specific strategies to players. For example, in a soccer game, the strategy unit analyzes the weaknesses of the opposing team and proposes an attacking strategy. The strategy unit can also propose tactics during a game based on player performance data. For example, in a basketball game, the strategy unit proposes effective attacking tactics based on the player's shooting success rate. Furthermore, the strategy unit can propose strategies that take into account player health data. For example, in a tennis game, the strategy unit suggests rest times during the game based on the player's fatigue level. This provides specific strategies for improving player performance and contributes to winning the game.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The video analysis unit analyzes the game video. For example, the video analysis unit uses image recognition technology to analyze the game video and motion analysis algorithms to analyze the movements of the players. It can also extract specific scenes from the game video. For example, in soccer game video, it can extract scenes of players dribbling and passing. Step 2: The performance evaluation unit evaluates the player's movements and technical performance based on the match footage analyzed by the video analysis unit, such as the speed and acceleration of the player's movements, the accuracy of their shots, and the success rate of their passes. Step 3: The play extraction unit extracts outstanding plays evaluated by the performance evaluation unit, such as scoring scenes, important defensive plays, and consecutive passing and shooting scenes. Step 4: The highlight editor compiles the outstanding plays extracted by the play extractor into a highlight reel. For example, the editor can edit the order of the plays, adjust the length of the edits, and add visual effects, such as adding slow-motion effects to goal scenes.

[0045] (Example 2) A video editing system according to an embodiment of the present invention is a system that automatically evaluates player performances from game footage and compiles and provides a collection of clips of good plays. This allows the video editing system to automatically extract excellent plays from game footage and edit them into a highlight reel.

[0046] A video editing system according to an embodiment includes a video analysis unit, a performance evaluation unit, a play extraction unit, and a highlight editing unit. The video analysis unit analyzes game footage. For example, the video analysis unit analyzes game footage using image recognition technology. The video analysis unit can also analyze player movements using a motion analysis algorithm. The video analysis unit can also extract specific scenes from game footage. For example, the video analysis unit extracts scenes of players dribbling and passing from soccer game footage. The performance evaluation unit evaluates player movements and technical performance based on the game footage analyzed by the video analysis unit. For example, the performance evaluation unit evaluates the speed and acceleration of players' movements. The performance evaluation unit can also evaluate players' technical performance. The performance evaluation unit can also analyze players' movement patterns. For example, the performance evaluation unit evaluates players' shooting accuracy and pass success rate from soccer game footage. The play extraction unit extracts excellent plays evaluated by the performance evaluation unit. For example, the play extraction unit extracts scoring scenes and important defensive plays. The play extraction unit can also extract excellent technical plays by players. The play extraction unit can also extract plays taking into account the flow of the game. For example, the play extraction unit extracts scenes of consecutive passes and shots from soccer game footage. The highlight editing unit edits a highlight reel by connecting the excellent plays extracted by the play extraction unit. For example, the highlight editing unit performs editing taking into account the order of plays. The highlight editing unit can also adjust the length of the edits. The highlight editing unit can also add visual effects. For example, the highlight editing unit adds a slow-motion effect to goal scenes in soccer game footage. In this way, the video editing system according to the embodiment can automatically evaluate player performance from game footage and compile good plays into a collection of clips to provide. For example, coaches can use highlight reels as a feedback tool for player growth and skill improvement, and fans can enjoy watching players' excellent plays.Sports media can also distribute highlight reels as content that captivates viewers.

[0047] The performance evaluation unit analyzes subtle changes in a player's movements and body balance, allowing for a more detailed evaluation of the quality of their performance. The performance evaluation unit, for example, uses a generative AI to analyze subtle changes in a player's movements. For example, the performance evaluation unit analyzes in detail the foot movements and body balance of a soccer player when dribbling to evaluate the technical merits and demerits of the player. The performance evaluation unit also uses a generative AI to analyze the balance of a player's body to evaluate the quality of their performance. For example, the performance evaluation unit analyzes the tilt of a basketball player's body and the position of their center of gravity when shooting to evaluate the success rate of the shot. The performance evaluation unit also uses a generative AI to analyze subtle changes in a player's movements and evaluate the quality of their performance. For example, the performance evaluation unit analyzes in detail the arm movements and body balance of a tennis player when serving to evaluate the accuracy of the serve. This allows for a detailed evaluation of subtle changes in a player's movements and body balance.

[0048] The performance evaluation unit can compare the data with the player's past performance data and automatically identify areas for growth and improvement. In the performance evaluation unit, for example, the generation AI compares the player's past performance data with their current performance and identifies areas for growth and improvement. For example, a soccer player's past dribbling data is compared with their current data to evaluate their improvement in technique. In addition, the performance evaluation unit can analyze the player's past performance data and identify areas for growth and improvement. For example, a basketball player's past shooting success rate is compared with their current success rate to evaluate their improvement in technique. In addition, the performance evaluation unit can compare the player's past performance data with their current data and identify areas for growth and improvement. For example, a tennis player's past serving success rate is compared with their current serving success rate to evaluate their improvement in technique. This makes it possible to automatically identify a player's growth and areas for improvement.

[0049] The performance evaluation unit can use the emotion estimation function to estimate emotions from the player's facial expressions and body movements, and evaluate the impact of the emotional state on performance. For example, the performance evaluation unit uses the emotion estimation function to estimate emotions from the player's facial expressions and body movements, and evaluate the impact of the emotional state on performance. For example, the performance evaluation unit analyzes the facial expression of a soccer player after scoring a goal, and evaluates the impact of emotional excitement on the next play. The performance evaluation unit also uses the emotion estimation function to estimate emotions from the player's facial expressions and body movements, and evaluate the impact of the emotional state on performance. For example, the performance evaluation unit analyzes the facial expression of a basketball player when taking a free throw, and evaluates the impact of nervousness on the success rate. The performance evaluation unit also uses the emotion estimation function to estimate emotions from the player's facial expressions and body movements, and evaluate the impact of the emotional state on performance. For example, the performance evaluation unit analyzes the facial expression of a tennis player during a match, and evaluates the impact of stress on play. This makes it possible to evaluate the impact of the player's emotional state on performance.

[0050] The performance evaluation unit can analyze game footage of different sports and evaluate the technical similarities and differences between the sports. For example, the performance evaluation unit uses generative AI to analyze game footage of soccer and basketball and evaluate the technical similarities and differences. For example, it analyzes the differences in dribbling techniques and shooting techniques. The performance evaluation unit also uses generative AI to analyze game footage of tennis and badminton and evaluate the technical similarities and differences. For example, it analyzes the differences in serving techniques and rally techniques. The performance evaluation unit also uses generative AI to analyze game footage of baseball and cricket and evaluate the technical similarities and differences. For example, it analyzes the differences in batting techniques and pitching techniques. This makes it possible to evaluate the technical similarities and differences between different sports.

[0051] When analyzing a player's performance, the performance evaluation unit can analyze the audience's reactions and cheers, and evaluate the correlation between the audience's emotions and the player's performance. In the performance evaluation unit, for example, the generation AI analyzes the audience's reactions and cheers, and evaluates the correlation between the audience's emotions and the player's performance. For example, in a soccer game, the performance evaluation unit analyzes the impact of the audience's cheers on the player's performance. In addition, the performance evaluation unit analyzes the audience's reactions and cheers, and evaluates the correlation between the audience's emotions and the player's performance. For example, in a basketball game, the performance evaluation unit analyzes the impact of the audience's reactions on the player's shooting success rate. In addition, the performance evaluation unit analyzes the audience's reactions and cheers, and evaluates the correlation between the audience's emotions and the player's performance. For example, in a tennis game, the performance evaluation unit analyzes the impact of the audience's reactions on the player's serve success rate. This makes it possible to evaluate the correlation between the audience's emotions and the player's performance.

[0052] The performance evaluation unit can use the emotion estimation function to analyze the emotional reactions of spectators, identify the plays that excite spectators most, and reflect the plays in a highlight reel. The performance evaluation unit, for example, uses the emotion estimation function to analyze the emotional reactions of spectators and identify the most exciting plays. For example, it identifies the goal scene that excites spectators most in a soccer game and reflects it in a highlight reel. The performance evaluation unit can also use the emotion estimation function to analyze the emotional reactions of spectators and identify the most exciting plays. For example, it identifies the dunk shot that excites spectators most in a basketball game and reflects it in a highlight reel. The performance evaluation unit can also use the emotion estimation function to analyze the emotional reactions of spectators and identify the most exciting plays. For example, it identifies the rally scene that excites spectators most in a tennis game and reflects it in a highlight reel. In this way, it is possible to identify the plays that excite spectators most and reflect it in a highlight reel.

[0053] The play extraction unit evaluates the continuity and consistency of a player's movements and is able to extract particularly excellent consecutive plays. The play extraction unit, for example, uses a generation AI to evaluate the continuity and consistency of a player's movements and extracts excellent consecutive plays. For example, consecutive dribbling and passing plays are extracted in a soccer game. The play extraction unit also uses a generation AI to evaluate the continuity and consistency of a player's movements and extracts excellent consecutive plays. For example, consecutive dribbling and shooting plays are extracted in a basketball game. The play extraction unit also uses a generation AI to evaluate the continuity and consistency of a player's movements and extracts excellent consecutive plays. For example, consecutive rallies and serving plays are extracted in a tennis game. This makes it possible to extract excellent consecutive plays.

[0054] The play extraction unit can use the emotion estimation function to identify the moment when a player's emotional state is at its highest and reflect that moment in a highlight reel. The play extraction unit, for example, uses the emotion estimation function to identify the moment when a player's emotional state is at its highest and reflect that moment in a highlight reel. For example, the play extraction unit identifies the moment when a player scores a goal in a soccer game and reflects that moment in a highlight reel. The play extraction unit also uses the emotion estimation function to identify the moment when a player's emotional state is at its highest and reflects that moment in a highlight reel. For example, the play extraction unit identifies the moment when a player makes a dunk shot in a basketball game and reflects that moment in a highlight reel. The play extraction unit also uses the emotion estimation function to identify the moment when a player's emotional state is at its highest and reflects that moment in a highlight reel. For example, the play extraction unit identifies the moment when a player scores a goal in a tennis game and reflects that moment in a highlight reel. In this way, the moment when a player's emotional state is at its highest can be reflected in a highlight reel.

[0055] The highlight editing unit can integrate footage from different games and seasons to create a highlight reel that shows the long-term growth and evolution of a player. For example, the highlight editing unit uses generative AI to integrate footage from different games and seasons to create a highlight reel that shows the long-term growth of a player. For example, it compiles a soccer player's goal scenes for each season. The highlight editing unit also uses generative AI to integrate footage from different games and seasons to create a highlight reel that shows the long-term growth of a player. For example, it compiles a basketball player's shooting scenes for each season. The highlight editing unit also uses generative AI to integrate footage from different games and seasons to create a highlight reel that shows the long-term growth of a player. For example, it compiles a tennis player's serving scenes for each season. In this way, it is possible to create a highlight reel that shows the long-term growth and evolution of a player.

[0056] When analyzing player performance, the highlight editing unit can evaluate the team's teamwork and create a highlight reel of the team's play. In the highlight editing unit, for example, the generation AI analyzes the team's teamwork and creates a highlight reel. For example, in a soccer game, the passing and teamwork of the entire team is evaluated and reflected in the highlight reel. In addition, in the highlight editing unit, the generation AI analyzes the team's teamwork and creates a highlight reel. For example, in a basketball game, the team's defensive and offensive teamwork is evaluated and reflected in the highlight reel. In addition, in the highlight editing unit, the generation AI analyzes the team's teamwork and creates a highlight reel. For example, in a tennis doubles game, the pair's teamwork is evaluated and reflected in the highlight reel. In this way, the team's teamwork can be evaluated and a highlight reel of the team's play can be created.

[0057] The highlight editing unit can use the emotion estimation function to analyze the emotional reactions of fans, identify the plays that move fans the most, and reflect those plays in the highlight reel. The highlight editing unit, for example, uses the emotion estimation function to analyze the emotional reactions of fans and identify the plays that move fans the most. For example, it identifies the goal scene that moves fans the most in a soccer game and reflects that in the highlight reel. The highlight editing unit can also use the emotion estimation function to analyze the emotional reactions of fans and identify the plays that move fans the most. For example, it identifies the dunk shot that moves fans the most in a basketball game and reflects that in the highlight reel. The highlight editing unit can also use the emotion estimation function to analyze the emotional reactions of fans and identify the plays that move fans the most. For example, it identifies the rally scene that moves fans the most in a tennis game and reflects that in the highlight reel. In this way, it is possible to identify the plays that move fans the most and reflect that in the highlight reel.

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

[0059] The video editing system can also be equipped with a health management unit that monitors the health of players. The health management unit collects biometric data, such as a player's heart rate and oxygen saturation, in real time and evaluates the player's physical condition in cooperation with the performance evaluation unit. For example, it can monitor a soccer player's heart rate during a match and recommend rest if the player is under excessive strain. The health management unit can also analyze a player's fatigue level and propose an appropriate training plan. For example, it can evaluate a basketball player's fatigue level after a match and provide a recovery plan. Furthermore, the health management unit can monitor a player's nutritional status and suggest the timing of nutritional supplementation. For example, it can evaluate a tennis player's nutritional status before and after a match and recommend appropriate nutritional supplementation. This allows for comprehensive management of the player's health and supports performance improvement.

[0060] The video editing system can further include an audience analysis unit that analyzes audience reactions in real time. The audience analysis unit analyzes the volume of audience cheers and applause during a game to evaluate the excitement of the game. For example, it identifies the moment when the audience cheers are loudest in a soccer game and reflects that scene in a highlight reel. The audience analysis unit can also analyze audience facial expressions to evaluate the intensity of emotions. For example, it identifies the moment when the audience is most excited in a basketball game and reflects that scene in a highlight reel. The audience analysis unit can also analyze the flow of a game based on audience reactions and identify important scenes. For example, it identifies the rally scene in a tennis game that elicits the greatest audience reaction and reflects that scene in a highlight reel. This allows the excitement of the game to be evaluated based on audience reactions, making it possible to create a more compelling highlight reel.

[0061] The video editing system can further include a psychology analysis unit that analyzes the psychological state of a player. The psychology analysis unit estimates the player's psychological state from their facial expressions and movements, and works with the performance evaluation unit to evaluate the player's mental state. For example, it can analyze a soccer player's facial expressions during a game to evaluate the level of tension and stress. The psychology analysis unit can also analyze interview footage of a player to evaluate their psychological state. For example, it can analyze interview footage of a basketball player after a game to evaluate their psychological state during the game. Furthermore, the psychology analysis unit can propose a training plan based on the player's psychological state. For example, it can evaluate a tennis player's psychological state before a game and suggest training to help them relax. This allows for comprehensive management of the player's psychological state and support for improving their performance.

[0062] The video editing system can further include a tactical evaluation unit that evaluates a player's level of tactical understanding. The tactical evaluation unit analyzes the player's movements during a game to evaluate the player's level of tactical understanding. For example, in a soccer game, it evaluates whether a player is moving according to the tactics instructed. The tactical evaluation unit can also analyze the player's positioning and movement coordination to evaluate the degree of tactical execution. For example, in a basketball game, it evaluates whether a player is moving according to the team's tactics. Furthermore, the tactical evaluation unit can propose a training plan based on the player's level of tactical understanding. For example, in a tennis doubles game, it evaluates the coordination between pairs and suggests areas for improvement in tactics. This allows for a comprehensive evaluation of a player's level of tactical understanding and improves team performance.

[0063] The video editing system can further include a prediction unit that predicts a player's performance. The prediction unit predicts a player's future performance based on past performance data. For example, it analyzes a soccer player's past match data to predict his or her performance in the next match. The prediction unit can also predict future performance based on a player's training data. For example, it analyzes a basketball player's training data to predict his or her shooting success rate in the next match. The prediction unit can also predict future performance based on a player's health data. For example, it analyzes a tennis player's health data to predict his or her performance in the next match. This makes it possible to predict a player's future performance and use it to improve training and tactics.

[0064] The video editing system can further include a feedback unit for improving a player's performance. The feedback unit provides specific feedback to the player based on the evaluation results of the performance evaluation unit. For example, the feedback unit can provide a soccer player with specific advice on improving dribbling technique. The feedback unit can also suggest a training plan for the player. For example, the feedback unit can suggest a training plan for a basketball player to improve their shooting success rate. The feedback unit can also provide feedback that takes into account the player's psychological state. For example, the feedback unit can suggest mental training for a tennis player to relieve tension before a match. This allows the player to receive specific feedback to improve their performance, which can be used in training and match preparation.

[0065] The video editing system may further include a comparison unit that compares a player's performance with other players. The comparison unit compares the player's performance data with the data of other players to make a relative evaluation. For example, the dribbling technique of a soccer player may be compared with that of other players to evaluate the superiority or inferiority of the technique. The comparison unit may also compare the player's training data with the data of other players to evaluate the effectiveness of the training. For example, the shooting success rate of a basketball player may be compared with that of other players to evaluate the effectiveness of the training. The comparison unit may also compare the player's health data with that of other players to make a relative evaluation of the health condition. For example, the heart rate of a tennis player may be compared with that of other players to evaluate the health condition. In this way, the player's performance can be compared with that of other players to make a relative evaluation.

[0066] The video editing system can further include a motivation unit for improving a player's performance. The motivation unit analyzes the player's emotional state and provides feedback to increase motivation. For example, it can provide a soccer player with a message to increase motivation before a match. The motivation unit can also suggest goal setting to increase motivation based on the player's performance data. For example, it can set a shooting success rate target for a basketball player in the next match. Furthermore, the motivation unit can suggest training plans that take the player's emotional state into consideration. For example, it can suggest mental training to a tennis player to relieve tension before a match. This can increase the player's motivation and support improved performance.

[0067] The video editing system can further include a strategy unit for improving player performance. The strategy unit analyzes game data and proposes specific strategies to players. For example, in a soccer game, the strategy unit analyzes the weaknesses of the opposing team and proposes an attacking strategy. The strategy unit can also propose tactics during a game based on player performance data. For example, in a basketball game, the strategy unit proposes effective attacking tactics based on the player's shooting success rate. Furthermore, the strategy unit can propose strategies that take into account player health data. For example, in a tennis game, the strategy unit suggests rest times during the game based on the player's fatigue level. This provides specific strategies for improving player performance and contributes to winning the game.

[0068] The video editing system can further include a recovery unit to improve a player's performance. The recovery unit analyzes the player's fatigue state after a match and proposes an appropriate recovery plan. For example, it evaluates a soccer player's fatigue level after a match and provides a recovery plan. The recovery unit can also propose a recovery plan based on the player's health data. For example, it analyzes a basketball player's heart rate after a match and provides a recovery plan. Furthermore, the recovery unit can propose a recovery plan that takes the player's psychological state into consideration. For example, it evaluates a tennis player's psychological state after a match and provides a recovery plan for relaxation. This allows the player to effectively recover from fatigue and prepare for the next match.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The video analysis unit analyzes the game video. For example, the video analysis unit uses image recognition technology to analyze the game video and motion analysis algorithms to analyze the movements of the players. It can also extract specific scenes from the game video. For example, in soccer game video, it can extract scenes of players dribbling and passing. Step 2: The performance evaluation unit evaluates the player's movements and technical performance based on the match footage analyzed by the video analysis unit, such as the speed and acceleration of the player's movements, the accuracy of their shots, and the success rate of their passes. Step 3: The play extraction unit extracts outstanding plays evaluated by the performance evaluation unit, such as scoring scenes, important defensive plays, and consecutive passing and shooting scenes. Step 4: The highlight editor compiles the outstanding plays extracted by the play extractor into a highlight reel. For example, the editor can edit the order of the plays, adjust the length of the edits, and add visual effects, such as adding slow-motion effects to goal scenes.

[0071] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0073] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0076] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0077] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0078] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0079] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0080] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0081] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0082] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0083] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0085] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0086] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0087] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0091] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0092] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0093] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0096] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0097] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0098] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0100] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0101] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0112] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0118] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0121] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0122] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0123] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0124] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0125] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0126] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0127] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0128] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0129] 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.

[0130] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0131] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0132] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0133] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0134] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0135] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0136] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0137] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0138] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A video analysis department that analyzes game footage, a performance evaluation unit that evaluates the movements and technical performance of players based on the match video analyzed by the video analysis unit; a play extraction unit that extracts excellent plays evaluated by the performance evaluation unit; a highlight editing unit that compiles a highlight reel by connecting the excellent plays extracted by the play extraction unit. A system characterized by:

2. The performance evaluation unit Analyze the minute changes in the athlete's movements and the balance of their body to evaluate the quality of their performance in more detail 2. The system of claim 1.

3. The performance evaluation unit Compare with the player's past performance data to automatically identify areas for growth and improvement 2. The system of claim 1.

4. The performance evaluation unit Emotions are estimated from the facial expressions and body movements of the athlete, and the influence of the emotional state on the performance is evaluated.

2. The system of claim 1.

5. The performance evaluation unit Analyzing the game footage of the different sports and evaluating the technical similarities and differences between the sports.

2. The system of claim 1.

6. The performance evaluation unit When analyzing the performance of the athlete, the reaction and cheers of the spectators are analyzed, and the correlation between the emotions of the spectators and the performance of the athlete is evaluated.

2. The system of claim 1.

7. The performance evaluation unit Analyzing the emotional reactions of the spectators, identifying the plays that excite the spectators the most, and reflecting those plays in the highlight reel.

2. The system of claim 1.

8. The play extraction unit Evaluate the continuity and consistency of the player's movements and extract particularly excellent continuous plays.

2. The system of claim 1.

Images