System

Abstract

An object of the system according to the embodiment is to automatically extract a specific scene from a game video.SOLUTION: A system according to an embodiment includes a tutorial video learning unit, a game video analysis unit, and a scene extraction unit. The tutorial video learning unit learns a tutorial video. The match video analysis unit analyzes the match video on the basis of the feature learned by the tutorial video learning unit. The scene extraction unit extracts a scene matching the learned feature from the game video analyzed by the game video analysis 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] With conventional technology, analyzing game footage requires a great deal of time and effort, and it is difficult to automatically extract specific scenes.

[0005] The system according to the embodiment aims to automatically extract specific scenes from game footage. [Means for solving the problem]

[0006] The system according to the embodiment includes a tutorial video learning unit, a game video analysis unit, and a scene extraction unit. The tutorial video learning unit learns tutorial videos. The game video analysis unit analyzes game video based on the features learned by the tutorial video learning unit. The scene extraction unit extracts scenes that match the learned features from the game video analyzed by the game video analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically extract specific scenes from game footage. [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 analysis system according to an embodiment of the present invention is a system that improves the efficiency of video analysis in amateur sports. This system trains a generation AI to learn tutorial videos containing scenes and patterns of interest in advance, and then extracts those scenes from full game footage. This improves the efficiency of video analysis in amateur sports, enabling the user to quickly and accurately extract specific scenes of interest.

[0029] A video analysis system according to an embodiment includes a tutorial video learning unit, a game video analysis unit, and a scene extraction unit. The tutorial video learning unit learns tutorial videos. For example, the tutorial video learning unit analyzes tutorial videos containing scenes or patterns that a user wants to extract and learns their features. The game video analysis unit analyzes game video based on the features learned by the tutorial video learning unit. For example, the game video analysis unit identifies scenes from the game video that match the learned features. The scene extraction unit extracts scenes that match the learned features from the game video analyzed by the game video analysis unit. For example, the scene extraction unit extracts scenes that attract a user's attention, such as specific passing or defensive moves. This allows the video analysis system according to an embodiment to improve the efficiency of video analysis in amateur sports and quickly and accurately extract specific scenes desired by a user.

[0030] The tutorial video learning unit reproduces the player's movements and position information as a 3D model and allows the generation AI to learn from it. For example, the tutorial video learning unit reproduces the player's movements in the tutorial video as a 3D model and allows the generation AI to learn from it. For example, the player's running trajectory and positioning are reproduced in 3D space, and the generation AI learns those movements. The tutorial video learning unit also reproduces the player's movements and position information as a 3D model and allows the generation AI to learn from it. For example, the player's movements are created as a 3D model using motion capture technology, and the generation AI analyzes those movements. The tutorial video learning unit also reproduces the player's movements in the tutorial video as a 3D model and allows the generation AI to learn from it. For example, the player's movements are reproduced as a skeletal model, and the generation AI learns those movements. In this way, by reproducing the player's movements and position information as a 3D model, more detailed characteristics can be learned.

[0031] The tutorial video learning unit analyzes the audio information of the tutorial video and allows the generation AI to learn from it. The tutorial video learning unit, for example, analyzes the audio information of the tutorial video and allows the generation AI to learn from it. For example, the coach's instructions and communication between players are converted into text using voice recognition technology, and the generation AI learns the content. The tutorial video learning unit also analyzes the audio information of the tutorial video and allows the generation AI to learn from it. For example, communication between players is analyzed using voice recognition technology, and the generation AI learns the content. The tutorial video learning unit also analyzes the audio information of the tutorial video and allows the generation AI to learn from it. For example, the coach's instructions are converted into text using voice recognition technology, and the generation AI learns the content. In this way, communication between players and the coach's instructions can be learned by analyzing the audio information.

[0032] The tutorial video learning unit can include scenes from different sports, allowing the generation AI to learn a variety of patterns. For example, the tutorial video can include scenes from different sports, allowing the generation AI to learn a variety of patterns. For example, scenes from soccer, basketball, and futsal can be included for learning. The tutorial video learning unit can also include scenes from different sports, allowing the generation AI to learn a variety of patterns. For example, scenes from tennis and volleyball can be included for learning. The tutorial video learning unit can also include scenes from different sports, allowing the generation AI to learn a variety of patterns. For example, scenes from rugby and handball can be included for learning. In this way, the versatility of the generation AI is improved by learning scenes from different sports.

[0033] The tutorial video learning unit analyzes the vital data of the players and has the generating AI learn from it. The tutorial video learning unit, for example, analyzes the vital data of the players in the tutorial video and has the generating AI learn from it. For example, it analyzes the heart rate and breathing rate, and the generating AI learns that data. The tutorial video learning unit also analyzes the vital data of the players in the tutorial video and has the generating AI learn from it. For example, it monitors the player's heart rate in real time, and the generating AI learns that data. The tutorial video learning unit also analyzes the vital data of the players in the tutorial video and has the generating AI learn from it. For example, it analyzes the player's breathing rate, and the generating AI learns that data. In this way, by learning the players' vital data, more detailed scene analysis is possible.

[0034] The game video analysis unit can learn more detailed features by taking into account the speed and acceleration of players' movements. For example, when analyzing a scene, the generation AI takes into account the speed and acceleration of players' movements to learn more detailed features. For example, it analyzes the running speed and acceleration of players and learns the data. Furthermore, when analyzing a scene, the generation AI takes into account the speed and acceleration of players' movements to learn more detailed features. For example, it analyzes the timing of the start and end of players' movements and learns the data. Furthermore, when analyzing a scene, the generation AI takes into account the speed and acceleration of players' movements to learn more detailed features. For example, it analyzes the points at which players' movements change and learns the data. In this way, by taking into account the speed and acceleration of players' movements, more detailed features can be learned.

[0035] The game video analysis unit can learn visual points of interest using the players' gaze tracking data. For example, when analyzing a scene, the generation AI uses the players' gaze tracking data to learn visual points of interest. For example, it analyzes the players' gaze movements and learns the data. Also, when analyzing a scene, the generation AI uses the players' gaze tracking data to learn visual points of interest. For example, it analyzes the points that players focus on and learns the data. Also, when analyzing a scene, the generation AI uses the players' gaze tracking data to learn visual points of interest. For example, it analyzes the players' gaze movement patterns and learns the data. In this way, the generation AI can learn visual points of interest by using the players' gaze tracking data.

[0036] The game video analysis unit can simultaneously analyze footage from different camera angles and learn three-dimensional features. For example, when analyzing a scene, the generative AI simultaneously analyzes footage from different camera angles and learns three-dimensional features. For example, it integrates footage from multiple cameras and generates a 3D model for learning. When analyzing a scene, the generative AI also simultaneously analyzes footage from different camera angles and learns three-dimensional features. For example, it analyzes footage from different perspectives and learns player movements in three dimensions. When analyzing a scene, the generative AI also simultaneously analyzes footage from different camera angles and learns three-dimensional features. For example, it uses footage from multiple cameras to recreate player movements in 3D space and learns. This allows it to learn three-dimensional features by analyzing footage from different camera angles.

[0037] The game video analysis unit can take into account environmental factors such as weather and time of day, and learn their influence. For example, when analyzing a scene, the generation AI takes into account environmental factors such as weather and time of day, and learns their influence. For example, it compares and learns the movements of players on sunny and rainy days. The generation AI also takes into account environmental factors such as weather and time of day, and learns their influence when analyzing a scene. For example, it analyzes game footage from daytime and nighttime, and learns the influence of environmental factors. The generation AI also takes into account environmental factors such as weather and time of day, and learns their influence when analyzing a scene. For example, it analyzes and learns the influence of changes in wind speed and temperature on player movements. This allows more detailed features to be learned by taking into account environmental factors such as weather and time of day.

[0038] The match video analysis unit can analyze the distance and interactions between players to learn tactical characteristics. For example, when analyzing a scene, the generative AI analyzes the distance and interactions between players to learn tactical characteristics. For example, it analyzes the pass distance and positional relationships between players to learn tactical patterns. Furthermore, when analyzing a scene, the generative AI analyzes the distance and interactions between players to learn tactical characteristics. For example, it analyzes teamwork and formations between players to learn tactical characteristics. Furthermore, when analyzing a scene, the generative AI analyzes the distance and interactions between players to learn tactical characteristics. For example, it analyzes the movement of players on the defensive line to learn tactical characteristics. In this way, tactical characteristics can be learned by analyzing the distance and interactions between players.

[0039] The match video analysis unit can analyze the trajectory and speed of the ball to learn the characteristics of the play. For example, when analyzing a scene, the generation AI analyzes the trajectory and speed of the ball to learn the characteristics of the play. For example, it analyzes the trajectory of a ball pass and the speed of a shot to learn the characteristics of the play. Furthermore, when analyzing a scene, the generation AI analyzes the trajectory and speed of the ball to learn the characteristics of the play. For example, it analyzes the dribbling trajectory of the ball and the speed of a pass to learn the characteristics of the play. Furthermore, when analyzing a scene, the generation AI analyzes the trajectory and speed of the ball to learn the characteristics of the play. For example, it analyzes the timing of trapping and releasing the ball to learn the characteristics of the play. In this way, the generation AI can learn the characteristics of the play by analyzing the trajectory and speed of the ball.

[0040] The match video analysis unit can integrate data from different matches and learn common characteristics. For example, when analyzing a scene, the generation AI integrates data from different matches and learns common characteristics. For example, it analyzes multiple match videos and learns common tactical patterns. When analyzing a scene, the generation AI also integrates data from different matches and learns common characteristics. For example, it analyzes match videos of different teams and learns common playing styles. When analyzing a scene, the generation AI also integrates data from different matches and learns common characteristics. For example, it analyzes match videos from different leagues and learns common tactical trends. This allows common characteristics to be learned by integrating data from different matches.

[0041] The match video analysis unit can analyze players' physical fitness data and pick scenes based on that information. When analyzing a scene, the generation AI, for example, analyzes players' physical fitness data and picks scenes based on that information. For example, it analyzes a player's running distance and picks scenes where physical fitness is significantly reduced. When analyzing a scene, the generation AI also analyzes players' physical fitness data and picks scenes based on that information. For example, it analyzes a player's calorie consumption and picks scenes where physical fitness is significantly reduced. When analyzing a scene, the generation AI also analyzes players' physical fitness data and picks scenes based on that information. For example, it analyzes a player's heart rate and picks scenes where physical fitness is significantly reduced. In this way, by analyzing players' physical fitness data, it is possible to pick out physically important scenes.

[0042] The scene extraction unit can extract more natural scenes by taking into account the continuity and consistency of the players' movements. For example, when extracting a scene, the generation AI takes into account the continuity and consistency of the players' movements to extract more natural scenes. For example, it extracts scenes so that the players' movements are not interrupted. Furthermore, when extracting a scene, the generation AI takes into account the continuity and consistency of the players' movements to extract more natural scenes. For example, it extracts scenes so that the players' movements are smoothly connected. Furthermore, when extracting a scene, the generation AI takes into account the continuity and consistency of the players' movements to extract more natural scenes. For example, it extracts scenes where the players' movements are consistent. In this way, by taking into account the continuity and consistency of the players' movements, more natural scenes can be extracted.

[0043] The scene extraction unit can analyze player positioning and formation to extract tactically important scenes. For example, when extracting scenes, the generation AI analyzes player positioning and formation to extract tactically important scenes. For example, it extracts scenes where player positioning is effective. Furthermore, when extracting scenes, the generation AI analyzes player positioning and formation to extract tactically important scenes. For example, it extracts scenes where the formation is successful. Furthermore, when extracting scenes, the generation AI analyzes player positioning and formation to extract tactically important scenes. For example, it extracts scenes where the defensive line functions effectively. In this way, tactically important scenes can be extracted by analyzing player positioning and formation.

[0044] The scene extraction unit can analyze game footage of different sports and extract common features. For example, when extracting scenes, the generation AI analyzes game footage of different sports and extracts common features. For example, it analyzes game footage of soccer and basketball and extracts common tactical patterns. When extracting scenes, the generation AI also analyzes game footage of different sports and extracts common features. For example, it analyzes game footage of futsal and handball and extracts common playing styles. When extracting scenes, the generation AI also analyzes game footage of different sports and extracts common features. For example, it analyzes game footage of rugby and American football and extracts common tactical trends. This makes it possible to extract common features by analyzing game footage of different sports.

[0045] The scene extraction unit can extract important scenes by taking into account the progress of the game. For example, when extracting scenes, the generation AI takes into account the progress of the game and extracts important scenes. For example, it distinguishes between important scenes from the first half and the second half and extracts them. The generation AI also takes into account the progress of the game when extracting scenes and extracts important scenes. For example, it identifies and extracts important scenes in overtime. The generation AI also takes into account the progress of the game when extracting scenes and extracts important scenes. For example, it extracts important scenes immediately after the start of the game or near the end of the game. In this way, important scenes can be extracted by taking into account the progress of the game.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The video analysis system can also be equipped with a health management unit that monitors the health of players. The health management unit collects players' vital signs in real time and can issue an alert if an abnormality is detected. For example, an alert can be issued if the player's heart rate is abnormally high or if the player's body temperature rises sharply. The health management unit can also analyze the player's fatigue level and suggest appropriate rest times. This allows the system to constantly monitor the player's health and take appropriate measures.

[0048] The video analysis system can further include a training suggestion unit to improve player performance. The training suggestion unit can analyze a player's weaknesses from game footage and suggest individual training menus. For example, it can suggest shooting practice for a player with poor shooting accuracy, and endurance training for a player who lacks stamina. The training suggestion unit can also monitor a player's growth and update the training menu as appropriate. This allows for continuous improvement of player performance.

[0049] The video analysis system can also be equipped with a tactical analysis unit that performs tactical analysis of the match. The tactical analysis unit can analyze the movements of the entire team from the match video and propose effective tactics. For example, it can identify weaknesses in the opposing team's defensive line and propose attacking tactics. The tactical analysis unit can also propose tactical adjustments based on real-time data during the match. This allows for flexible changes to tactics during the match and leads to victory.

[0050] The video analysis system can also be equipped with a technical analysis section that supports the improvement of players' techniques. The technical analysis section can analyze players' technical movements from game footage and suggest areas for improvement. For example, it can analyze dribbling speed and passing accuracy and suggest specific ways to improve. The technical analysis section can also monitor players' technical improvement and report progress. This allows players to continuously improve their techniques.

[0051] The video analysis system can also be equipped with a tactical adjustment unit that adjusts match tactics in real time. The tactical adjustment unit can analyze data during a match in real time and propose effective tactical changes. For example, it can analyze the movements of the opposing team and change the positioning of the defensive line. The tactical adjustment unit can also suggest the timing of player substitutions based on player performance data. This allows for flexible changes to tactics during a match and leads to victory.

[0052] The video analysis system can further include a data integration unit that integrates match data and performs comprehensive analysis. The data integration unit can integrate match footage, player vital data, emotional data, and other data to perform comprehensive analysis. For example, it can integrate player performance data and emotional data to provide specific suggestions for improving performance. The data integration unit can also integrate data from different matches and extract common characteristics. This allows for comprehensive data analysis and the suggestion of more effective tactics and training methods.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The tutorial video learning unit learns the tutorial video. For example, it analyzes a tutorial video that includes a scene or pattern that the user wants to extract, and learns its features. Step 2: The game video analysis unit analyzes the game video based on the features learned by the tutorial video learning unit. For example, it identifies scenes from the game video that match the learned features. Step 3: The scene extraction unit extracts scenes that match the learned features from the game footage analyzed by the game footage analysis unit. For example, it extracts scenes that attract the user's attention, such as specific passing or defensive moves.

[0055] (Example 2) A video analysis system according to an embodiment of the present invention is a system that improves the efficiency of video analysis in amateur sports. This system trains a generation AI to learn tutorial videos containing scenes and patterns of interest in advance, and then extracts those scenes from full game footage. This improves the efficiency of video analysis in amateur sports, enabling the user to quickly and accurately extract specific scenes of interest.

[0056] A video analysis system according to an embodiment includes a tutorial video learning unit, a game video analysis unit, and a scene extraction unit. The tutorial video learning unit learns tutorial videos. For example, the tutorial video learning unit analyzes tutorial videos containing scenes or patterns that a user wants to extract and learns their features. The game video analysis unit analyzes game video based on the features learned by the tutorial video learning unit. For example, the game video analysis unit identifies scenes from the game video that match the learned features. The scene extraction unit extracts scenes that match the learned features from the game video analyzed by the game video analysis unit. For example, the scene extraction unit extracts scenes that attract a user's attention, such as specific passing or defensive moves. This allows the video analysis system according to an embodiment to improve the efficiency of video analysis in amateur sports and quickly and accurately extract specific scenes desired by a user.

[0057] The tutorial video learning unit reproduces the player's movements and position information as a 3D model and allows the generation AI to learn from it. For example, the tutorial video learning unit reproduces the player's movements in the tutorial video as a 3D model and allows the generation AI to learn from it. For example, the player's running trajectory and positioning are reproduced in 3D space, and the generation AI learns those movements. The tutorial video learning unit also reproduces the player's movements and position information as a 3D model and allows the generation AI to learn from it. For example, the player's movements are created as a 3D model using motion capture technology, and the generation AI analyzes those movements. The tutorial video learning unit also reproduces the player's movements in the tutorial video as a 3D model and allows the generation AI to learn from it. For example, the player's movements are reproduced as a skeletal model, and the generation AI learns those movements. In this way, by reproducing the player's movements and position information as a 3D model, more detailed characteristics can be learned.

[0058] The tutorial video learning unit analyzes the audio information of the tutorial video and allows the generation AI to learn from it. The tutorial video learning unit, for example, analyzes the audio information of the tutorial video and allows the generation AI to learn from it. For example, the coach's instructions and communication between players are converted into text using voice recognition technology, and the generation AI learns the content. The tutorial video learning unit also analyzes the audio information of the tutorial video and allows the generation AI to learn from it. For example, communication between players is analyzed using voice recognition technology, and the generation AI learns the content. The tutorial video learning unit also analyzes the audio information of the tutorial video and allows the generation AI to learn from it. For example, the coach's instructions are converted into text using voice recognition technology, and the generation AI learns the content. In this way, communication between players and the coach's instructions can be learned by analyzing the audio information.

[0059] The tutorial video learning unit uses the emotion estimation function to estimate emotions from the players' facial expressions and movements, and has the generation AI learn that emotional information. The tutorial video learning unit, for example, uses the emotion estimation function to estimate emotions from the players' facial expressions and movements, and has the generation AI learn that emotional information. For example, the player's facial expressions are analyzed, an emotion score is calculated, and the generation AI learns from that. The tutorial video learning unit also uses the emotion estimation function to estimate emotions from the players' facial expressions and movements, and has the generation AI learn that emotional information. For example, the player's movements are analyzed, an emotion score is calculated, and the generation AI learns from that. The tutorial video learning unit also uses the emotion estimation function to estimate emotions from the players' facial expressions and movements, and has the generation AI learn that emotional information. For example, the player's facial expressions and movements are analyzed simultaneously, an emotion score is calculated, and the generation AI learns from that. In this way, by learning the players' emotional information, more detailed scene analysis is possible.

[0060] The tutorial video learning unit can include scenes from different sports, allowing the generation AI to learn a variety of patterns. For example, the tutorial video can include scenes from different sports, allowing the generation AI to learn a variety of patterns. For example, scenes from soccer, basketball, and futsal can be included for learning. The tutorial video learning unit can also include scenes from different sports, allowing the generation AI to learn a variety of patterns. For example, scenes from tennis and volleyball can be included for learning. The tutorial video learning unit can also include scenes from different sports, allowing the generation AI to learn a variety of patterns. For example, scenes from rugby and handball can be included for learning. In this way, the versatility of the generation AI is improved by learning scenes from different sports.

[0061] The tutorial video learning unit analyzes the vital data of the players and has the generating AI learn from it. The tutorial video learning unit, for example, analyzes the vital data of the players in the tutorial video and has the generating AI learn from it. For example, it analyzes the heart rate and breathing rate, and the generating AI learns that data. The tutorial video learning unit also analyzes the vital data of the players in the tutorial video and has the generating AI learn from it. For example, it monitors the player's heart rate in real time, and the generating AI learns that data. The tutorial video learning unit also analyzes the vital data of the players in the tutorial video and has the generating AI learn from it. For example, it analyzes the player's breathing rate, and the generating AI learns that data. In this way, by learning the players' vital data, more detailed scene analysis is possible.

[0062] The tutorial video learning unit uses the emotion estimation function to analyze audience reactions and have the generation AI learn that information. The tutorial video learning unit, for example, uses the emotion estimation function to analyze audience reactions and have the generation AI learn that information. For example, the audience's facial expressions are analyzed and an emotion score is calculated, and the generation AI learns from that. The tutorial video learning unit also uses the emotion estimation function to analyze audience reactions and have the generation AI learn that information. For example, the audience's cheers and cheers are analyzed and an emotion score is calculated, and the generation AI learns from that. The tutorial video learning unit also uses the emotion estimation function to analyze audience reactions and have the generation AI learn that information. For example, the audience's movements are analyzed and an emotion score is calculated, and the generation AI learns from that. In this way, by learning audience reactions, more detailed scene analysis is possible.

[0063] The game video analysis unit can learn more detailed features by taking into account the speed and acceleration of players' movements. For example, when analyzing a scene, the generation AI takes into account the speed and acceleration of players' movements to learn more detailed features. For example, it analyzes the running speed and acceleration of players and learns the data. Furthermore, when analyzing a scene, the generation AI takes into account the speed and acceleration of players' movements to learn more detailed features. For example, it analyzes the timing of the start and end of players' movements and learns the data. Furthermore, when analyzing a scene, the generation AI takes into account the speed and acceleration of players' movements to learn more detailed features. For example, it analyzes the points at which players' movements change and learns the data. In this way, by taking into account the speed and acceleration of players' movements, more detailed features can be learned.

[0064] The game video analysis unit can learn visual points of interest using the players' gaze tracking data. For example, when analyzing a scene, the generation AI uses the players' gaze tracking data to learn visual points of interest. For example, it analyzes the players' gaze movements and learns the data. Also, when analyzing a scene, the generation AI uses the players' gaze tracking data to learn visual points of interest. For example, it analyzes the points that players focus on and learns the data. Also, when analyzing a scene, the generation AI uses the players' gaze tracking data to learn visual points of interest. For example, it analyzes the players' gaze movement patterns and learns the data. In this way, the generation AI can learn visual points of interest by using the players' gaze tracking data.

[0065] The game video analysis unit uses the emotion estimation function to analyze the psychological state of the players and have the generation AI learn that information. For example, when analyzing a scene, the generation AI uses the emotion estimation function to analyze the psychological state of the players and have the generation AI learn that information. For example, the generation AI analyzes the players' facial expressions and calculates an emotion score, which the generation AI learns from. Also, when analyzing a scene, the generation AI uses the emotion estimation function to analyze the players' psychological state and have the generation AI learn that information. For example, the generation AI analyzes the players' movements and calculates an emotion score, which the generation AI learns from. Also, when analyzing a scene, the generation AI uses the emotion estimation function to analyze the players' psychological state and have the generation AI learn that information. For example, the generation AI analyzes the players' tone of voice and calculates an emotion score, which the generation AI learns from. In this way, analyzing the players' psychological states enables more detailed scene analysis.

[0066] The game video analysis unit can simultaneously analyze footage from different camera angles and learn three-dimensional features. For example, when analyzing a scene, the generative AI simultaneously analyzes footage from different camera angles and learns three-dimensional features. For example, it integrates footage from multiple cameras and generates a 3D model for learning. When analyzing a scene, the generative AI also simultaneously analyzes footage from different camera angles and learns three-dimensional features. For example, it analyzes footage from different perspectives and learns player movements in three dimensions. When analyzing a scene, the generative AI also simultaneously analyzes footage from different camera angles and learns three-dimensional features. For example, it uses footage from multiple cameras to recreate player movements in 3D space and learns. This allows it to learn three-dimensional features by analyzing footage from different camera angles.

[0067] The game video analysis unit can take into account environmental factors such as weather and time of day, and learn their influence. For example, when analyzing a scene, the generation AI takes into account environmental factors such as weather and time of day, and learns their influence. For example, it compares and learns the movements of players on sunny and rainy days. The generation AI also takes into account environmental factors such as weather and time of day, and learns their influence when analyzing a scene. For example, it analyzes game footage from daytime and nighttime, and learns the influence of environmental factors. The generation AI also takes into account environmental factors such as weather and time of day, and learns their influence when analyzing a scene. For example, it analyzes and learns the influence of changes in wind speed and temperature on player movements. This allows more detailed features to be learned by taking into account environmental factors such as weather and time of day.

[0068] The game video analysis unit uses the emotion estimation function to analyze the emotional reactions of the coach and spectators and has the generation AI learn that information. For example, when analyzing a scene, the generation AI uses the emotion estimation function to analyze the emotional reactions of the coach and spectators and has the generation AI learn that information. For example, the coach's facial expression is analyzed, an emotion score is calculated, and the generation AI learns from that. Also, when analyzing a scene, the generation AI uses the emotion estimation function to analyze the emotional reactions of the coach and spectators and has the generation AI learn that information. For example, the cheers of the spectators are analyzed, an emotion score is calculated, and the generation AI learns from that. Also, when analyzing a scene, the generation AI uses the emotion estimation function to analyze the emotional reactions of the coach and spectators and has the generation AI learn that information. For example, the tone of the coach's instructions is analyzed, an emotion score is calculated, and the generation AI learns from that. In this way, by learning the emotional reactions of the coach and spectators, more detailed scene analysis is possible.

[0069] The match video analysis unit can analyze the distance and interactions between players to learn tactical characteristics. For example, when analyzing a scene, the generative AI analyzes the distance and interactions between players to learn tactical characteristics. For example, it analyzes the pass distance and positional relationships between players to learn tactical patterns. Furthermore, when analyzing a scene, the generative AI analyzes the distance and interactions between players to learn tactical characteristics. For example, it analyzes teamwork and formations between players to learn tactical characteristics. Furthermore, when analyzing a scene, the generative AI analyzes the distance and interactions between players to learn tactical characteristics. For example, it analyzes the movement of players on the defensive line to learn tactical characteristics. In this way, tactical characteristics can be learned by analyzing the distance and interactions between players.

[0070] The match video analysis unit can analyze the trajectory and speed of the ball to learn the characteristics of the play. For example, when analyzing a scene, the generation AI analyzes the trajectory and speed of the ball to learn the characteristics of the play. For example, it analyzes the trajectory of a ball pass and the speed of a shot to learn the characteristics of the play. Furthermore, when analyzing a scene, the generation AI analyzes the trajectory and speed of the ball to learn the characteristics of the play. For example, it analyzes the dribbling trajectory of the ball and the speed of a pass to learn the characteristics of the play. Furthermore, when analyzing a scene, the generation AI analyzes the trajectory and speed of the ball to learn the characteristics of the play. For example, it analyzes the timing of trapping and releasing the ball to learn the characteristics of the play. In this way, the generation AI can learn the characteristics of the play by analyzing the trajectory and speed of the ball.

[0071] The game video analysis unit can use the emotion estimation function to analyze the emotional changes of players and pick scenes based on that information. For example, when analyzing a scene, the generation AI can use the emotion estimation function to analyze the emotional changes of players and pick scenes based on that information. For example, it can analyze the players' facial expressions and pick scenes with a high emotion score. Furthermore, when analyzing a scene, the generation AI can use the emotion estimation function to analyze the emotional changes of players and pick scenes based on that information. For example, it can analyze the players' movements and pick scenes with a high emotion score. Furthermore, when analyzing a scene, the generation AI can use the emotion estimation function to analyze the emotional changes of players and pick scenes based on that information. For example, it can analyze the tone of the players' voices and pick scenes with a high emotion score. In this way, by analyzing the emotional changes of players, it is possible to pick out emotionally important scenes.

[0072] The match video analysis unit can integrate data from different matches and learn common characteristics. For example, when analyzing a scene, the generation AI integrates data from different matches and learns common characteristics. For example, it analyzes multiple match videos and learns common tactical patterns. When analyzing a scene, the generation AI also integrates data from different matches and learns common characteristics. For example, it analyzes match videos of different teams and learns common playing styles. When analyzing a scene, the generation AI also integrates data from different matches and learns common characteristics. For example, it analyzes match videos from different leagues and learns common tactical trends. This allows common characteristics to be learned by integrating data from different matches.

[0073] The match video analysis unit can analyze players' physical fitness data and pick scenes based on that information. When analyzing a scene, the generation AI, for example, analyzes players' physical fitness data and picks scenes based on that information. For example, it analyzes a player's running distance and picks scenes where physical fitness is significantly reduced. When analyzing a scene, the generation AI also analyzes players' physical fitness data and picks scenes based on that information. For example, it analyzes a player's calorie consumption and picks scenes where physical fitness is significantly reduced. When analyzing a scene, the generation AI also analyzes players' physical fitness data and picks scenes based on that information. For example, it analyzes a player's heart rate and picks scenes where physical fitness is significantly reduced. In this way, by analyzing players' physical fitness data, it is possible to pick out physically important scenes.

[0074] The game video analysis unit can use the emotion estimation function to analyze the emotional reactions of spectators and pick scenes based on that information. For example, when analyzing a scene, the generation AI can use the emotion estimation function to analyze the emotional reactions of spectators and pick scenes based on that information. For example, it can analyze the cheers of spectators and pick scenes with a high emotion score. Furthermore, when analyzing a scene, the generation AI can use the emotion estimation function to analyze the emotional reactions of spectators and pick scenes based on that information. For example, it can analyze the facial expressions of spectators and pick scenes with a high emotion score. Furthermore, when analyzing a scene, the generation AI can use the emotion estimation function to analyze the emotional reactions of spectators and pick scenes based on that information. For example, it can analyze the movements of spectators and pick scenes with a high emotion score. In this way, by analyzing the emotional reactions of spectators, it is possible to pick out emotionally important scenes.

[0075] The scene extraction unit can extract more natural scenes by taking into account the continuity and consistency of the players' movements. For example, when extracting a scene, the generation AI takes into account the continuity and consistency of the players' movements to extract more natural scenes. For example, it extracts scenes so that the players' movements are not interrupted. Furthermore, when extracting a scene, the generation AI takes into account the continuity and consistency of the players' movements to extract more natural scenes. For example, it extracts scenes so that the players' movements are smoothly connected. Furthermore, when extracting a scene, the generation AI takes into account the continuity and consistency of the players' movements to extract more natural scenes. For example, it extracts scenes where the players' movements are consistent. In this way, by taking into account the continuity and consistency of the players' movements, more natural scenes can be extracted.

[0076] The scene extraction unit can analyze player positioning and formation to extract tactically important scenes. For example, when extracting scenes, the generation AI analyzes player positioning and formation to extract tactically important scenes. For example, it extracts scenes where player positioning is effective. Furthermore, when extracting scenes, the generation AI analyzes player positioning and formation to extract tactically important scenes. For example, it extracts scenes where the formation is successful. Furthermore, when extracting scenes, the generation AI analyzes player positioning and formation to extract tactically important scenes. For example, it extracts scenes where the defensive line functions effectively. In this way, tactically important scenes can be extracted by analyzing player positioning and formation.

[0077] The scene extraction unit can use the emotion estimation function to extract scenes where a player's emotions reach their peak. For example, when extracting scenes, the generation AI uses the emotion estimation function to extract scenes where a player's emotions reach their peak. For example, it analyzes the player's facial expressions and extracts scenes with the highest emotion score. In addition, when extracting scenes, the generation AI also uses the emotion estimation function to extract scenes where a player's emotions reach their peak. For example, it analyzes the player's movements and extracts scenes with the highest emotion score. In addition, when extracting scenes, the generation AI also uses the emotion estimation function to extract scenes where a player's emotions reach their peak. For example, it analyzes the player's tone of voice and extracts scenes with the highest emotion score. In this way, by extracting scenes where a player's emotions reach their peak, it is possible to pick out emotionally important scenes.

[0078] The scene extraction unit can analyze game footage of different sports and extract common features. For example, when extracting scenes, the generation AI analyzes game footage of different sports and extracts common features. For example, it analyzes game footage of soccer and basketball and extracts common tactical patterns. When extracting scenes, the generation AI also analyzes game footage of different sports and extracts common features. For example, it analyzes game footage of futsal and handball and extracts common playing styles. When extracting scenes, the generation AI also analyzes game footage of different sports and extracts common features. For example, it analyzes game footage of rugby and American football and extracts common tactical trends. This makes it possible to extract common features by analyzing game footage of different sports.

[0079] The scene extraction unit can extract important scenes by taking into account the progress of the game. For example, when extracting scenes, the generation AI takes into account the progress of the game and extracts important scenes. For example, it distinguishes between important scenes from the first half and the second half and extracts them. The generation AI also takes into account the progress of the game when extracting scenes and extracts important scenes. For example, it identifies and extracts important scenes in overtime. The generation AI also takes into account the progress of the game when extracting scenes and extracts important scenes. For example, it extracts important scenes immediately after the start of the game or near the end of the game. In this way, important scenes can be extracted by taking into account the progress of the game.

[0080] The scene extraction unit can use the emotion estimation function to extract scenes that will most strongly stimulate audience emotions. For example, when extracting scenes, the generation AI uses the emotion estimation function to extract scenes that will most strongly stimulate audience emotions. For example, it analyzes the audience's cheers and applause and extracts the scene with the highest emotion score. In addition, when extracting scenes, the generation AI also uses the emotion estimation function to extract scenes that will most strongly stimulate audience emotions. For example, it analyzes the audience's facial expressions and extracts the scene with the highest emotion score. In addition, when extracting scenes, the generation AI also uses the emotion estimation function to extract scenes that will most strongly stimulate audience emotions. For example, it analyzes the audience's movements and extracts the scene with the highest emotion score. In this way, by extracting scenes that will most strongly stimulate audience emotions, it is possible to pick out emotionally important scenes.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The video analysis system can also be equipped with a health management unit that monitors the health of players. The health management unit collects players' vital signs in real time and can issue an alert if an abnormality is detected. For example, an alert can be issued if the player's heart rate is abnormally high or if the player's body temperature rises sharply. The health management unit can also analyze the player's fatigue level and suggest appropriate rest times. This allows the system to constantly monitor the player's health and take appropriate measures.

[0083] The video analysis system can further include a training suggestion unit to improve player performance. The training suggestion unit can analyze a player's weaknesses from game footage and suggest individual training menus. For example, it can suggest shooting practice for a player with poor shooting accuracy, and endurance training for a player who lacks stamina. The training suggestion unit can also monitor a player's growth and update the training menu as appropriate. This allows for continuous improvement of player performance.

[0084] The video analysis system can also be equipped with a tactical analysis unit that performs tactical analysis of the match. The tactical analysis unit can analyze the movements of the entire team from the match video and propose effective tactics. For example, it can identify weaknesses in the opposing team's defensive line and propose attacking tactics. The tactical analysis unit can also propose tactical adjustments based on real-time data during the match. This allows for flexible changes to tactics during the match and leads to victory.

[0085] The video analysis system can also be equipped with a mental care section to support the players' psychological state. The mental care section can estimate the players' emotions and provide advice to reduce stress and anxiety. For example, it can suggest relaxation methods to relieve tension before a match. The mental care section can also monitor the players' emotional changes and arrange for professional counseling if necessary. This can stabilize the players' psychological state and maximize their performance.

[0086] The video analysis system can further include a spectator analysis unit that analyzes spectator reactions. The spectator analysis unit can estimate the emotions of spectators during a match and evaluate the excitement of the match based on that information. For example, it can analyze the volume of spectator cheers and applause to identify highlight scenes of the match. The spectator analysis unit can also analyze spectator facial expressions and calculate an emotion score. This allows for an objective evaluation of the excitement of the match and improves spectator satisfaction.

[0087] The video analysis system can also be equipped with a technical analysis section that supports the improvement of players' techniques. The technical analysis section can analyze players' technical movements from game footage and suggest areas for improvement. For example, it can analyze dribbling speed and passing accuracy and suggest specific ways to improve. The technical analysis section can also monitor players' technical improvement and report progress. This allows players to continuously improve their techniques.

[0088] The video analysis system can also be equipped with a tactical adjustment unit that adjusts match tactics in real time. The tactical adjustment unit can analyze data during a match in real time and propose effective tactical changes. For example, it can analyze the movements of the opposing team and change the positioning of the defensive line. The tactical adjustment unit can also suggest the timing of player substitutions based on player performance data. This allows for flexible changes to tactics during a match and leads to victory.

[0089] The video analysis system can further include a training suggestion unit that takes into account the player's emotions. The training suggestion unit can estimate the player's emotions and suggest a training menu based on those emotions. For example, if the player is feeling stressed, it can suggest training that incorporates relaxation. The training suggestion unit can also monitor the player's emotional changes and update the training menu as appropriate. This allows training that takes the player's emotions into consideration and maximizes performance.

[0090] The video analysis system can further include a data integration unit that integrates match data and performs comprehensive analysis. The data integration unit can integrate match footage, player vital data, emotional data, and other data to perform comprehensive analysis. For example, it can integrate player performance data and emotional data to provide specific suggestions for improving performance. The data integration unit can also integrate data from different matches and extract common characteristics. This allows for comprehensive data analysis and the suggestion of more effective tactics and training methods.

[0091] The video analysis system can further include a tactical suggestion unit that takes into account the player's emotions. The tactical suggestion unit can estimate the player's emotions and suggest tactics based on those emotions. For example, if a player is nervous, it can suggest tactics to relax the player. The tactical suggestion unit can also monitor the player's emotional changes and modify tactics as appropriate. This allows for tactics that take the player's emotions into account, maximizing the player's performance in the game.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The tutorial video learning unit learns the tutorial video. For example, it analyzes a tutorial video that includes a scene or pattern that the user wants to extract, and learns its features. Step 2: The game video analysis unit analyzes the game video based on the features learned by the tutorial video learning unit. For example, it identifies scenes from the game video that match the learned features. Step 3: The scene extraction unit extracts scenes that match the learned features from the game footage analyzed by the game footage analysis unit. For example, it extracts scenes that attract the user's attention, such as specific passing or defensive moves.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 tutorial video learning unit for learning tutorial videos; a game video analysis unit that analyzes game video based on the features learned by the tutorial video learning unit; a scene extraction unit that extracts scenes that match the learned features from the game video analyzed by the game video analysis unit. A system characterized by:

2. The tutorial video learning unit Player movements and position information are reproduced as 3D models and trained by the generative AI 2. The system of claim 1.

3. The tutorial video learning unit Analyzing the audio information of tutorial videos and training the generative AI 2. The system of claim 1.

4. The tutorial video learning unit Estimate emotions from the players' facial expressions and movements, and have the AI ​​learn that emotional information.

2. The system of claim 1.

5. The tutorial video learning unit Teaching the generative AI to learn a variety of patterns, including scenes from different sports 2. The system of claim 1.

Images