system

Abstract

The system according to this embodiment aims to analyze match footage and automatically perform the roles of referee and scorekeeper. [Solution] The system according to the embodiment comprises an analysis unit, a recognition unit, a judgment unit, and a recording unit. The analysis unit analyzes video of the match. The recognition unit recognizes the movements of the players and the position of the ball based on the data analyzed by the analysis unit. The judgment unit determines rule violations and scores based on the information recognized by the recognition unit. The recording unit records scores and fouls based on the information determined by the judgment unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, analyzing the video of a game and automatically playing the roles of a referee and a scorer have not been fully carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the video of a game and automatically play the roles of a referee and a scorer.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a recognition unit, a judgment unit, and a recording unit. The analysis unit analyzes video footage of the match. The recognition unit recognizes the movements of the players and the position of the ball based on the data analyzed by the analysis unit. The judgment unit determines rule violations and scores based on the information recognized by the recognition unit. The recording unit records scores and fouls based on the information determined by the judgment unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze video footage of a match and automatically perform the roles of referee and scorekeeper. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The basketball game recognition system according to an embodiment of the present invention is a system that recognizes a basketball game and automatically acts as both referee and scorer. This system analyzes game footage in real time to recognize player movements and the position of the ball. Next, based on the recognized information, it determines rule violations and scores. It also automatically records scores and fouls. This mechanism improves the fairness of the game and reduces the burden on referees and scorers. For example, the basketball game recognition system analyzes game footage in real time. In this process, the system uses machine learning techniques to accurately recognize player movements and the position of the ball. For example, the system identifies the numbers on the players' uniforms and the color of the ball, and tracks the players' movements and the position of the ball based on this. This allows for an accurate understanding of the progress of the game. Next, based on the recognized information, the system performs the role of a referee. Specifically, the system determines rule violations and scores. For example, the system detects when a player steps on the line or when the ball enters the goal, and determines rule violations and scores based on these. This improves the fairness of the game. Furthermore, the system also functions as a scorer. Specifically, the system automatically records scores and fouls. For example, the system detects when a player scores a point or commits a foul, and records the score or foul accordingly. This reduces the burden on the scorer. This mechanism improves the fairness of the game and reduces the burden on referees and scorers. For instance, by having the system act as a referee, rule violations and scoring decisions during the game are made quickly and accurately. Also, by having the system function as a scorer, the recording of points and fouls is done automatically, reducing the burden on the scorer. This makes the game run more smoothly and improves satisfaction for both spectators and players. In short, a basketball game recognition system can improve the fairness of the game and reduce the burden on referees and scorers.

[0029] The basketball game recognition system according to this embodiment comprises an analysis unit, a recognition unit, a judgment unit, and a recording unit. The analysis unit analyzes video footage of the game. The analysis unit, for example, receives video data of the game and analyzes the movements of the players and the position of the ball using machine learning techniques. The analysis unit, for example, analyzes the video data using deep learning techniques to identify the movements of the players and the position of the ball. The analysis unit can also analyze the video data using support vector machines to recognize the movements of the players and the position of the ball. Furthermore, the analysis unit can also analyze the video data using image analysis techniques to identify the movements of the players and the position of the ball. The recognition unit recognizes the movements of the players and the position of the ball based on the data analyzed by the analysis unit. The recognition unit, for example, identifies the numbers on the players' uniforms and the color of the ball, and tracks the movements of the players and the position of the ball based on this. The recognition unit, for example, uses image recognition techniques to recognize the movements of the players and the position of the ball. The recognition unit can also recognize the movements of the players using motion recognition techniques. Furthermore, the recognition unit can also recognize the position of the ball using position recognition techniques. The judgment unit determines rule violations and scores based on the information recognized by the recognition unit. The judgment unit detects, for example, when a player steps on the line or when the ball enters the goal, and determines rule violations and scores based on these. The judgment unit makes judgments based on criteria for rule violations, for example. The judgment unit can also make judgments based on criteria for scores. Furthermore, the judgment unit can also make judgments based on criteria for fouls. The recording unit records scores and fouls based on the information determined by the judgment unit. The recording unit records scores and fouls in digital format, for example. The recording unit records scores and fouls using digital recording technology, for example. The recording unit can also record on paper. Furthermore, the recording unit can record in a database. As a result, the basketball game recognition system can analyze game footage, recognize player movements and ball positions, determine rule violations and scores, and record scores and fouls.

[0030] The analysis unit analyzes the match footage. For example, the analysis unit receives match video data and uses machine learning techniques to analyze player movements and ball positions. Specifically, match video data is acquired in real time from high-resolution cameras and drones and transmitted to a central data server. The analysis unit uses deep learning techniques to analyze the video data and identify player movements and ball positions. Deep learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used, enabling high-precision recognition of complex patterns and movements within the video. Furthermore, it is also possible to analyze the video data using support vector machines (SVMs) to recognize player movements and ball positions. Because SVMs classify data based on specific features, they can quickly and accurately identify player movements and ball positions. By combining these techniques, the analysis unit can analyze the match video data from multiple angles and identify player movements and ball positions with high precision. In addition, the analysis unit can also use image analysis techniques to analyze the video data and identify player movements and ball positions. The image analysis technology utilizes optical flow and object detection algorithms, enabling real-time tracking of movement and position within the video. This allows the analysis unit to analyze the match video data with high accuracy, quickly and accurately identifying player movements and the ball's position.

[0031] The recognition unit recognizes the players' movements and the ball's position based on the data analyzed by the analysis unit. For example, the recognition unit identifies the players' uniform numbers and the ball's color, and tracks the players' movements and the ball's position based on that. Specifically, the recognition unit uses image recognition technology to identify the players' uniform numbers and the ball's color, and tracks the players' movements and the ball's position based on that. Image recognition technology employs object detection algorithms and pattern recognition technologies, enabling high-precision identification of players' uniform numbers and ball colors. The recognition unit can also recognize players' movements using motion recognition technology. Motion recognition technology employs posture estimation algorithms and motion classification algorithms, enabling high-precision recognition of players' movements. Furthermore, the recognition unit can also recognize the ball's position using position recognition technology. Position recognition technology employs tracking algorithms and position estimation algorithms, enabling high-precision recognition of the ball's position. As a result, the recognition unit can recognize players' movements and the ball's position with high precision based on the data analyzed by the analysis unit, and grasp the game situation in real time.

[0032] The judgment unit determines rule violations and scores based on information recognized by the recognition unit. For example, the judgment unit detects when a player steps on the line or when the ball goes into the goal, and determines rule violations and scores based on these. Specifically, the judgment unit makes judgments based on the criteria for rule violations. For example, it detects when a player steps on the line or when the ball goes out of bounds, and determines a rule violation based on these. The judgment unit can also make judgments based on the criteria for scores. For example, it detects when the ball goes into the goal or when a free throw is successful, and determines a score based on these. Furthermore, the judgment unit can also make judgments based on the criteria for fouls. For example, it detects when a player makes illegal contact with an opposing player or performs an action that violates the rules, and determines a foul based on these. As a result, the judgment unit can make highly accurate judgments on rule violations and scores based on information recognized by the recognition unit, and can fairly manage the progress of the game. Furthermore, the judgment unit can improve the accuracy of its judgments using AI. For example, it can make more accurate judgments by learning from past game data. This allows the judging panel to manage the progress of the match fairly and accurately, thereby improving the reliability of the match.

[0033] The recording department records scores and fouls based on the information determined by the refereeing department. For example, the recording department records scores and fouls digitally. Specifically, the recording department uses digital recording technology to record scores and fouls. This digital recording technology utilizes database management systems and cloud storage, enabling efficient and accurate recording of scores and fouls. The recording department can also record on paper. For example, it can record scores and foul information on paper as the official match record for later review. Furthermore, the recording department can record in a database. The database stores detailed match information and statistical data for later analysis and review. This allows the recording department to efficiently and accurately record scores and fouls based on the information determined by the refereeing department, and to centrally manage match records. Additionally, the recording department can always record the progress of the match in real time based on real-time updated data. For example, it can instantly record the moment a score or foul occurs in the database, allowing for real-time monitoring of the match's progress. Furthermore, the recording unit can automatically generate statistical data for matches, which can be used for post-match analysis and reporting. This allows the recording unit to efficiently and accurately record matches, grasp the progress of matches in real time, and utilize this data for post-match analysis and reporting.

[0034] The analysis unit can analyze match video data using machine learning techniques. For example, the analysis unit can analyze video data using deep learning techniques to identify player movements and ball positions. The analysis unit can also analyze video data using support vector machines to recognize player movements and ball positions. The analysis unit can also analyze video data using image analysis techniques to identify player movements and ball positions. This improves the accuracy of the analysis of match video data by using machine learning techniques. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input match video data into a generative AI and have the generative AI perform the analysis of player movements and ball positions.

[0035] The recognition unit can recognize the player's movements and the ball's position based on data from the analysis unit. For example, the recognition unit can identify the player's uniform number or the ball's color and track the player's movements and the ball's position based on that. The recognition unit can recognize the player's movements and the ball's position using, for example, image recognition technology. The recognition unit can also recognize the player's movements using motion recognition technology. Furthermore, the recognition unit can recognize the ball's position using position recognition technology. This improves the accuracy of recognizing the player's movements and the ball's position by using data from the analysis unit. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input data from the analysis unit into a generative AI and have the generative AI perform the recognition of the player's movements and the ball's position.

[0036] The judgment unit can determine rule violations and scores based on information from the recognition unit. For example, the judgment unit can detect when a player steps on the line or when the ball enters the goal, and determine rule violations and scores based on these. The judgment unit makes judgments based on criteria for rule violations, for example. The judgment unit can also make judgments based on criteria for scores. Furthermore, the judgment unit can make judgments based on criteria for fouls. This improves the accuracy of rule violation and score determinations by using information from the recognition unit. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input information from the recognition unit into a generative AI and have the generative AI perform rule violation and score determinations.

[0037] The recording unit can record scores and fouls based on information from the judging unit. The recording unit can, for example, record scores and fouls in digital format. The recording unit can, for example, record scores and fouls using digital recording technology. The recording unit can also record on paper. Furthermore, the recording unit can record in a database. This improves the accuracy of score and foul recording by basing it on information from the judging unit. Some or all of the above processing in the recording unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recording unit can input information from the judging unit into a generation AI and have the generation AI record scores and fouls.

[0038] The analysis unit can improve the accuracy of its analysis by integrating video footage from different camera angles when analyzing match video data. For example, the analysis unit can integrate video footage from different camera angles in real time to accurately analyze player movements. The analysis unit can also integrate video footage from different camera angles to accurately analyze the position of the ball. The analysis unit can also integrate video footage from different camera angles to accurately analyze the progress of the match. This improves the accuracy of the analysis by integrating video footage from different camera angles. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input video data from different camera angles into a generative AI and have the generative AI perform the integration and analysis of the video.

[0039] The analysis unit can estimate the players' physical strength and fatigue levels when analyzing video data from a match, and correct the analysis results based on these estimates. For example, the analysis unit can analyze the speed and performance of the players' movements to estimate their physical strength and fatigue levels. The analysis unit can also analyze the players' heart rate and respiratory rate to estimate their physical strength and fatigue levels. The analysis unit can also analyze the players' facial expressions and movements to estimate their physical strength and fatigue levels. This improves the accuracy of the analysis results by taking into account the players' physical strength and fatigue levels. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input player movement data into a generative AI and have the generative AI perform the estimation of physical strength and fatigue levels and the correction of the analysis results.

[0040] The analysis unit can adjust its analysis algorithm when analyzing video data of a match, taking into account weather and lighting conditions. For example, in rainy weather, the analysis unit can apply an algorithm to remove noise from the video. For example, in nighttime matches, the analysis unit can also adjust the brightness of the video according to the lighting conditions. For example, in indoor matches, the analysis unit can also adjust its analysis algorithm to take into account the reflection of lighting. This improves the accuracy of the analysis by taking weather and lighting conditions into account. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input weather and lighting condition data into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0041] The analysis unit can improve the accuracy of its analysis by referring to the player's past performance data when analyzing video data of a match. For example, the analysis unit can analyze the player's current performance based on the player's past match data. The analysis unit can also analyze the player's current movements by referring to the player's past movement patterns. The analysis unit can also analyze the current scoring scene based on the player's past scoring data. This improves the accuracy of the analysis by referring to the player's past performance data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the player's past performance data into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0042] The recognition unit can improve recognition accuracy by considering the color and design of the player's uniform when recognizing the player's movement and the ball's position. For example, the recognition unit can identify the color of the player's uniform and recognize their movement. The recognition unit can also identify the design of the player's uniform and recognize their movement. For example, the recognition unit can identify the number on the player's uniform and recognize their movement. This improves recognition accuracy by considering the color and design of the player's uniform. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input data on the color and design of the player's uniform into a generative AI and have the generative AI perform the task of improving the accuracy of movement recognition.

[0043] The recognition unit can improve recognition accuracy by taking into account the player's height and build when recognizing the player's movements and the ball's position. For example, the recognition unit recognizes movements based on the player's height. The recognition unit can also recognize movements based on the player's build. The recognition unit can also recognize movements based on the player's weight. This improves recognition accuracy by taking into account the player's height and build. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input data on the player's height and build into a generative AI and have the generative AI perform the task of improving the accuracy of movement recognition.

[0044] The recognition unit can adjust its recognition algorithm while considering the progress of the match when recognizing player movements and the position of the ball. For example, in the early stages of the match, the recognition unit can recognize general changes in player movements. For example, in the middle stages of the match, the recognition unit can also recognize subtle changes in player movements. For example, in the later stages of the match, the recognition unit can also recognize rapid changes in player movements. This improves recognition accuracy by considering the progress of the match. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input match progress data into a generative AI and have the generative AI perform adjustments to the recognition algorithm.

[0045] The recognition unit can improve recognition accuracy by referring to the player's past movement data when recognizing the player's movements and the ball's position. For example, the recognition unit recognizes the player's current movements based on the player's past movement data. The recognition unit can also recognize the player's current movements based on the player's past performance data. The recognition unit can also recognize the player's current movements based on the player's past match data. This improves recognition accuracy by referring to the player's past movement data. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input the player's past movement data into a generative AI and have the generative AI perform the improvement of recognition accuracy.

[0046] The judging unit can adjust its judgment criteria when determining rule violations or scores, taking into account the importance of the match and the audience's reaction. For example, the judging unit may apply strict criteria in important matches. For example, the judging unit may also apply flexible criteria when there is a strong audience reaction. For example, the judging unit may adjust its judgment criteria according to the progress of the match. This improves the fairness of the judgment by taking into account the importance of the match and the audience's reaction. Some or all of the above processing in the judging unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the judging unit can input data on the importance of the match and the audience's reaction into a generative AI and have the generative AI perform the adjustment of the judgment criteria.

[0047] The judgment unit can improve its judgment accuracy by referring to the player's past behavior patterns when determining rule violations and scores. For example, the judgment unit determines the current action based on the player's past behavior patterns. The judgment unit can also determine the current action based on the player's past match data. The judgment unit can also determine the current action based on the player's past performance data. This improves the judgment accuracy by referring to the player's past behavior patterns. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input data on the player's past behavior patterns into a generative AI and have the generative AI perform the improvement of judgment accuracy.

[0048] The judging unit can adjust its judgment algorithm considering the progress of the match when determining rule violations and scores. For example, the judging unit may apply strict criteria in the early stages of the match. For example, it may apply flexible criteria in the middle stages of the match. For example, it may apply rapid criteria in the later stages of the match. This improves the accuracy of judgments by considering the progress of the match. Some or all of the above processing in the judging unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the judging unit can input match progress data into a generative AI and have the generative AI adjust the judgment algorithm.

[0049] The judgment unit can improve its judgment accuracy by referring to the player's past performance data when determining rule violations and scores. For example, the judgment unit determines the current action based on the player's past performance data. The judgment unit can also determine the current action based on the player's past match data. The judgment unit can also determine the current action based on the player's past action patterns. This improves the judgment accuracy by referring to the player's past performance data. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input the player's past performance data into a generative AI and have the generative AI perform the improvement of judgment accuracy.

[0050] The recording unit can adjust its recording method when recording scores and fouls, taking into account the progress of the game and the reactions of the spectators. For example, the recording unit may simplify the recording of scores and fouls in the early stages of the game. For example, the recording unit may perform detailed recording of scores and fouls in the middle stages of the game. For example, the recording unit may highlight important scores and fouls when there is a strong reaction from the spectators. This improves the accuracy of the recording by taking into account the progress of the game and the reactions of the spectators. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recording unit may input data on the progress of the game and the reactions of the spectators into a generative AI and have the generative AI perform adjustments to the recording method.

[0051] The recording unit can improve recording accuracy by referring to the player's past behavior patterns when recording scores and fouls. For example, the recording unit can record the current score based on the player's past scoring data. The recording unit can also record the current foul based on the player's past foul data. The recording unit can also record the current score and fouls based on the player's past match data. This improves recording accuracy by referring to the player's past behavior patterns. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input data on the player's past behavior patterns into a generative AI and have the generative AI perform the improvement of recording accuracy.

[0052] The recording unit can adjust its recording algorithm when recording scores and fouls, taking into account the progress of the game. For example, the recording unit may simplify the recording of scores and fouls in the early stages of the game. For example, it may perform detailed recording of scores and fouls in the middle stages of the game. For example, it may highlight important scores and fouls in the later stages of the game. This improves the accuracy of the recording by taking into account the progress of the game. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recording unit may input data on the progress of the game into a generative AI and have the generative AI perform adjustments to the recording algorithm.

[0053] The recording unit can improve recording accuracy by referring to the player's past performance data when recording scores and fouls. For example, the recording unit can record the current score based on the player's past scoring data. The recording unit can also record the current foul based on the player's past foul data. The recording unit can also record the current score and fouls based on the player's past match data. This improves recording accuracy by referring to the player's past performance data. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input the player's past performance data into a generative AI and have the generative AI perform the improvement of recording accuracy.

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

[0055] The analysis unit learns player movement patterns in real time when analyzing match video data and can predict movements during the match. For example, the analysis unit predicts current movements based on past movement data of players. The analysis unit can also predict next movements by analyzing the speed and direction of players' movements. For example, the analysis unit can predict the movements of the entire team by analyzing the relative positions of players. By predicting player movements, the progress of the match can be understood more accurately.

[0056] The recognition unit can improve recognition accuracy by considering the player's body temperature and sweating rate when recognizing the player's movements and the ball's position. For example, the recognition unit recognizes movement based on the player's body temperature. The recognition unit can also recognize movement based on the player's sweating rate. For example, the recognition unit can combine data on the player's body temperature and sweating rate to recognize movement. This improves recognition accuracy by considering the player's body temperature and sweating rate.

[0057] The scorekeeping unit can adjust its recording methods for scores and fouls, taking into account the progress of the game and the reactions of the spectators. For example, the scorekeeping unit can simplify the recording of scores and fouls in the early stages of the game. For example, the scorekeeping unit can perform detailed recording of scores and fouls in the middle stages of the game. For example, the scorekeeping unit can highlight important scores and fouls when there is a strong reaction from the spectators. This improves the accuracy of the recording by taking into account the progress of the game and the reactions of the spectators.

[0058] The analysis unit can estimate the players' physical strength and fatigue levels when analyzing match video data, and correct the analysis results based on this. For example, the analysis unit can analyze the speed and performance of the players' movements to estimate their physical strength and fatigue levels. For example, the analysis unit can also analyze the players' heart rate and respiratory rate to estimate their physical strength and fatigue levels. For example, the analysis unit can analyze the players' facial expressions and movements to estimate their physical strength and fatigue levels. By taking into account the players' physical strength and fatigue levels, the accuracy of the analysis results is improved.

[0059] The analysis unit can adjust its analysis algorithm when analyzing match video data, taking into account weather and lighting conditions. For example, in rainy weather, the analysis unit applies an algorithm to remove noise from the video. For example, in nighttime matches, the analysis unit can adjust the brightness of the video according to the lighting conditions. For example, in indoor matches, the analysis unit can adjust its analysis algorithm to take into account lighting reflections. This improves the accuracy of the analysis by taking weather and lighting conditions into consideration.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The analysis unit analyzes the match video. For example, the analysis unit receives the match video data and uses machine learning techniques to analyze the players' movements and the ball's position. The analysis unit uses deep learning techniques, support vector machines, and image analysis techniques to analyze the video data and identify the players' movements and the ball's position. Step 2: The recognition unit recognizes the player's movements and the ball's position based on the data analyzed by the analysis unit. The recognition unit identifies the player's uniform number and the ball's color, and tracks the player's movements and the ball's position based on this. The recognition unit recognizes the player's movements and the ball's position using image recognition technology, motion recognition technology, and position recognition technology. Step 3: The judgment unit determines rule violations and scores based on the information recognized by the recognition unit. The judgment unit detects when a player steps on the line or when the ball enters the goal, and determines rule violations and scores based on these. The judgment unit makes decisions based on the criteria for rule violations, scoring criteria, and foul criteria. Step 4: The recording unit records scores and fouls based on the information determined by the judging unit. The recording unit records scores and fouls in digital format. In addition to recording scores and fouls using digital recording technology, the recording unit can also record on paper or in a database.

[0062] (Example of form 2) The basketball game recognition system according to an embodiment of the present invention is a system that recognizes a basketball game and automatically acts as both referee and scorer. This system analyzes game footage in real time to recognize player movements and the position of the ball. Next, based on the recognized information, it determines rule violations and scores. It also automatically records scores and fouls. This mechanism improves the fairness of the game and reduces the burden on referees and scorers. For example, the basketball game recognition system analyzes game footage in real time. In this process, the system uses machine learning techniques to accurately recognize player movements and the position of the ball. For example, the system identifies the numbers on the players' uniforms and the color of the ball, and tracks the players' movements and the position of the ball based on this. This allows for an accurate understanding of the progress of the game. Next, based on the recognized information, the system performs the role of a referee. Specifically, the system determines rule violations and scores. For example, the system detects when a player steps on the line or when the ball enters the goal, and determines rule violations and scores based on these. This improves the fairness of the game. Furthermore, the system also functions as a scorer. Specifically, the system automatically records scores and fouls. For example, the system detects when a player scores a point or commits a foul, and records the score or foul accordingly. This reduces the burden on the scorer. This mechanism improves the fairness of the game and reduces the burden on referees and scorers. For instance, by having the system act as a referee, rule violations and scoring decisions during the game are made quickly and accurately. Also, by having the system function as a scorer, the recording of points and fouls is done automatically, reducing the burden on the scorer. This makes the game run more smoothly and improves satisfaction for both spectators and players. In short, a basketball game recognition system can improve the fairness of the game and reduce the burden on referees and scorers.

[0063] The basketball game recognition system according to this embodiment comprises an analysis unit, a recognition unit, a judgment unit, and a recording unit. The analysis unit analyzes video footage of the game. The analysis unit, for example, receives video data of the game and analyzes the movements of the players and the position of the ball using machine learning techniques. The analysis unit, for example, analyzes the video data using deep learning techniques to identify the movements of the players and the position of the ball. The analysis unit can also analyze the video data using support vector machines to recognize the movements of the players and the position of the ball. Furthermore, the analysis unit can also analyze the video data using image analysis techniques to identify the movements of the players and the position of the ball. The recognition unit recognizes the movements of the players and the position of the ball based on the data analyzed by the analysis unit. The recognition unit, for example, identifies the numbers on the players' uniforms and the color of the ball, and tracks the movements of the players and the position of the ball based on this. The recognition unit, for example, uses image recognition techniques to recognize the movements of the players and the position of the ball. The recognition unit can also recognize the movements of the players using motion recognition techniques. Furthermore, the recognition unit can also recognize the position of the ball using position recognition techniques. The judgment unit determines rule violations and scores based on the information recognized by the recognition unit. The judgment unit detects, for example, when a player steps on the line or when the ball enters the goal, and determines rule violations and scores based on these. The judgment unit makes judgments based on criteria for rule violations, for example. The judgment unit can also make judgments based on criteria for scores. Furthermore, the judgment unit can also make judgments based on criteria for fouls. The recording unit records scores and fouls based on the information determined by the judgment unit. The recording unit records scores and fouls in digital format, for example. The recording unit records scores and fouls using digital recording technology, for example. The recording unit can also record on paper. Furthermore, the recording unit can record in a database. As a result, the basketball game recognition system can analyze game footage, recognize player movements and ball positions, determine rule violations and scores, and record scores and fouls.

[0064] The analysis unit analyzes the match footage. For example, the analysis unit receives match video data and uses machine learning techniques to analyze player movements and ball positions. Specifically, match video data is acquired in real time from high-resolution cameras and drones and transmitted to a central data server. The analysis unit uses deep learning techniques to analyze the video data and identify player movements and ball positions. Deep learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used, enabling high-precision recognition of complex patterns and movements within the video. Furthermore, it is also possible to analyze the video data using support vector machines (SVMs) to recognize player movements and ball positions. Because SVMs classify data based on specific features, they can quickly and accurately identify player movements and ball positions. By combining these techniques, the analysis unit can analyze the match video data from multiple angles and identify player movements and ball positions with high precision. In addition, the analysis unit can also use image analysis techniques to analyze the video data and identify player movements and ball positions. The image analysis technology utilizes optical flow and object detection algorithms, enabling real-time tracking of movement and position within the video. This allows the analysis unit to analyze the match video data with high accuracy, quickly and accurately identifying player movements and the ball's position.

[0065] The recognition unit recognizes the players' movements and the ball's position based on the data analyzed by the analysis unit. For example, the recognition unit identifies the players' uniform numbers and the ball's color, and tracks the players' movements and the ball's position based on that. Specifically, the recognition unit uses image recognition technology to identify the players' uniform numbers and the ball's color, and tracks the players' movements and the ball's position based on that. Image recognition technology employs object detection algorithms and pattern recognition technologies, enabling high-precision identification of players' uniform numbers and ball colors. The recognition unit can also recognize players' movements using motion recognition technology. Motion recognition technology employs posture estimation algorithms and motion classification algorithms, enabling high-precision recognition of players' movements. Furthermore, the recognition unit can also recognize the ball's position using position recognition technology. Position recognition technology employs tracking algorithms and position estimation algorithms, enabling high-precision recognition of the ball's position. As a result, the recognition unit can recognize players' movements and the ball's position with high precision based on the data analyzed by the analysis unit, and grasp the game situation in real time.

[0066] The judgment unit determines rule violations and scores based on information recognized by the recognition unit. For example, the judgment unit detects when a player steps on the line or when the ball goes into the goal, and determines rule violations and scores based on these. Specifically, the judgment unit makes judgments based on the criteria for rule violations. For example, it detects when a player steps on the line or when the ball goes out of bounds, and determines a rule violation based on these. The judgment unit can also make judgments based on the criteria for scores. For example, it detects when the ball goes into the goal or when a free throw is successful, and determines a score based on these. Furthermore, the judgment unit can also make judgments based on the criteria for fouls. For example, it detects when a player makes illegal contact with an opposing player or performs an action that violates the rules, and determines a foul based on these. As a result, the judgment unit can make highly accurate judgments on rule violations and scores based on information recognized by the recognition unit, and can fairly manage the progress of the game. Furthermore, the judgment unit can improve the accuracy of its judgments using AI. For example, it can make more accurate judgments by learning from past game data. This allows the judging panel to manage the progress of the match fairly and accurately, thereby improving the reliability of the match.

[0067] The recording department records scores and fouls based on the information determined by the refereeing department. For example, the recording department records scores and fouls digitally. Specifically, the recording department uses digital recording technology to record scores and fouls. This digital recording technology utilizes database management systems and cloud storage, enabling efficient and accurate recording of scores and fouls. The recording department can also record on paper. For example, it can record scores and foul information on paper as the official match record for later review. Furthermore, the recording department can record in a database. The database stores detailed match information and statistical data for later analysis and review. This allows the recording department to efficiently and accurately record scores and fouls based on the information determined by the refereeing department, and to centrally manage match records. Additionally, the recording department can always record the progress of the match in real time based on real-time updated data. For example, it can instantly record the moment a score or foul occurs in the database, allowing for real-time monitoring of the match's progress. Furthermore, the recording unit can automatically generate statistical data for matches, which can be used for post-match analysis and reporting. This allows the recording unit to efficiently and accurately record matches, grasp the progress of matches in real time, and utilize this data for post-match analysis and reporting.

[0068] The analysis unit can analyze match video data using machine learning techniques. For example, the analysis unit can analyze video data using deep learning techniques to identify player movements and ball positions. The analysis unit can also analyze video data using support vector machines to recognize player movements and ball positions. The analysis unit can also analyze video data using image analysis techniques to identify player movements and ball positions. This improves the accuracy of the analysis of match video data by using machine learning techniques. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input match video data into a generative AI and have the generative AI perform the analysis of player movements and ball positions.

[0069] The recognition unit can recognize the player's movements and the ball's position based on data from the analysis unit. For example, the recognition unit can identify the player's uniform number or the ball's color and track the player's movements and the ball's position based on that. The recognition unit can recognize the player's movements and the ball's position using, for example, image recognition technology. The recognition unit can also recognize the player's movements using motion recognition technology. Furthermore, the recognition unit can recognize the ball's position using position recognition technology. This improves the accuracy of recognizing the player's movements and the ball's position by using data from the analysis unit. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input data from the analysis unit into a generative AI and have the generative AI perform the recognition of the player's movements and the ball's position.

[0070] The judgment unit can determine rule violations and scores based on information from the recognition unit. For example, the judgment unit can detect when a player steps on the line or when the ball enters the goal, and determine rule violations and scores based on these. The judgment unit makes judgments based on criteria for rule violations, for example. The judgment unit can also make judgments based on criteria for scores. Furthermore, the judgment unit can make judgments based on criteria for fouls. This improves the accuracy of rule violation and score determinations by using information from the recognition unit. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input information from the recognition unit into a generative AI and have the generative AI perform rule violation and score determinations.

[0071] The recording unit can record scores and fouls based on information from the judging unit. The recording unit can, for example, record scores and fouls in digital format. The recording unit can, for example, record scores and fouls using digital recording technology. The recording unit can also record on paper. Furthermore, the recording unit can record in a database. This improves the accuracy of score and foul recording by basing it on information from the judging unit. Some or all of the above processing in the recording unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recording unit can input information from the judging unit into a generation AI and have the generation AI record scores and fouls.

[0072] The analysis unit can estimate the emotions of the audience and adjust the priority of the match video analysis based on the estimated emotions. For example, if the audience is excited, the analysis unit may prioritize analyzing important play scenes. For example, if the audience is bored, the analysis unit may also prioritize analyzing the highlight scenes of the match. For example, if the audience is tense, the analysis unit may also prioritize analyzing the decisive moments of the match. By adjusting the priority of the match video analysis based on the emotions of the audience, it becomes easier to capture the audience's interest. The estimation of audience emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit may input audience facial expression data into a generative AI and have the generative AI perform the estimation of audience emotions.

[0073] The analysis unit can improve the accuracy of its analysis by integrating video footage from different camera angles when analyzing match video data. For example, the analysis unit can integrate video footage from different camera angles in real time to accurately analyze player movements. The analysis unit can also integrate video footage from different camera angles to accurately analyze the position of the ball. The analysis unit can also integrate video footage from different camera angles to accurately analyze the progress of the match. This improves the accuracy of the analysis by integrating video footage from different camera angles. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input video data from different camera angles into a generative AI and have the generative AI perform the integration and analysis of the video.

[0074] The analysis unit can estimate the players' physical strength and fatigue levels when analyzing video data from a match, and correct the analysis results based on these estimates. For example, the analysis unit can analyze the speed and performance of the players' movements to estimate their physical strength and fatigue levels. The analysis unit can also analyze the players' heart rate and respiratory rate to estimate their physical strength and fatigue levels. The analysis unit can also analyze the players' facial expressions and movements to estimate their physical strength and fatigue levels. This improves the accuracy of the analysis results by taking into account the players' physical strength and fatigue levels. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input player movement data into a generative AI and have the generative AI perform the estimation of physical strength and fatigue levels and the correction of the analysis results.

[0075] The analysis unit can estimate the audience's emotions and adjust the display method of the analysis results based on the estimated audience emotions. For example, if the audience is excited, the analysis unit can highlight important play scenes. For example, if the audience is bored, the analysis unit can also display highlight scenes of the match. For example, if the audience is tense, the analysis unit can also highlight decisive moments of the match. By adjusting the display method of the analysis results based on the audience's emotions, it becomes easier to capture the audience's interest. The estimation of audience emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input audience facial expression data into a generative AI and have the generative AI perform the estimation of audience emotions and the adjustment of the display method.

[0076] The analysis unit can adjust its analysis algorithm when analyzing video data of a match, taking into account weather and lighting conditions. For example, in rainy weather, the analysis unit can apply an algorithm to remove noise from the video. For example, in nighttime matches, the analysis unit can also adjust the brightness of the video according to the lighting conditions. For example, in indoor matches, the analysis unit can also adjust its analysis algorithm to take into account the reflection of lighting. This improves the accuracy of the analysis by taking weather and lighting conditions into account. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input weather and lighting condition data into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0077] The analysis unit can improve the accuracy of its analysis by referring to the player's past performance data when analyzing video data of a match. For example, the analysis unit can analyze the player's current performance based on the player's past match data. The analysis unit can also analyze the player's current movements by referring to the player's past movement patterns. The analysis unit can also analyze the current scoring scene based on the player's past scoring data. This improves the accuracy of the analysis by referring to the player's past performance data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the player's past performance data into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0078] The recognition unit can estimate the player's emotions and adjust the accuracy of movement recognition based on the estimated emotions. For example, if the player is tense, the recognition unit can recognize subtle changes in movement. For example, if the player is relaxed, the recognition unit can also recognize broader changes in movement. For example, if the player is excited, the recognition unit can also recognize rapid changes in movement. By adjusting the accuracy of movement recognition based on the player's emotions, the recognition accuracy is improved. The estimation of the player's emotions is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using a generative AI, or not using a generative AI. For example, the recognition unit can input the player's facial expression data into a generative AI and have the generative AI perform the estimation of the player's emotions and the adjustment of the accuracy of movement recognition.

[0079] The recognition unit can improve recognition accuracy by considering the color and design of the player's uniform when recognizing the player's movement and the ball's position. For example, the recognition unit can identify the color of the player's uniform and recognize their movement. The recognition unit can also identify the design of the player's uniform and recognize their movement. For example, the recognition unit can identify the number on the player's uniform and recognize their movement. This improves recognition accuracy by considering the color and design of the player's uniform. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input data on the color and design of the player's uniform into a generative AI and have the generative AI perform the task of improving the accuracy of movement recognition.

[0080] The recognition unit can improve recognition accuracy by taking into account the player's height and build when recognizing the player's movements and the ball's position. For example, the recognition unit recognizes movements based on the player's height. The recognition unit can also recognize movements based on the player's build. The recognition unit can also recognize movements based on the player's weight. This improves recognition accuracy by taking into account the player's height and build. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input data on the player's height and build into a generative AI and have the generative AI perform the task of improving the accuracy of movement recognition.

[0081] The recognition unit can estimate the player's emotions and adjust the display method of the recognition results based on the estimated emotions of the player. For example, if the player is tense, the recognition unit can provide a simple and highly visible display method. For example, if the player is relaxed, the recognition unit can also provide a display method that includes detailed information. For example, if the player is excited, the recognition unit can also provide a visually stimulating display method. By adjusting the display method of the recognition results based on the player's emotions, the visibility of the recognition results is improved. The estimation of the player's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using a generative AI, or not using a generative AI. For example, the recognition unit can input the player's facial expression data into a generative AI and have the generative AI perform the estimation of the player's emotions and the adjustment of the display method.

[0082] The recognition unit can adjust its recognition algorithm while considering the progress of the match when recognizing player movements and the position of the ball. For example, in the early stages of the match, the recognition unit can recognize general changes in player movements. For example, in the middle stages of the match, the recognition unit can also recognize subtle changes in player movements. For example, in the later stages of the match, the recognition unit can also recognize rapid changes in player movements. This improves recognition accuracy by considering the progress of the match. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input match progress data into a generative AI and have the generative AI perform adjustments to the recognition algorithm.

[0083] The recognition unit can improve recognition accuracy by referring to the player's past movement data when recognizing the player's movements and the ball's position. For example, the recognition unit recognizes the player's current movements based on the player's past movement data. The recognition unit can also recognize the player's current movements based on the player's past performance data. The recognition unit can also recognize the player's current movements based on the player's past match data. This improves recognition accuracy by referring to the player's past movement data. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input the player's past movement data into a generative AI and have the generative AI perform the improvement of recognition accuracy.

[0084] The judging unit can estimate the referee's emotions and adjust the criteria for rule violations and scoring based on the estimated emotions. For example, if the referee is tense, the judging unit may apply strict criteria. For example, if the referee is relaxed, the judging unit may apply flexible criteria. For example, if the referee is excited, the judging unit may apply rapid criteria. This improves the fairness of judgments by adjusting the criteria based on the referee's emotions. The estimation of the referee's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judging unit may be performed using a generative AI, or not using a generative AI. For example, the judging unit can input the referee's facial expression data into a generative AI and have the generative AI perform the estimation of the referee's emotions and the adjustment of the criteria.

[0085] The judging unit can adjust its judgment criteria when determining rule violations or scores, taking into account the importance of the match and the audience's reaction. For example, the judging unit may apply strict criteria in important matches. For example, the judging unit may also apply flexible criteria when there is a strong audience reaction. For example, the judging unit may adjust its judgment criteria according to the progress of the match. This improves the fairness of the judgment by taking into account the importance of the match and the audience's reaction. Some or all of the above processing in the judging unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the judging unit can input data on the importance of the match and the audience's reaction into a generative AI and have the generative AI perform the adjustment of the judgment criteria.

[0086] The judgment unit can improve its judgment accuracy by referring to the player's past behavior patterns when determining rule violations and scores. For example, the judgment unit determines the current action based on the player's past behavior patterns. The judgment unit can also determine the current action based on the player's past match data. The judgment unit can also determine the current action based on the player's past performance data. This improves the judgment accuracy by referring to the player's past behavior patterns. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input data on the player's past behavior patterns into a generative AI and have the generative AI perform the improvement of judgment accuracy.

[0087] The judgment unit can estimate the referee's emotions and adjust the display method of the judgment result based on the estimated emotions of the referee. For example, if the referee is tense, the judgment unit may provide a simple and highly visible display method. For example, if the referee is relaxed, the judgment unit may also provide a display method that includes detailed information. For example, if the referee is excited, the judgment unit may also provide a visually stimulating display method. This improves visibility by adjusting the display method of the judgment result based on the referee's emotions. The estimation of the referee's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using a generative AI, or not using a generative AI. For example, the judgment unit may input the referee's facial expression data into a generative AI and have the generative AI perform the estimation of the referee's emotions and the adjustment of the display method.

[0088] The judging unit can adjust its judgment algorithm considering the progress of the match when determining rule violations and scores. For example, the judging unit may apply strict criteria in the early stages of the match. For example, it may apply flexible criteria in the middle stages of the match. For example, it may apply rapid criteria in the later stages of the match. This improves the accuracy of judgments by considering the progress of the match. Some or all of the above processing in the judging unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the judging unit can input match progress data into a generative AI and have the generative AI adjust the judgment algorithm.

[0089] The judgment unit can improve its judgment accuracy by referring to the player's past performance data when determining rule violations and scores. For example, the judgment unit determines the current action based on the player's past performance data. The judgment unit can also determine the current action based on the player's past match data. The judgment unit can also determine the current action based on the player's past action patterns. This improves the judgment accuracy by referring to the player's past performance data. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input the player's past performance data into a generative AI and have the generative AI perform the improvement of judgment accuracy.

[0090] The recording unit can estimate the scorer's emotions and adjust the scoring and foul recording methods based on the estimated emotions. For example, if the scorer is nervous, the recording unit can provide a simple and highly visible recording method. For example, if the scorer is relaxed, the recording unit can also provide a recording method that includes detailed information. For example, if the scorer is excited, the recording unit can also provide a visually stimulating recording method. This improves the visibility of the records by adjusting the recording method based on the scorer's emotions. The estimation of the scorer's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using a generative AI, or not. For example, the recording unit can input the scorer's facial expression data into a generative AI and have the generative AI perform the estimation of the scorer's emotions and the adjustment of the recording method.

[0091] The recording unit can adjust its recording method when recording scores and fouls, taking into account the progress of the game and the reactions of the spectators. For example, the recording unit may simplify the recording of scores and fouls in the early stages of the game. For example, the recording unit may perform detailed recording of scores and fouls in the middle stages of the game. For example, the recording unit may highlight important scores and fouls when there is a strong reaction from the spectators. This improves the accuracy of the recording by taking into account the progress of the game and the reactions of the spectators. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recording unit may input data on the progress of the game and the reactions of the spectators into a generative AI and have the generative AI perform adjustments to the recording method.

[0092] The recording unit can improve recording accuracy by referring to the player's past behavior patterns when recording scores and fouls. For example, the recording unit can record the current score based on the player's past scoring data. The recording unit can also record the current foul based on the player's past foul data. The recording unit can also record the current score and fouls based on the player's past match data. This improves recording accuracy by referring to the player's past behavior patterns. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input data on the player's past behavior patterns into a generative AI and have the generative AI perform the improvement of recording accuracy.

[0093] The recording unit can estimate the scorer's emotions and adjust the display method of the recording results based on the estimated emotions of the scorer. For example, if the scorer is nervous, the recording unit can provide a simple and highly visible display method. For example, if the scorer is relaxed, the recording unit can also provide a display method that includes detailed information. For example, if the scorer is excited, the recording unit can also provide a visually stimulating display method. This improves visibility by adjusting the display method of the recording results based on the scorer's emotions. The estimation of the scorer's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recording unit may be performed using a generative AI, or not using a generative AI. For example, the recording unit can input the scorer's facial expression data into a generative AI and have the generative AI perform the estimation of the scorer's emotions and the adjustment of the display method.

[0094] The recording unit can adjust its recording algorithm when recording scores and fouls, taking into account the progress of the game. For example, the recording unit may simplify the recording of scores and fouls in the early stages of the game. For example, it may perform detailed recording of scores and fouls in the middle stages of the game. For example, it may highlight important scores and fouls in the later stages of the game. This improves the accuracy of the recording by taking into account the progress of the game. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recording unit may input data on the progress of the game into a generative AI and have the generative AI perform adjustments to the recording algorithm.

[0095] The recording unit can improve recording accuracy by referring to the player's past performance data when recording scores and fouls. For example, the recording unit can record the current score based on the player's past scoring data. The recording unit can also record the current foul based on the player's past foul data. The recording unit can also record the current score and fouls based on the player's past match data. This improves recording accuracy by referring to the player's past performance data. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input the player's past performance data into a generative AI and have the generative AI perform the improvement of recording accuracy.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit learns player movement patterns in real time when analyzing match video data and can predict movements during the match. For example, the analysis unit predicts current movements based on past movement data of players. The analysis unit can also predict next movements by analyzing the speed and direction of players' movements. For example, the analysis unit can predict the movements of the entire team by analyzing the relative positions of players. By predicting player movements, the progress of the match can be understood more accurately.

[0098] The recognition unit can improve recognition accuracy by considering the player's body temperature and sweating rate when recognizing the player's movements and the ball's position. For example, the recognition unit recognizes movement based on the player's body temperature. The recognition unit can also recognize movement based on the player's sweating rate. For example, the recognition unit can combine data on the player's body temperature and sweating rate to recognize movement. This improves recognition accuracy by considering the player's body temperature and sweating rate.

[0099] The judging unit can estimate the player's psychological state when determining rule violations or scores, and adjust the judgment criteria based on the estimated psychological state. For example, if the player is nervous, the judging unit will apply stricter criteria. For example, if the player is relaxed, the judging unit may apply more flexible criteria. For example, if the player is excited, the judging unit may apply more rapid criteria. This improves the fairness of judgments by adjusting the criteria based on the player's psychological state.

[0100] The scorekeeping unit can adjust its recording methods for scores and fouls, taking into account the progress of the game and the reactions of the spectators. For example, the scorekeeping unit can simplify the recording of scores and fouls in the early stages of the game. For example, the scorekeeping unit can perform detailed recording of scores and fouls in the middle stages of the game. For example, the scorekeeping unit can highlight important scores and fouls when there is a strong reaction from the spectators. This improves the accuracy of the recording by taking into account the progress of the game and the reactions of the spectators.

[0101] The analysis unit can estimate the players' physical strength and fatigue levels when analyzing match video data, and correct the analysis results based on this. For example, the analysis unit can analyze the speed and performance of the players' movements to estimate their physical strength and fatigue levels. For example, the analysis unit can also analyze the players' heart rate and respiratory rate to estimate their physical strength and fatigue levels. For example, the analysis unit can analyze the players' facial expressions and movements to estimate their physical strength and fatigue levels. By taking into account the players' physical strength and fatigue levels, the accuracy of the analysis results is improved.

[0102] The analysis unit can estimate the audience's emotions and adjust the priority of the match video analysis based on those estimated emotions. For example, if the audience is excited, the analysis unit will prioritize analyzing important play scenes. If the audience is bored, the analysis unit can also prioritize analyzing the match's highlight scenes. If the audience is tense, the analysis unit can also prioritize analyzing the decisive moments of the match. By adjusting the priority of the match video analysis based on the audience's emotions, it becomes easier to capture the audience's interest.

[0103] The recognition unit can estimate the player's emotions and adjust the accuracy of movement recognition based on the estimated emotions. For example, if the player is tense, the recognition unit can recognize subtle changes in movement. For example, if the player is relaxed, the recognition unit can also recognize broader changes in movement. For example, if the player is excited, the recognition unit can also recognize rapid changes in movement. By adjusting the accuracy of movement recognition based on the player's emotions, the recognition accuracy is improved.

[0104] The judging unit can estimate the referee's emotions and adjust the criteria for judging rule violations and scoring based on those estimated emotions. For example, if the referee is tense, the judging unit will apply strict criteria. For example, if the referee is relaxed, the judging unit may apply flexible criteria. For example, if the referee is excited, the judging unit may apply rapid criteria. This improves the fairness of judgments by adjusting the criteria based on the referee's emotions.

[0105] The recording unit can estimate the scorer's emotions and adjust the scoring and foul recording methods based on the estimated emotions. For example, if the scorer is nervous, the recording unit can provide a simple and highly visible recording method. For example, if the scorer is relaxed, the recording unit can also provide a recording method that includes detailed information. For example, if the scorer is excited, the recording unit can also provide a visually stimulating recording method. This improves the visibility of the records by adjusting the recording method based on the scorer's emotions.

[0106] The analysis unit can adjust its analysis algorithm when analyzing match video data, taking into account weather and lighting conditions. For example, in rainy weather, the analysis unit applies an algorithm to remove noise from the video. For example, in nighttime matches, the analysis unit can adjust the brightness of the video according to the lighting conditions. For example, in indoor matches, the analysis unit can adjust its analysis algorithm to take into account lighting reflections. This improves the accuracy of the analysis by taking weather and lighting conditions into consideration.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The analysis unit analyzes the match video. For example, the analysis unit receives the match video data and uses machine learning techniques to analyze the players' movements and the ball's position. The analysis unit uses deep learning techniques, support vector machines, and image analysis techniques to analyze the video data and identify the players' movements and the ball's position. Step 2: The recognition unit recognizes the player's movements and the ball's position based on the data analyzed by the analysis unit. The recognition unit identifies the player's uniform number and the ball's color, and tracks the player's movements and the ball's position based on this. The recognition unit recognizes the player's movements and the ball's position using image recognition technology, motion recognition technology, and position recognition technology. Step 3: The judgment unit determines rule violations and scores based on the information recognized by the recognition unit. The judgment unit detects when a player steps on the line or when the ball enters the goal, and determines rule violations and scores based on these. The judgment unit makes decisions based on the criteria for rule violations, scoring criteria, and foul criteria. Step 4: The recording unit records scores and fouls based on the information determined by the judging unit. The recording unit records scores and fouls in digital format. In addition to recording scores and fouls using digital recording technology, the recording unit can also record on paper or in a database.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the analysis unit, recognition unit, judgment unit, and recording unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit acquires video footage of the match using the camera 42 of the smart device 14 and analyzes the video footage using the identification processing unit 290 of the data processing unit 12. The recognition unit recognizes the movements of players and the position of the ball using the control unit 46A of the smart device 14. The judgment unit determines rule violations and scores using the identification processing unit 290 of the data processing unit 12. The recording unit records scores and fouls in the database 24 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the analysis unit, recognition unit, judgment unit, and recording unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit acquires video footage of the match using the camera 42 of the smart glasses 214 and analyzes the video using the identification processing unit 290 of the data processing unit 12. The recognition unit recognizes the movements of players and the position of the ball using the control unit 46A of the smart glasses 214. The judgment unit determines rule violations and scores using the identification processing unit 290 of the data processing unit 12. The recording unit records scores and fouls in the database 24 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the analysis unit, recognition unit, judgment unit, and recording unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit acquires video footage of the match using the camera 42 of the headset terminal 314 and analyzes the video footage using the identification processing unit 290 of the data processing unit 12. The recognition unit recognizes the movements of players and the position of the ball using the control unit 46A of the headset terminal 314. The judgment unit determines rule violations and scores using the identification processing unit 290 of the data processing unit 12. The recording unit records scores and fouls in the database 24 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the analysis unit, recognition unit, judgment unit, and recording unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit acquires video footage of the game using the camera 42 of the robot 414 and analyzes the video footage using the identification processing unit 290 of the data processing unit 12. The recognition unit recognizes the movements of the players and the position of the ball using, for example, the control unit 46A of the robot 414. The judgment unit determines rule violations and scores using, for example, the identification processing unit 290 of the data processing unit 12. The recording unit records scores and fouls in, for example, the database 24 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The analysis unit analyzes the match footage, A recognition unit recognizes the player's movements and the ball's position based on the data analyzed by the aforementioned analysis unit, A judgment unit that determines rule violations and scores based on the information recognized by the recognition unit, The system includes a recording unit that records scores and fouls based on the information determined by the aforementioned determination unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We will use machine learning techniques to analyze video data from the match. The system described in Appendix 1, characterized by the features described herein. (Note 3) The recognition unit, Based on the data from the aforementioned analysis unit, the unit recognizes the player's movements and the ball's position. The system described in Appendix 1, characterized by the features described herein. (Note 4) The determination unit, Based on the information from the aforementioned recognition unit, rule violations and scores are determined. The system described in Appendix 1, characterized by the features described herein. (Note 5) The recording unit is, Based on the information from the aforementioned scoring unit, scores and fouls are recorded. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The system estimates audience emotions and adjusts the priority of video analysis of the match based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, When analyzing match video data, integrating footage from different camera angles improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing match video data, the player's physical condition and fatigue level are estimated, and the analysis results are corrected based on this. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the audience's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing match video data, the analysis algorithm is adjusted to take weather and lighting conditions into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing match video data, we improve the accuracy of the analysis by referring to the players' past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recognition unit, The system estimates the player's emotions and adjusts the accuracy of motion recognition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The recognition unit, When recognizing player movements and ball positions, the system takes into account the color and design of the players' uniforms to improve recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 14) The recognition unit, When recognizing player movements and ball positions, the system takes into account the player's height and build to improve recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The recognition unit, The system estimates the player's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The recognition unit, When recognizing player movements and ball positions, the recognition algorithm is adjusted to take into account the progress of the match. The system described in Appendix 1, characterized by the features described herein. (Note 17) The recognition unit, When recognizing player movements and ball positions, the system improves recognition accuracy by referencing past player movement data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, The system estimates the referee's emotions and adjusts the criteria for judging rule violations and scoring based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, When determining rule violations or scoring, the criteria for judgment are adjusted considering the importance of the match and the reaction of the audience. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, When determining rule violations or scoring, the system improves accuracy by referencing the player's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, The system estimates the referee's emotions and adjusts how the ruling is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, When determining rule violations or scoring, the judging algorithm is adjusted to take into account the progress of the match. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, When judging rule violations or scoring, the system improves accuracy by referencing players' past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recording unit is, The system estimates the scorer's emotions and adjusts how scores and fouls are recorded based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recording unit is, When recording goals and fouls, the recording method is adjusted to take into account the progress of the game and the reactions of the spectators. The system described in Appendix 1, characterized by the features described herein. (Note 26) The recording unit is, When recording scores and fouls, the system improves recording accuracy by referencing the player's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 27) The recording unit is, The system estimates the scorer's emotions and adjusts how the results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The recording unit is, When recording goals and fouls, the recording algorithm is adjusted to take into account the progress of the game. The system described in Appendix 1, characterized by the features described herein. (Note 29) The recording unit is, When recording scores and fouls, the system improves recording accuracy by referencing the player's past performance data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes the match footage, A recognition unit recognizes the player's movements and the ball's position based on the data analyzed by the aforementioned analysis unit, A judgment unit that determines rule violations and scores based on the information recognized by the recognition unit, The system includes a recording unit that records scores and fouls based on the information determined by the aforementioned determination unit. A system characterized by the following features.

2. The aforementioned analysis unit, We will use machine learning techniques to analyze video data from the match. The system according to feature 1.

3. The aforementioned recognition unit, Based on the data from the aforementioned analysis unit, the unit recognizes the player's movements and the ball's position. The system according to feature 1.

4. The determination unit, Based on the information from the aforementioned recognition unit, rule violations and scores are determined. The system according to feature 1.

5. The aforementioned recording unit is Based on the information from the aforementioned scoring unit, scores and fouls are recorded. The system according to feature 1.

6. The aforementioned analysis unit, The system estimates audience emotions and adjusts the priority of video analysis of the match based on those estimated emotions. The system according to feature 1.

7. The aforementioned analysis unit, When analyzing match video data, integrating footage from different camera angles improves the accuracy of the analysis. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing match video data, the system estimates the players' physical condition and fatigue levels, and then adjusts the analysis results based on these estimates. The system according to feature 1.

9. The aforementioned analysis unit, It estimates the audience's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing match video data, the analysis algorithm is adjusted to take weather and lighting conditions into consideration. The system according to feature 1.

Images