System

Abstract

An object of a system according to an embodiment is to automate data analysis in sports and to efficiently and accurately perform the data analysis.SOLUTION: A system according to an embodiment includes a moving image analysis unit, a recognition unit, a motion analysis unit, a data aggregation unit, and a tactic analysis unit. The moving image analysis unit analyzes moving image data. The recognition unit recognizes a person and a ball from the moving image data analyzed by the moving image analysis unit. The motion analysis unit analyzes motions of the person and the ball recognized by the recognition unit. The data aggregation unit automatically aggregates the number of passes, shots, and turnovers analyzed by the motion analysis unit. The tactics analysis unit analyzes tactics based on the data aggregated by the data aggregation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology involves manually analyzing sports data, leaving room for improvement in terms of efficiency and accuracy.

[0005] The system according to the embodiment aims to automate data analysis in sports and perform it efficiently and accurately. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a recognition unit, a motion analysis unit, a data compilation unit, and a tactical analysis unit. The video analysis unit analyzes video data. The recognition unit recognizes people and the ball from the video data analyzed by the video analysis unit. The motion analysis unit analyzes the movements of the people and the ball recognized by the recognition unit. The data compilation unit automatically compiles the number of passes, shots, and turnovers analyzed by the motion analysis unit. The tactical analysis unit analyzes tactics based on the data compiled by the data compilation unit. [Effects of the Invention]

[0007] The system according to the embodiment automates data analysis in sports and can perform it efficiently and accurately. [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 data analysis system according to an embodiment of the present invention uses AI to analyze video data and automatically perform data analysis. This system uses AI to recognize people and the ball in the video, identify passes and shots based on their motion and ball movement, and automatically analyze and compile the number of passes, successful shots, and turnovers, enabling tactical analysis based on overall movements. This data analysis system streamlines data analysis in the sports world, reduces the burden on staff, provides real-time data analysis results to spectators, and assists individual users in their own analysis.

[0029] A data analysis system according to an embodiment includes a video analysis unit, a recognition unit, a motion analysis unit, a data compilation unit, and a tactical analysis unit. The video analysis unit analyzes video data. For example, the video analysis unit analyzes game footage and identifies people and the ball in the footage. The recognition unit recognizes people and the ball from the video data analyzed by the video analysis unit. For example, the recognition unit identifies player movements and the position of the ball and tracks the movements of each player. The motion analysis unit analyzes the movements of people and the ball recognized by the recognition unit. For example, the motion analysis unit analyzes a player's kicking motion to determine whether it was a pass or a shot. The data compilation unit automatically tallys the number of passes, shots, and turnovers analyzed by the motion analysis unit. For example, the data compilation unit counts all passes made during a game and tallys the number of successful shots. The tactical analysis unit analyzes tactics based on the data compiled by the data compilation unit. For example, the tactical analysis unit analyzes the movements of the entire team and determines what tactics are being used. As a result, the data analysis system according to the embodiment can improve the efficiency of data analysis in the sports world, reduce the burden on staff, provide real-time data analysis results to spectators, and assist individual users in their own analysis.

[0030] The recognition unit can estimate a player's level of fatigue and physical condition based on their movements and provide feedback in real time. For example, the recognition unit uses AI to analyze a player's movements and estimate their level of fatigue based on their heart rate and movement speed. For example, if a player's movements slow down, it determines that fatigue is accumulating and suggests that they take a break. The recognition unit also uses AI to analyze a player's movement patterns and detect abnormal movements to estimate poor physical condition. For example, if it detects movements that differ from normal movements, it suggests the possibility of poor physical condition. The recognition unit also uses AI to analyze a player's movements in real time and provide feedback on their level of fatigue and physical condition. For example, if a player is fatigued, it instructs them to take a break. This allows the system to grasp a player's level of fatigue and physical condition in real time and provide appropriate feedback, thereby optimizing their performance.

[0031] The motion analysis unit can simultaneously measure the rotation and speed of the ball and provide detailed data. For example, the motion analysis unit uses AI to analyze the movement of the ball and measure the rotation speed and direction. For example, it measures the rotation speed of the ball during a shot and evaluates the accuracy of the shot. The motion analysis unit also uses AI to measure the speed of the ball in real time and evaluate the strength of a player's pass or shot. For example, a fast pass speed is determined to have a high success rate. The motion analysis unit also uses AI to simultaneously analyze the rotation and speed of the ball and evaluate a player's technique. For example, if the ball rotates accurately and quickly, it is determined that the player has high skill. This allows for detailed measurements of the rotation and speed of the ball, which is useful for evaluating a player's technique and analyzing their tactics.

[0032] The video analysis unit can simultaneously analyze video data and audio data to analyze communication between players and the content of instructions. For example, the video analysis unit uses AI to simultaneously analyze video data and audio data to analyze communication between players. For example, it analyzes the audio when players give instructions and understands the content of the instructions. The video analysis unit also uses AI to analyze audio data and evaluate the frequency and content of communication between players. For example, it determines that players who communicate frequently have strong teamwork. The video analysis unit also uses AI to analyze audio data and display the content of instructions between players in real time. For example, if a player gives the instruction to "pass," the instruction is displayed on the screen. In this way, by analyzing communication between players and the content of instructions, it is possible to strengthen teamwork.

[0033] The video analysis unit develops general-purpose analysis algorithms that can be applied to different sports and competitions, and can also be used to analyze data from other sports. For example, the video analysis unit develops general-purpose analysis algorithms that can be applied not only to soccer but also to other sports such as basketball and tennis. For example, the algorithms for analyzing player movements and ball movement can be standardized. Furthermore, the video analysis unit realizes centralized management of sports data by having the AI ​​analyze data from different sports and use a common analysis algorithm. For example, the video analysis unit analyzes soccer and basketball data using the same algorithm. Furthermore, the video analysis unit analyzes data from different sports using the AI ​​and develops general-purpose analysis algorithms that take into account the characteristics of each sport. For example, it analyzes passes in soccer and shots in basketball. This allows for the development of general-purpose analysis algorithms that can be applied to different sports and competitions, enabling centralized management of sports data.

[0034] The motion analysis unit can analyze a player's muscle movements and joint angles to evaluate the efficiency of their movements. For example, the motion analysis unit uses AI to analyze a player's movements and measure muscle movements and joint angles. For example, it can analyze a player's running form and evaluate the efficiency of their movements. The motion analysis unit also uses AI to analyze a player's movements and evaluate the efficiency of their movements based on muscle movements and joint angles. For example, it can analyze joint angles when shooting and evaluate the efficiency of their shooting form. The motion analysis unit also uses AI to analyze a player's movements in real time and evaluate the efficiency of their movements based on muscle movements and joint angles. For example, if a player moves efficiently, it can give them a high rating. This evaluation of the efficiency of a player's movements can help improve their skills and prevent injuries.

[0035] The motion analysis unit can estimate a player's line of sight and intentions, and analyze the tactical intentions of passes and shots. For example, the motion analysis unit uses AI to analyze a player's line of sight and estimate the intention to pass or shoot. For example, it analyzes which direction a player is looking and estimates the intention to pass in that direction. The motion analysis unit also uses AI to analyze a player's movements and estimate the intention to pass or shoot. For example, it analyzes a player's movements while holding the ball and estimates the intention to pass. The motion analysis unit also uses AI to simultaneously analyze a player's line of sight and movements and estimate the intention to pass or shoot. For example, it estimates the intention to pass in the direction the player is looking. In this way, analyzing a player's line of sight and intentions allows for understanding tactical intentions, which is useful for formulating strategies.

[0036] The motion analysis unit can develop a system that automatically provides feedback on improvements to a player's form and technique. For example, the motion analysis unit develops a system in which AI analyzes a player's movements and automatically provides feedback on improvements to their form and technique. For example, it points out areas for improvement in running form. The motion analysis unit also develops a system in which AI analyzes a player's movements in real time and provides feedback on improvements to their form and technique. For example, it points out areas for improvement in their shooting form. The motion analysis unit also develops a system in which AI analyzes a player's movements and automatically provides feedback on improvements to their form and technique. For example, it points out areas for improvement to improve the accuracy of their passes. This automatically provides feedback on improvements to a player's form and technique, helping to improve their technique.

[0037] The motion analysis unit can automatically generate a training plan for a player based on the pass and shot recognition results and suggest individual training. For example, the motion analysis unit uses AI to analyze the pass and shot recognition results and automatically generate a training plan for a player. For example, it suggests a training menu to improve the shooting success rate. The motion analysis unit also uses AI to propose an individual training plan for a player based on the pass and shot recognition results. For example, it suggests a training menu to improve passing accuracy. The motion analysis unit also uses AI to analyze the pass and shot recognition results and automatically generate a training plan for a player. For example, it suggests a training menu to reduce turnovers. This allows for efficient training by automatically generating a training plan for a player and suggesting individual training.

[0038] The data aggregation unit can predict player performance based on the aggregated data and propose a strategy for the next game. For example, the data aggregation unit analyzes data aggregated by AI and predicts player performance. For example, it predicts performance in the next game based on past data and proposes a strategy. The data aggregation unit also predicts player performance based on data aggregated by AI and proposes a strategy for the next game. For example, it proposes a strategy that takes into account the player's fatigue level. The data aggregation unit also analyzes data aggregated by AI, predicts player performance and proposes a strategy for the next game. For example, it proposes tactics that will improve the performance of a specific player. In this way, by predicting player performance and proposing a strategy for the next game, it is possible to improve the win rate of the game.

[0039] The data aggregation unit can track a player's growth and evolution over the long term based on the aggregated data and support career plans. For example, AI analyzes the aggregated data to track a player's growth and evolution over the long term. For example, it draws a player's growth curve based on past performance data. The data aggregation unit also supports a player's career plan based on the aggregated data using AI. For example, it proposes an optimal training plan based on the player's growth data. The data aggregation unit also analyzes the aggregated data using AI to track a player's growth and evolution over the long term and support career plans. For example, it proposes future position changes based on the player's growth data. In this way, it is possible to promote a player's growth by tracking a player's growth and evolution over the long term and supporting career plans.

[0040] The data aggregation unit can develop a system that proposes optimization of the tactics and formation of the entire team based on the aggregated data. The data aggregation unit, for example, uses AI to analyze the aggregated data and propose optimization of the tactics and formation of the entire team. For example, it proposes an optimal formation based on past match data. The data aggregation unit also develops a system that uses AI to optimize the tactics of the entire team based on the aggregated data. For example, it analyzes and proposes situations in which a particular tactic is likely to be successful. The data aggregation unit also develops a system that uses AI to analyze the aggregated data and optimize the formation of the entire team. For example, it proposes changes to player positions and builds an optimal formation. In this way, by proposing optimization of the tactics and formation of the entire team, it is possible to improve team performance.

[0041] The data aggregation unit can visualize the data analysis results and provide a dashboard that coaches and players can intuitively understand. For example, the data aggregation unit uses AI to visualize the data analysis results and provide a dashboard that coaches and players can intuitively understand. For example, it displays performance data using graphs and charts. The data aggregation unit also develops a dashboard that coaches and players can intuitively understand based on the data analysis results using AI. For example, it displays performance data that is updated in real time. The data aggregation unit also visualizes the data analysis results using AI and provides a dashboard that coaches and players can intuitively understand. For example, it provides an interface that allows performance data for each player to be viewed at a glance. In this way, visualizing the data analysis results makes it easier for coaches and players to intuitively understand, which is useful for formulating strategies and improving performance.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data analysis system can further include a nutritional analysis unit that analyzes the nutritional status of athletes. The nutritional analysis unit, for example, analyzes the athletes' dietary data and evaluates their nutritional balance. For example, it analyzes the calories and nutrients the athletes have ingested and proposes an appropriate nutritional balance. The nutritional analysis unit also compares the athletes' performance data with their dietary data to evaluate the impact of their nutritional status on performance. For example, if a particular nutrient is lacking, it may suggest that performance may decline. The nutritional analysis unit also proposes an individual nutritional plan based on the athletes' dietary data. For example, it may suggest the nutrients needed before a match to help improve performance. This makes it possible to optimize performance by analyzing the athletes' nutritional status and proposing an appropriate nutritional plan.

[0044] The data analysis system may further include a sleep analysis unit that analyzes the sleep state of a player. The sleep analysis unit, for example, analyzes the player's sleep data and evaluates the quality of sleep. For example, it analyzes the player's sleep time and the proportion of deep sleep and proposes an appropriate sleep pattern. The sleep analysis unit also compares the player's performance data with the player's sleep data and evaluates the impact of the sleep state on performance. For example, it suggests that lack of sleep may have a negative impact on performance. The sleep analysis unit also proposes an individual sleep improvement plan based on the player's sleep data. For example, it provides advice on ensuring an appropriate amount of sleep before a match. This makes it possible to optimize performance by analyzing the player's sleep state and proposing an appropriate sleep plan.

[0045] The data analysis system may further include an injury prevention analysis unit that analyzes injury prevention for a player. The injury prevention analysis unit, for example, analyzes a player's movement data and evaluates injury risk. For example, it may suggest that a particular movement pattern may increase injury risk. The injury prevention analysis unit also compares a player's performance data with injury data and evaluates the impact of injury risk on performance. For example, it may suggest that a player with a high injury risk may experience a decline in performance. The injury prevention analysis unit also proposes an individual injury prevention plan based on the player's movement data. For example, it may suggest specific stretches or training to reduce injury risk. In this way, by analyzing a player's injury prevention and proposing an appropriate injury prevention plan, performance can be optimized.

[0046] The data analysis system can further include a recovery analysis unit that analyzes a player's recovery. The recovery analysis unit, for example, analyzes the player's biometric data to evaluate the progress of recovery. For example, it analyzes heart rate and muscle recovery status to evaluate the effectiveness of recovery. The recovery analysis unit also compares the player's performance data with recovery data to evaluate the impact of recovery on performance. For example, it suggests the possibility that appropriate recovery may contribute to improved performance. The recovery analysis unit also proposes an individual recovery plan based on the player's recovery data. For example, it suggests specific recovery training and nutritional supplementation to maximize the effectiveness of recovery. In this way, by analyzing a player's recovery and proposing an appropriate recovery plan, it is possible to optimize performance.

[0047] The data analysis system can further include a feedback analysis unit that analyzes feedback from players. The feedback analysis unit, for example, analyzes a player's self-evaluation data and evaluates the effectiveness of the feedback. For example, it analyzes the player's self-evaluated performance data and evaluates the effectiveness of the feedback. The feedback analysis unit also compares the player's performance data with the self-evaluation data and evaluates the impact of feedback on performance. For example, it suggests the possibility that positive feedback may contribute to improving performance. The feedback analysis unit also proposes an individual feedback plan based on the player's self-evaluation data. For example, it suggests a specific feedback method and timing to maximize the effectiveness of the feedback. In this way, performance can be optimized by analyzing a player's feedback and proposing an appropriate feedback plan.

[0048] The data analysis system can further include a communication analysis unit that analyzes players' communication skills. The communication analysis unit, for example, analyzes conversation data between players to evaluate the quality of communication. For example, it analyzes the frequency and content of conversations between players to evaluate the effectiveness of communication. The communication analysis unit also compares the players' performance data with the conversation data to evaluate the impact of communication on performance. For example, it suggests the possibility that effective communication may contribute to improved performance. The communication analysis unit also proposes individual communication improvement plans based on the players' conversation data. For example, it suggests specific communication techniques or training to improve the quality of communication. This makes it possible to optimize performance by analyzing players' communication skills and proposing appropriate communication improvement plans.

[0049] The data analysis system can further include a learning analysis unit that analyzes a player's learning ability. The learning analysis unit, for example, analyzes a player's training data and evaluates their learning progress. For example, it analyzes the speed at which a player learns new techniques and evaluates their learning ability. The learning analysis unit also compares the player's performance data with their learning data and evaluates the impact of learning on performance. For example, it suggests the possibility that effective learning may contribute to improved performance. The learning analysis unit also proposes an individual learning plan based on the player's learning data. For example, it suggests specific learning methods and training to maximize the learning effect. In this way, by analyzing a player's learning ability and proposing an appropriate learning plan, it is possible to optimize performance.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The video analysis unit analyzes video data. For example, the video analysis unit analyzes game footage and identifies people and the ball in the footage. Step 2: The recognition unit recognizes people and the ball from the video data analyzed by the video analysis unit. For example, the recognition unit identifies the movements of players and the position of the ball, and tracks the movements of each player. Step 3: The motion analysis unit analyzes the movements of the person and the ball recognized by the recognition unit. For example, the motion analysis unit analyzes the motion of a player kicking the ball and determines whether it is a pass or a shot. Step 4: The data collection unit automatically tally the number of passes, shots, and turnovers analyzed by the motion analysis unit. For example, the data collection unit counts all passes made during a match and tally the number of successful shots. Step 5: The tactical analysis unit analyzes tactics based on the data collected by the data collection unit. For example, the tactical analysis unit analyzes the movements of the entire team and determines what tactics are being used.

[0052] (Example 2) A data analysis system according to an embodiment of the present invention uses AI to analyze video data and automatically perform data analysis. This system uses AI to recognize people and the ball in the video, identify passes and shots based on their motion and ball movement, and automatically analyze and compile the number of passes, successful shots, and turnovers, enabling tactical analysis based on overall movements. This data analysis system streamlines data analysis in the sports world, reduces the burden on staff, provides real-time data analysis results to spectators, and assists individual users in their own analysis.

[0053] A data analysis system according to an embodiment includes a video analysis unit, a recognition unit, a motion analysis unit, a data compilation unit, and a tactical analysis unit. The video analysis unit analyzes video data. For example, the video analysis unit analyzes game footage and identifies people and the ball in the footage. The recognition unit recognizes people and the ball from the video data analyzed by the video analysis unit. For example, the recognition unit identifies player movements and the position of the ball and tracks the movements of each player. The motion analysis unit analyzes the movements of people and the ball recognized by the recognition unit. For example, the motion analysis unit analyzes a player's kicking motion to determine whether it was a pass or a shot. The data compilation unit automatically tallys the number of passes, shots, and turnovers analyzed by the motion analysis unit. For example, the data compilation unit counts all passes made during a game and tallys the number of successful shots. The tactical analysis unit analyzes tactics based on the data compiled by the data compilation unit. For example, the tactical analysis unit analyzes the movements of the entire team and determines what tactics are being used. As a result, the data analysis system according to the embodiment can improve the efficiency of data analysis in the sports world, reduce the burden on staff, provide real-time data analysis results to spectators, and assist individual users in their own analysis.

[0054] The recognition unit can estimate a player's level of fatigue and physical condition based on their movements and provide feedback in real time. For example, the recognition unit uses AI to analyze a player's movements and estimate their level of fatigue based on their heart rate and movement speed. For example, if a player's movements slow down, it determines that fatigue is accumulating and suggests that they take a break. The recognition unit also uses AI to analyze a player's movement patterns and detect abnormal movements to estimate poor physical condition. For example, if it detects movements that differ from normal movements, it suggests the possibility of poor physical condition. The recognition unit also uses AI to analyze a player's movements in real time and provide feedback on their level of fatigue and physical condition. For example, if a player is fatigued, it instructs them to take a break. This allows the system to grasp a player's level of fatigue and physical condition in real time and provide appropriate feedback, thereby optimizing their performance.

[0055] The motion analysis unit can simultaneously measure the rotation and speed of the ball and provide detailed data. For example, the motion analysis unit uses AI to analyze the movement of the ball and measure the rotation speed and direction. For example, it measures the rotation speed of the ball during a shot and evaluates the accuracy of the shot. The motion analysis unit also uses AI to measure the speed of the ball in real time and evaluate the strength of a player's pass or shot. For example, a fast pass speed is determined to have a high success rate. The motion analysis unit also uses AI to simultaneously analyze the rotation and speed of the ball and evaluate a player's technique. For example, if the ball rotates accurately and quickly, it is determined that the player has high skill. This allows for detailed measurements of the rotation and speed of the ball, which is useful for evaluating a player's technique and analyzing their tactics.

[0056] The recognition unit can infer emotions from players' facial expressions and movements, and analyze their psychological state during a match. For example, the recognition unit uses AI to analyze a player's facial expression and infer their emotion. For example, if a player smiles, it infers positive emotion, and if they frown, it infers negative emotion. The recognition unit also uses AI to analyze a player's movements and infer their emotion. For example, if a player moves relaxed, it infers positive emotion, and if they move tensely, it infers negative emotion. The recognition unit also uses AI to simultaneously analyze a player's facial expression and movements and infer their emotion. For example, if a player's facial expression is smiling and their movements are relaxed, it infers positive emotion, and if a player's facial expression is stern and their movements are tense, it infers negative emotion. By analyzing a player's psychological state, it is possible to develop strategies to improve their performance during a match.

[0057] The video analysis unit can simultaneously analyze video data and audio data to analyze communication between players and the content of instructions. For example, the video analysis unit uses AI to simultaneously analyze video data and audio data to analyze communication between players. For example, it analyzes the audio when players give instructions and understands the content of the instructions. The video analysis unit also uses AI to analyze audio data and evaluate the frequency and content of communication between players. For example, it determines that players who communicate frequently have strong teamwork. The video analysis unit also uses AI to analyze audio data and display the content of instructions between players in real time. For example, if a player gives the instruction to "pass," the instruction is displayed on the screen. In this way, by analyzing communication between players and the content of instructions, it is possible to strengthen teamwork.

[0058] The video analysis unit develops general-purpose analysis algorithms that can be applied to different sports and competitions, and can also be used to analyze data from other sports. For example, the video analysis unit develops general-purpose analysis algorithms that can be applied not only to soccer but also to other sports such as basketball and tennis. For example, the algorithms for analyzing player movements and ball movement can be standardized. Furthermore, the video analysis unit realizes centralized management of sports data by having the AI ​​analyze data from different sports and use a common analysis algorithm. For example, the video analysis unit analyzes soccer and basketball data using the same algorithm. Furthermore, the video analysis unit analyzes data from different sports using the AI ​​and develops general-purpose analysis algorithms that take into account the characteristics of each sport. For example, it analyzes passes in soccer and shots in basketball. This allows for the development of general-purpose analysis algorithms that can be applied to different sports and competitions, enabling centralized management of sports data.

[0059] The recognition unit can analyze the spectators' facial expressions and reactions and evaluate the level of excitement of the match in real time. For example, the recognition unit uses AI to analyze the spectators' facial expressions and evaluate the level of excitement of the match. For example, if the spectators are smiling, it will determine that they are excited and display this in real time. The recognition unit also uses AI to analyze the spectators' reactions and evaluate the level of excitement of the match. For example, if the spectators are clapping, it will determine that they are excited and display this in real time. The recognition unit also uses AI to simultaneously analyze the spectators' facial expressions and reactions and evaluate the level of excitement of the match. For example, if the spectators are smiling and clapping, it will determine that they are very excited and display this in real time. In this way, by analyzing the spectators' reactions, it is possible to grasp the level of excitement of the match in real time and improve spectator engagement.

[0060] The motion analysis unit can analyze a player's muscle movements and joint angles to evaluate the efficiency of their movements. For example, the motion analysis unit uses AI to analyze a player's movements and measure muscle movements and joint angles. For example, it can analyze a player's running form and evaluate the efficiency of their movements. The motion analysis unit also uses AI to analyze a player's movements and evaluate the efficiency of their movements based on muscle movements and joint angles. For example, it can analyze joint angles when shooting and evaluate the efficiency of their shooting form. The motion analysis unit also uses AI to analyze a player's movements in real time and evaluate the efficiency of their movements based on muscle movements and joint angles. For example, if a player moves efficiently, it can give them a high rating. This evaluation of the efficiency of a player's movements can help improve their skills and prevent injuries.

[0061] The motion analysis unit can estimate a player's line of sight and intentions, and analyze the tactical intentions of passes and shots. For example, the motion analysis unit uses AI to analyze a player's line of sight and estimate the intention to pass or shoot. For example, it analyzes which direction a player is looking and estimates the intention to pass in that direction. The motion analysis unit also uses AI to analyze a player's movements and estimate the intention to pass or shoot. For example, it analyzes a player's movements while holding the ball and estimates the intention to pass. The motion analysis unit also uses AI to simultaneously analyze a player's line of sight and movements and estimate the intention to pass or shoot. For example, it estimates the intention to pass in the direction the player is looking. In this way, analyzing a player's line of sight and intentions allows for understanding tactical intentions, which is useful for formulating strategies.

[0062] The motion analysis unit can analyze a player's psychological state when passing or shooting, and evaluate the level of pressure and concentration. For example, the motion analysis unit uses AI to analyze a player's facial expression and estimate the player's psychological state when passing or shooting. For example, if a player is nervous, it determines that the player is under high pressure. The motion analysis unit also uses AI to analyze a player's movements and estimate the player's psychological state when passing or shooting. For example, if a player is relaxed, it determines that the player is under high concentration. The motion analysis unit also uses AI to simultaneously analyze a player's facial expression and movements and estimate the player's psychological state when passing or shooting. For example, if a player is nervous, it determines that the player is under high pressure, and if a player is relaxed, it determines that the player is under high concentration. In this way, analyzing a player's psychological state can evaluate the player's pressure and concentration, which helps improve performance.

[0063] The motion analysis unit can develop a system that automatically provides feedback on improvements to a player's form and technique. For example, the motion analysis unit develops a system in which AI analyzes a player's movements and automatically provides feedback on improvements to their form and technique. For example, it points out areas for improvement in running form. The motion analysis unit also develops a system in which AI analyzes a player's movements in real time and provides feedback on improvements to their form and technique. For example, it points out areas for improvement in their shooting form. The motion analysis unit also develops a system in which AI analyzes a player's movements and automatically provides feedback on improvements to their form and technique. For example, it points out areas for improvement to improve the accuracy of their passes. This automatically provides feedback on improvements to a player's form and technique, helping to improve their technique.

[0064] The motion analysis unit can automatically generate a training plan for a player based on the pass and shot recognition results and suggest individual training. For example, the motion analysis unit uses AI to analyze the pass and shot recognition results and automatically generate a training plan for a player. For example, it suggests a training menu to improve the shooting success rate. The motion analysis unit also uses AI to propose an individual training plan for a player based on the pass and shot recognition results. For example, it suggests a training menu to improve passing accuracy. The motion analysis unit also uses AI to analyze the pass and shot recognition results and automatically generate a training plan for a player. For example, it suggests a training menu to reduce turnovers. This allows for efficient training by automatically generating a training plan for a player and suggesting individual training.

[0065] The motion analysis unit can analyze spectators' reactions and automatically extract highlight scenes during a match. For example, the motion analysis unit uses AI to analyze spectators' facial expressions and automatically extract highlight scenes during a match. For example, scenes in which spectators are excited are extracted as highlights. The motion analysis unit also uses AI to analyze spectators' reactions and automatically extract highlight scenes during a match. For example, scenes in which spectators are clapping are extracted as highlights. The motion analysis unit also uses AI to simultaneously analyze spectators' facial expressions and reactions and automatically extract highlight scenes during a match. For example, scenes in which spectators are clapping excitedly are extracted as highlights. In this way, by analyzing spectators' reactions, highlight scenes during a match can be automatically extracted, increasing spectator excitement.

[0066] The data aggregation unit can predict player performance based on the aggregated data and propose a strategy for the next game. For example, the data aggregation unit analyzes data aggregated by AI and predicts player performance. For example, it predicts performance in the next game based on past data and proposes a strategy. The data aggregation unit also predicts player performance based on data aggregated by AI and proposes a strategy for the next game. For example, it proposes a strategy that takes into account the player's fatigue level. The data aggregation unit also analyzes data aggregated by AI, predicts player performance and proposes a strategy for the next game. For example, it proposes tactics that will improve the performance of a specific player. In this way, by predicting player performance and proposing a strategy for the next game, it is possible to improve the win rate of the game.

[0067] The data aggregation unit can track a player's growth and evolution over the long term based on the aggregated data and support career plans. For example, AI analyzes the aggregated data to track a player's growth and evolution over the long term. For example, it draws a player's growth curve based on past performance data. The data aggregation unit also supports a player's career plan based on the aggregated data using AI. For example, it proposes an optimal training plan based on the player's growth data. The data aggregation unit also analyzes the aggregated data using AI to track a player's growth and evolution over the long term and support career plans. For example, it proposes future position changes based on the player's growth data. In this way, it is possible to promote a player's growth by tracking a player's growth and evolution over the long term and supporting career plans.

[0068] The data aggregation unit can use the emotion estimation function to analyze the relationship between a player's psychological state and performance, and evaluate the effectiveness of mental training. For example, the data aggregation unit uses AI to analyze a player's psychological state and evaluate the relationship with performance. For example, it compares the player's emotion score with performance data. The data aggregation unit also uses AI to analyze the relationship between a player's psychological state and performance using the emotion estimation function. For example, it evaluates whether a player with high positive emotions will improve their performance. The data aggregation unit also uses AI to analyze a player's psychological state and evaluate the effectiveness of mental training. For example, it compares the emotion score of a player who has received mental training with performance data. This allows the analysis of the relationship between a player's psychological state and performance and the evaluation of the effectiveness of mental training to help strengthen the player's mental aspects.

[0069] The data aggregation unit can develop a system that proposes optimization of the tactics and formation of the entire team based on the aggregated data. The data aggregation unit, for example, uses AI to analyze the aggregated data and propose optimization of the tactics and formation of the entire team. For example, it proposes an optimal formation based on past match data. The data aggregation unit also develops a system that uses AI to optimize the tactics of the entire team based on the aggregated data. For example, it analyzes and proposes situations in which a particular tactic is likely to be successful. The data aggregation unit also develops a system that uses AI to analyze the aggregated data and optimize the formation of the entire team. For example, it proposes changes to player positions and builds an optimal formation. In this way, by proposing optimization of the tactics and formation of the entire team, it is possible to improve team performance.

[0070] The data aggregation unit can visualize the data analysis results and provide a dashboard that coaches and players can intuitively understand. For example, the data aggregation unit uses AI to visualize the data analysis results and provide a dashboard that coaches and players can intuitively understand. For example, it displays performance data using graphs and charts. The data aggregation unit also develops a dashboard that coaches and players can intuitively understand based on the data analysis results using AI. For example, it displays performance data that is updated in real time. The data aggregation unit also visualizes the data analysis results using AI and provides a dashboard that coaches and players can intuitively understand. For example, it provides an interface that allows performance data for each player to be viewed at a glance. In this way, visualizing the data analysis results makes it easier for coaches and players to intuitively understand, which is useful for formulating strategies and improving performance.

[0071] The data aggregation unit uses the emotion estimation function to analyze audience reactions, which can be used to improve marketing strategies and fan engagement. For example, the data aggregation unit uses AI to analyze audience facial expressions and use this information to improve marketing strategies. For example, it analyzes scenes in which audience members are excited and uses the data to conduct promotions. The data aggregation unit also uses AI to analyze audience reactions and use this information to improve fan engagement. For example, it analyzes scenes in which audience members are applauding and uses the data to plan events. The data aggregation unit also uses AI to simultaneously analyze audience facial expressions and reactions and use this information to improve marketing strategies and fan engagement. For example, it analyzes scenes in which audience members are excited and uses the data to provide content for fans. In this way, analyzing audience reactions can be used to improve marketing strategies and fan engagement.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The data analysis system can further include a nutritional analysis unit that analyzes the nutritional status of athletes. The nutritional analysis unit, for example, analyzes the athletes' dietary data and evaluates their nutritional balance. For example, it analyzes the calories and nutrients the athletes have ingested and proposes an appropriate nutritional balance. The nutritional analysis unit also compares the athletes' performance data with their dietary data to evaluate the impact of their nutritional status on performance. For example, if a particular nutrient is lacking, it may suggest that performance may decline. The nutritional analysis unit also proposes an individual nutritional plan based on the athletes' dietary data. For example, it may suggest the nutrients needed before a match to help improve performance. This makes it possible to optimize performance by analyzing the athletes' nutritional status and proposing an appropriate nutritional plan.

[0074] The data analysis system may further include a sleep analysis unit that analyzes the sleep state of a player. The sleep analysis unit, for example, analyzes the player's sleep data and evaluates the quality of sleep. For example, it analyzes the player's sleep time and the proportion of deep sleep and proposes an appropriate sleep pattern. The sleep analysis unit also compares the player's performance data with the player's sleep data and evaluates the impact of the sleep state on performance. For example, it suggests that lack of sleep may have a negative impact on performance. The sleep analysis unit also proposes an individual sleep improvement plan based on the player's sleep data. For example, it provides advice on ensuring an appropriate amount of sleep before a match. This makes it possible to optimize performance by analyzing the player's sleep state and proposing an appropriate sleep plan.

[0075] The data analysis system can further include a stress analysis unit that analyzes the player's stress level. The stress analysis unit, for example, analyzes the player's biometric data to assess the player's stress level. For example, it analyzes heart rate and galvanic skin response to detect signs of stress. The stress analysis unit also compares the player's performance data with the player's stress data to assess the impact of stress on performance. For example, it suggests that high stress levels may have a negative impact on performance. The stress analysis unit also proposes an individual stress management plan based on the player's stress data. For example, it suggests relaxation techniques and mental training to support stress reduction. This makes it possible to optimize performance by analyzing the player's stress level and proposing an appropriate stress management plan.

[0076] The data analysis system can further include a motivation analysis unit that analyzes a player's motivation. The motivation analysis unit, for example, analyzes the player's behavioral data and evaluates their motivation level. For example, it analyzes the frequency and intensity of practice to detect fluctuations in motivation. The motivation analysis unit also compares the player's performance data with their motivation data and evaluates the impact that motivation has on performance. For example, it suggests the possibility that high motivation may contribute to improved performance. The motivation analysis unit also proposes an individual motivation improvement plan based on the player's motivation data. For example, it proposes goal setting and a reward system and provides support to maintain motivation. This makes it possible to optimize performance by analyzing a player's motivation and proposing an appropriate motivation improvement plan.

[0077] The data analysis system can further include a concentration analysis unit that analyzes a player's concentration. The concentration analysis unit, for example, analyzes the player's behavioral data and evaluates the player's concentration level. For example, it analyzes the frequency of distractions during practice and detects fluctuations in concentration. The concentration analysis unit also compares the player's performance data with the player's concentration data and evaluates the impact of concentration on performance. For example, it suggests the possibility that high concentration contributes to improved performance. The concentration analysis unit also proposes an individual concentration improvement plan based on the player's concentration data. For example, it suggests mental training or relaxation techniques to improve concentration. This makes it possible to analyze a player's concentration and propose an appropriate concentration improvement plan, thereby optimizing performance.

[0078] The data analysis system may further include an injury prevention analysis unit that analyzes injury prevention for a player. The injury prevention analysis unit, for example, analyzes a player's movement data and evaluates injury risk. For example, it may suggest that a particular movement pattern may increase injury risk. The injury prevention analysis unit also compares a player's performance data with injury data and evaluates the impact of injury risk on performance. For example, it may suggest that a player with a high injury risk may experience a decline in performance. The injury prevention analysis unit also proposes an individual injury prevention plan based on the player's movement data. For example, it may suggest specific stretches or training to reduce injury risk. In this way, by analyzing a player's injury prevention and proposing an appropriate injury prevention plan, performance can be optimized.

[0079] The data analysis system can further include a recovery analysis unit that analyzes a player's recovery. The recovery analysis unit, for example, analyzes the player's biometric data to evaluate the progress of recovery. For example, it analyzes heart rate and muscle recovery status to evaluate the effectiveness of recovery. The recovery analysis unit also compares the player's performance data with recovery data to evaluate the impact of recovery on performance. For example, it suggests the possibility that appropriate recovery may contribute to improved performance. The recovery analysis unit also proposes an individual recovery plan based on the player's recovery data. For example, it suggests specific recovery training and nutritional supplementation to maximize the effectiveness of recovery. In this way, by analyzing a player's recovery and proposing an appropriate recovery plan, it is possible to optimize performance.

[0080] The data analysis system can further include a feedback analysis unit that analyzes feedback from players. The feedback analysis unit, for example, analyzes a player's self-evaluation data and evaluates the effectiveness of the feedback. For example, it analyzes the player's self-evaluated performance data and evaluates the effectiveness of the feedback. The feedback analysis unit also compares the player's performance data with the self-evaluation data and evaluates the impact of feedback on performance. For example, it suggests the possibility that positive feedback may contribute to improving performance. The feedback analysis unit also proposes an individual feedback plan based on the player's self-evaluation data. For example, it suggests a specific feedback method and timing to maximize the effectiveness of the feedback. In this way, performance can be optimized by analyzing a player's feedback and proposing an appropriate feedback plan.

[0081] The data analysis system can further include a communication analysis unit that analyzes players' communication skills. The communication analysis unit, for example, analyzes conversation data between players to evaluate the quality of communication. For example, it analyzes the frequency and content of conversations between players to evaluate the effectiveness of communication. The communication analysis unit also compares the players' performance data with the conversation data to evaluate the impact of communication on performance. For example, it suggests the possibility that effective communication may contribute to improved performance. The communication analysis unit also proposes individual communication improvement plans based on the players' conversation data. For example, it suggests specific communication techniques or training to improve the quality of communication. This makes it possible to optimize performance by analyzing players' communication skills and proposing appropriate communication improvement plans.

[0082] The data analysis system can further include a learning analysis unit that analyzes a player's learning ability. The learning analysis unit, for example, analyzes a player's training data and evaluates their learning progress. For example, it analyzes the speed at which a player learns new techniques and evaluates their learning ability. The learning analysis unit also compares the player's performance data with their learning data and evaluates the impact of learning on performance. For example, it suggests the possibility that effective learning may contribute to improved performance. The learning analysis unit also proposes an individual learning plan based on the player's learning data. For example, it suggests specific learning methods and training to maximize the learning effect. In this way, by analyzing a player's learning ability and proposing an appropriate learning plan, it is possible to optimize performance.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The video analysis unit analyzes video data. For example, the video analysis unit analyzes game footage and identifies people and the ball in the footage. Step 2: The recognition unit recognizes people and the ball from the video data analyzed by the video analysis unit. For example, the recognition unit identifies the movements of players and the position of the ball, and tracks the movements of each player. Step 3: The motion analysis unit analyzes the movements of the person and the ball recognized by the recognition unit. For example, the motion analysis unit analyzes the motion of a player kicking the ball and determines whether it is a pass or a shot. Step 4: The data collection unit automatically tally the number of passes, shots, and turnovers analyzed by the motion analysis unit. For example, the data collection unit counts all passes made during a match and tally the number of successful shots. Step 5: The tactical analysis unit analyzes tactics based on the data collected by the data collection unit. For example, the tactical analysis unit analyzes the movements of the entire team and determines what tactics are being used.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a video analysis unit that analyzes video data; a recognition unit that recognizes people and a ball from the video data analyzed by the video analysis unit; a motion analysis unit that analyzes the motion of the person and the ball recognized by the recognition unit; a data collection unit that automatically collects the number of passes, shots, and turnovers analyzed by the motion analysis unit; a tactics analysis unit that analyzes tactics based on the data collected by the data collection unit. A system characterized by:

2. The recognition unit Estimates fatigue and physical condition based on player movements and provides real-time feedback 2. The system of claim 1.

3. The motion analysis unit The ball's rotation and speed are measured simultaneously to provide detailed data 2. The system of claim 1.

4. The recognition unit Inferring emotions from players' facial expressions and movements, and analyzing their psychological state during a match 2. The system of claim 1.

5. The video analysis unit The video data and audio data are analyzed simultaneously to analyze the communication between players and the content of instructions.

2. The system of claim 1.

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