System

Abstract

The system according to the embodiment is intended to automatically play the role of a referee in a sports practice match.SOLUTION: A system includes a video acquisition unit, an analysis unit, and a determination unit. The video acquisition unit acquires a video of a play. The analyzer analyzes the video of the play acquired by the video acquirer. The determiner performs determination on the basis of the video of the play analyzed by the analyzer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that when inexperienced referees act as referees in sports practice matches, erroneous decisions are likely to occur.

[0005] The system according to the embodiment aims to automatically act as a referee in a sports practice match. [Means for solving the problem]

[0006] The system according to the embodiment includes a video acquisition unit, an analysis unit, and a judgment unit. The video acquisition unit acquires video of a play. The analysis unit analyzes the video of the play acquired by the video acquisition unit. The judgment unit makes a judgment based on the video of the play analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically act as a referee in a sports practice match. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automated refereeing system according to the embodiment of the present invention automatically captures video footage of plays, analyzes it using a generated AI, and makes judgments. This allows even inexperienced people to automatically act as referees.

[0029] The automated refereeing system according to the embodiment includes a video acquisition unit, an analysis unit, and a judgment unit. The video acquisition unit acquires video of a play. For example, the video of a play is captured using a smartphone camera. The video acquisition unit can also acquire video of a play using a fixed camera. The video acquisition unit can also acquire video of a play from the air using a drone. For example, the smartphone camera uses a wide-angle lens to capture the overall picture of the play. The fixed camera uses a high-resolution camera to capture specific areas in detail. The drone has an automatic tracking function to track the movement of the play. The analysis unit analyzes the video of a play acquired by the video acquisition unit. For example, the generation AI analyzes the video of a play using a text generation AI (e.g., LLM). The generation AI can also analyze the video of a play using a multimodal generation AI. The generation AI can also use object recognition technology to analyze the movement and position of objects during play. For example, the text generation AI describes actions in the video as text. The multimodal generation AI combines video and audio for analysis. Object recognition technology identifies the positions of the ball and players during play. The judgement unit makes judgments based on the video of the play analyzed by the analysis unit. For example, the judgement unit judges strikes, balls, and outs based on rules. The judgement unit can also judge offsides and fouls based on rules. The judgement unit can also judge goals and points based on rules. For example, the judgement unit judges strikes and balls based on the strike zone, judges offsides based on the offside line, and judges goals based on the goal line. This allows the automated refereeing system of the embodiment to automatically perform the role of a referee, even for inexperienced people. For example, the output unit displays the judgment results via a smartphone app. By displaying the judgment results in real time, the judgement can be immediately confirmed during play. The judgment results can also be saved and reviewed later. The judgment results can also be shared to share information with other participants.

[0030] The judgment unit analyzes the position of the ball and the movements of players in baseball games and can make judgments such as strike, ball, and out. In the judgment unit, for example, the generation AI analyzes video of the play and learns the movement patterns of players. For example, in a basketball game, it learns dribbling movements and automatically judges double dribbles and traveling. In addition, the judgment unit analyzes video of a soccer game and learns the movement patterns of players. For example, it learns tackling movements and automatically judges whether or not it is a foul. In addition, the judgment unit analyzes video of a tennis game and learns the movement patterns of players. For example, it learns serving movements and automatically judges foot faults. This enables accurate judgments in baseball games.

[0031] The judgment unit can analyze the position of the ball and the movements of players in a soccer game and make decisions such as offside and fouls. For example, the judgment unit uses a generation AI to analyze video of a play and estimate a player's physical strength and fatigue level from their movements and facial expressions. For example, in a basketball game, the judgment unit can suggest a rest if a player's movements become sluggish. The judgment unit can also use a generation AI to analyze video of a soccer game and estimate a player's fatigue level from the distance they run and the number of times they sprint. For example, the judgment unit can suggest a rest to a player who has run a certain distance. The judgment unit can also use a generation AI to analyze video of a tennis game and estimate a player's physical strength and fatigue level from their movements and facial expressions. For example, the judgment unit can suggest a rest to a player who has conceded consecutive points. This enables accurate judgment in soccer games.

[0032] The judgment unit can accurately recognize subtle movements and changes in position during play and make appropriate judgments. For example, the judgment unit uses a generation AI to analyze video of a play and estimate the emotional state of a player from their facial expressions and movements. For example, in a basketball game, the judgment unit evaluates the impact of a player feeling anger or impatience. The judgment unit also uses a generation AI to analyze video of a soccer game and estimate the emotional state of a player from their facial expressions and movements. For example, the judgment unit evaluates the impact of a player feeling nervous. The judgment unit also uses a generation AI to analyze video of a tennis game and estimate the emotional state of a player from their facial expressions and movements. For example, the judgment unit evaluates the impact of a player feeling depressed. This allows the judgment unit to accurately recognize subtle movements and changes in position during play and make appropriate judgments.

[0033] The judgement unit displays the strike zone in a baseball game and can judge strikes and balls in real time. For example, when the generation AI analyzes video of a play, the judgement unit can simultaneously learn rule sets for different sports, allowing it to handle multiple sports. For example, the generation AI can simultaneously learn rule sets for basketball and soccer, allowing it to handle both sports. For example, it can judge traveling in a basketball game and offside in a soccer game. The judgement unit can also simultaneously learn rule sets for tennis and volleyball, allowing it to handle both sports. For example, it can judge foot faults in a tennis game and net touches in a volleyball game. The judgement unit can also simultaneously learn rule sets for baseball and cricket, allowing it to handle both sports. For example, it can judge strikes in a baseball game and no-balls in a cricket game. This allows the strike zone to be displayed in a baseball game and strikes and balls to be judged in real time.

[0034] The judgment unit displays the offside line in a soccer game and can make offside judgments in real time. For example, when the generation AI analyzes video of a play, the judgment unit analyzes the reactions of spectators and evaluates the excitement and satisfaction of the spectators. For example, the generation AI analyzes video of a play and estimates the excitement and satisfaction from the facial expressions and movements of the spectators. For example, in a basketball game, the excitement level is evaluated if the spectators are cheering. Furthermore, the judgment unit analyzes video of a soccer game and estimates the excitement and satisfaction from the facial expressions and movements of the spectators. For example, the satisfaction level is evaluated if the spectators are clapping. Furthermore, the judgment unit analyzes video of a tennis game and estimates the excitement and satisfaction from the facial expressions and movements of the spectators. For example, the excitement level is evaluated if the spectators are standing. This allows the offside line to be displayed in a soccer game and offside judgments to be made in real time.

[0035] The judgment unit can be equipped with a function to explain why a certain call was made during play. For example, the judgment unit uses a generation AI to analyze video of a play, analyze the emotional state of the spectators, and evaluate the excitement of the game in real time. For example, the generation AI analyzes video of a play and infers the emotional state from the spectators' facial expressions and actions. For example, in a basketball game, if the spectators are happy, the excitement of the game is evaluated. In addition, the judgment unit uses a generation AI to analyze video of a soccer game and infers the emotional state from the spectators' facial expressions and actions. For example, if the spectators are surprised, the excitement of the game is evaluated. In addition, the judgment unit uses a generation AI to analyze video of a tennis game and infers the emotional state from the spectators' facial expressions and actions. For example, if the spectators are sad, the excitement of the game is evaluated. This makes it easier for participants to understand the rules by explaining why a certain call was made during play.

[0036] The analysis unit learns the movement patterns of players and can automatically determine whether a specific movement violates the rules. For example, the generation AI of the analysis unit analyzes video of a play and learns the movement patterns of players. For example, in a basketball game, it learns dribbling movements and automatically determines double dribbles and traveling. The analysis unit also analyzes video of a soccer game and learns the movement patterns of players. For example, it learns tackling movements and automatically determines whether they are fouls. The analysis unit also analyzes video of a tennis game and learns the movement patterns of players. For example, it learns serving movements and automatically determines foot faults. In this way, it is possible to learn the movement patterns of players and automatically determine whether a specific movement violates the rules.

[0037] The analysis unit can simultaneously learn rule sets for different sports, enabling it to support multiple sports. For example, when the generation AI analyzes video of a play, the analysis unit simultaneously learns rule sets for different sports, enabling it to support multiple sports. For example, the generation AI can simultaneously learn rule sets for basketball and soccer, enabling it to support both sports. For example, determining traveling in a basketball game and determining offside in a soccer game. The analysis unit can also simultaneously learn rule sets for tennis and volleyball, enabling it to support both sports. For example, determining foot faults in a tennis game and determining net touches in a volleyball game. The analysis unit can also simultaneously learn rule sets for baseball and cricket, enabling it to support both sports. For example, determining strikes in a baseball game and no-balls in a cricket game. This allows the generation AI to simultaneously learn rule sets for different sports, enabling it to support multiple sports.

[0038] The analysis unit can analyze the spectators' reactions and evaluate their excitement and satisfaction. For example, when the generation AI analyzes video of a game, the analysis unit analyzes the spectators' reactions and evaluates their excitement and satisfaction. For example, the generation AI analyzes video of a game and estimates the excitement and satisfaction levels from the spectators' facial expressions and movements. For example, the excitement level is evaluated when spectators are cheering in a basketball game. The analysis unit can also analyze video of a soccer game and estimate the excitement and satisfaction levels from the spectators' facial expressions and movements. For example, the satisfaction level is evaluated when spectators are clapping. The analysis unit can also analyze video of a tennis game and estimate the excitement and satisfaction levels from the spectators' facial expressions and movements. For example, the excitement level is evaluated when spectators are standing. In this way, the spectators' reactions can be analyzed and the excitement and satisfaction levels of the spectators can be evaluated.

[0039] The analysis unit predicts the movements of players and can predict their future positions. For example, the generation AI in the analysis unit analyzes video of a play and predicts the movements of players. For example, in a basketball game, the future position is predicted when a player starts dribbling. The analysis unit also predicts the movements of players when the generation AI analyzes video of a soccer game. For example, the future position is predicted when a player passes the ball. The analysis unit also predicts the movements of players when the generation AI analyzes video of a tennis game. For example, the future position is predicted when a player serves. This makes it possible to predict the movements of players and predict their future positions.

[0040] The analysis unit can analyze the speed and acceleration of a player's movements and evaluate the quality of the movements. For example, the analysis unit uses the generation AI to analyze video of a play and analyze the speed and acceleration of a player's movements. For example, in a basketball game, the analysis unit evaluates a player's dribbling speed. The analysis unit also uses the generation AI to analyze video of a soccer game and analyze the speed and acceleration of a player's movements. For example, the analysis unit evaluates a player's sprint speed. The analysis unit also uses the generation AI to analyze video of a tennis game and analyze the speed and acceleration of a player's movements. For example, the analysis unit evaluates a player's serve speed. This allows the speed and acceleration of a player's movements to be analyzed and the quality of their movements to be evaluated.

[0041] The analysis unit can simultaneously analyze footage from different viewpoints and perform three-dimensional movement analysis. For example, the analysis unit uses a generation AI to simultaneously analyze footage from multiple cameras and recognize three-dimensional movement. For example, in a basketball game, the analysis unit analyzes footage from different angles and evaluates the players' movements three-dimensionally. The analysis unit also uses a generation AI to analyze footage of a soccer game from multiple viewpoints and recognize three-dimensional movement. For example, the analysis unit analyzes a scramble in front of the goal three-dimensionally and determines offside. The analysis unit also uses a generation AI to analyze footage of a tennis game from multiple viewpoints and recognize three-dimensional movement. For example, the analysis unit analyzes the trajectory of a serve three-dimensionally and determines whether it is in or out. This allows the analysis of footage from different viewpoints simultaneously and performs three-dimensional movement analysis.

[0042] The analysis unit can analyze the movement of a player's equipment and uniform and evaluate the impact of the equipment. For example, the generation AI analyzes video of a game and recognizes the movement of a player's equipment and uniform. For example, in a basketball game, the movement of shoes is analyzed and the impact on performance is evaluated. In addition, the analysis unit can analyze video of a soccer game and recognize the movement of a player's equipment and uniform. For example, the fit of the uniform is analyzed and the ease of movement is evaluated. In addition, the analysis unit can analyze video of a tennis game and recognize the movement of a player's equipment and uniform. For example, the movement of the racket is analyzed and the quality of the swing is evaluated. This makes it possible to analyze the movement of a player's equipment and uniform and evaluate the impact of the equipment.

[0043] The analysis unit can learn to adapt to different environmental conditions. For example, the generation AI learns from footage of basketball games under different weather conditions to improve the accuracy of its judgments. For example, it can adapt to lighting conditions in outdoor games. The analysis unit also learns from footage of soccer games under different weather conditions to improve the accuracy of its judgments. For example, it can accurately recognize the movement of the ball in games played in the rain. The analysis unit also learns from footage of tennis games under different lighting conditions to improve the accuracy of its judgments. For example, it can accurately judge the lines in night games. This allows the generation AI to learn to adapt to different environmental conditions.

[0044] The analysis unit can learn the judging criteria for different sports and reduce incorrect calls for each sport. For example, the generation AI can learn the judging criteria for basketball and soccer and reduce incorrect calls in both sports. For example, it can accurately judge traveling in basketball and offside in soccer. The analysis unit can also learn the judging criteria for tennis and volleyball and reduce incorrect calls in both sports. For example, it can accurately judge line calls in tennis and net touches in volleyball. The analysis unit can also learn the judging criteria for baseball and cricket and reduce incorrect calls in both sports. For example, it can accurately judge strikes in baseball and no-balls in cricket. This allows the generation AI to learn the judging criteria for different sports and reduce incorrect calls for each sport.

[0045] The analysis unit can feed back the results of decisions in real time, allowing players and referees to respond immediately. For example, the analysis unit can feed back the results of basketball decisions to the generation AI in real time, allowing players and referees to respond immediately. For example, it can instantly notify a traveling decision. The analysis unit can also feed back the results of soccer decisions to the generation AI in real time, allowing players and referees to respond immediately. For example, it can instantly notify an offside decision. The analysis unit can also feed back the results of tennis decisions to the generation AI in real time, allowing players and referees to respond immediately. For example, it can instantly notify a line decision. This allows for feedback of decisions in real time, allowing players and referees to respond immediately.

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

[0047] The analysis unit learns the movement patterns of players and can automatically determine whether a specific movement violates the rules. For example, in a basketball game, it learns dribbling movements and automatically determines double dribbles and traveling. The analysis unit also has a generation AI that analyzes video of a soccer game and learns the movement patterns of players. For example, it learns tackling movements and automatically determines whether they are fouls. The analysis unit also has a generation AI that analyzes video of a tennis game and learns the movement patterns of players. For example, it learns serving movements and automatically determines foot faults. This allows the analysis unit to learn the movement patterns of players and automatically determine whether a specific movement violates the rules.

[0048] The refereeing unit can have a function to explain why a call was made during play. For example, in a basketball game, the refereeing unit can explain why a traveling call was made. In a soccer game, the refereeing unit can also explain why an offside call was made. Furthermore, in a tennis game, the refereeing unit can also explain why a foot fault call was made. This makes it easier for participants to understand the rules by explaining why a call was made during play.

[0049] The analysis unit can simultaneously analyze video from different viewpoints and perform three-dimensional movement analysis. For example, in a basketball game, it analyzes video from different angles and evaluates the players' movements in three dimensions. The analysis unit also uses a generation AI to analyze video of a soccer game from multiple viewpoints and recognize three-dimensional movement. For example, it analyzes the scramble in front of the goal in three dimensions and determines offside. The analysis unit also uses a generation AI to analyze video of a tennis game from multiple viewpoints and recognize three-dimensional movement. For example, it analyzes the trajectory of a serve in three dimensions and determines whether it is in or out. This allows video from different viewpoints to be simultaneously analyzed and three-dimensional movement analysis to be performed.

[0050] The analysis unit can analyze the movement of a player's equipment and uniform to evaluate the impact of the equipment. For example, in a basketball game, the movement of shoes can be analyzed to evaluate the impact on performance. The analysis unit also uses the generation AI to analyze video of a soccer game and recognize the movement of a player's equipment and uniform. For example, it analyzes the fit of the uniform and evaluates the ease of movement. The analysis unit also uses the generation AI to analyze video of a tennis game and recognize the movement of a player's equipment and uniform. For example, it analyzes the movement of the racket and evaluates the quality of the swing. This allows the movement of a player's equipment and uniform to be analyzed and the impact of the equipment to be evaluated.

[0051] The analysis unit can learn to adapt to different environmental conditions. For example, the generation AI learns from footage of basketball games under different weather conditions to improve the accuracy of its judgments. For example, it can adapt to lighting conditions in outdoor games. The analysis unit also allows the generation AI to learn from footage of soccer games under different weather conditions to improve the accuracy of its judgments. For example, it can accurately recognize the movement of the ball in games played in the rain. The analysis unit also allows the generation AI to learn from footage of tennis games under different lighting conditions to improve the accuracy of its judgments. For example, it can accurately judge the lines in night games. This allows the system to learn to adapt to different environmental conditions.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The video acquisition unit acquires video of the play. For example, the video of the play is captured using a smartphone camera. The video acquisition unit can also acquire video of the play using a fixed camera. Furthermore, the video acquisition unit can also acquire video of the play from the air using a drone. For example, the smartphone camera uses a wide-angle lens to capture the entire play. The fixed camera uses a high-resolution camera to capture specific areas in detail. The drone has an automatic tracking function to track the movement of the play. Step 2: The analysis unit analyzes the video of the play captured by the video capture unit. For example, the generation AI analyzes the video of the play using text generation AI (e.g., LLM). The generation AI can also analyze the video of the play using multimodal generation AI. The generation AI can also analyze the movement and position of objects during play using object recognition technology. For example, the text generation AI describes actions in the video as text. The multimodal generation AI analyzes a combination of video and audio. The object recognition technology identifies the position of the ball and players during play. Step 3: The judgement unit makes a decision based on the video of the play analyzed by the analysis unit. For example, the judgement unit makes a decision such as a strike, a ball, or an out based on the rules. The judgement unit can also make decisions such as offside and foul based on the rules. The judgement unit can also make decisions such as a goal or a score based on the rules. For example, the judgement unit makes a decision such as a strike or a ball based on the strike zone, makes a decision such as offside based on the offside line, and makes a decision such as a goal based on the goal line.

[0054] (Example 2) The automated refereeing system according to the embodiment of the present invention automatically captures video footage of plays, analyzes it using a generated AI, and makes judgments. This allows even inexperienced people to automatically act as referees.

[0055] The automated refereeing system according to the embodiment includes a video acquisition unit, an analysis unit, and a judgment unit. The video acquisition unit acquires video of a play. For example, the video of a play is captured using a smartphone camera. The video acquisition unit can also acquire video of a play using a fixed camera. The video acquisition unit can also acquire video of a play from the air using a drone. For example, the smartphone camera uses a wide-angle lens to capture the overall picture of the play. The fixed camera uses a high-resolution camera to capture specific areas in detail. The drone has an automatic tracking function to track the movement of the play. The analysis unit analyzes the video of a play acquired by the video acquisition unit. For example, the generation AI analyzes the video of a play using a text generation AI (e.g., LLM). The generation AI can also analyze the video of a play using a multimodal generation AI. The generation AI can also use object recognition technology to analyze the movement and position of objects during play. For example, the text generation AI describes actions in the video as text. The multimodal generation AI combines video and audio for analysis. Object recognition technology identifies the positions of the ball and players during play. The judgement unit makes judgments based on the video of the play analyzed by the analysis unit. For example, the judgement unit judges strikes, balls, and outs based on rules. The judgement unit can also judge offsides and fouls based on rules. The judgement unit can also judge goals and points based on rules. For example, the judgement unit judges strikes and balls based on the strike zone, judges offsides based on the offside line, and judges goals based on the goal line. This allows the automated refereeing system of the embodiment to automatically perform the role of a referee, even for inexperienced people. For example, the output unit displays the judgment results via a smartphone app. By displaying the judgment results in real time, the judgement can be immediately confirmed during play. The judgment results can also be saved and reviewed later. The judgment results can also be shared to share information with other participants.

[0056] The judgment unit analyzes the position of the ball and the movements of players in baseball games and can make judgments such as strike, ball, and out. In the judgment unit, for example, the generation AI analyzes video of the play and learns the movement patterns of players. For example, in a basketball game, it learns dribbling movements and automatically judges double dribbles and traveling. In addition, the judgment unit analyzes video of a soccer game and learns the movement patterns of players. For example, it learns tackling movements and automatically judges whether or not it is a foul. In addition, the judgment unit analyzes video of a tennis game and learns the movement patterns of players. For example, it learns serving movements and automatically judges foot faults. This enables accurate judgments in baseball games.

[0057] The judgment unit can analyze the position of the ball and the movements of players in a soccer game and make decisions such as offside and fouls. For example, the judgment unit uses a generation AI to analyze video of a play and estimate a player's physical strength and fatigue level from their movements and facial expressions. For example, in a basketball game, the judgment unit can suggest a rest if a player's movements become sluggish. The judgment unit can also use a generation AI to analyze video of a soccer game and estimate a player's fatigue level from the distance they run and the number of times they sprint. For example, the judgment unit can suggest a rest to a player who has run a certain distance. The judgment unit can also use a generation AI to analyze video of a tennis game and estimate a player's physical strength and fatigue level from their movements and facial expressions. For example, the judgment unit can suggest a rest to a player who has conceded consecutive points. This enables accurate judgment in soccer games.

[0058] The judgment unit can accurately recognize subtle movements and changes in position during play and make appropriate judgments. For example, the judgment unit uses a generation AI to analyze video of a play and estimate the emotional state of a player from their facial expressions and movements. For example, in a basketball game, the judgment unit evaluates the impact of a player feeling anger or impatience. The judgment unit also uses a generation AI to analyze video of a soccer game and estimate the emotional state of a player from their facial expressions and movements. For example, the judgment unit evaluates the impact of a player feeling nervous. The judgment unit also uses a generation AI to analyze video of a tennis game and estimate the emotional state of a player from their facial expressions and movements. For example, the judgment unit evaluates the impact of a player feeling depressed. This allows the judgment unit to accurately recognize subtle movements and changes in position during play and make appropriate judgments.

[0059] The judgement unit displays the strike zone in a baseball game and can judge strikes and balls in real time. For example, when the generation AI analyzes video of a play, the judgement unit can simultaneously learn rule sets for different sports, allowing it to handle multiple sports. For example, the generation AI can simultaneously learn rule sets for basketball and soccer, allowing it to handle both sports. For example, it can judge traveling in a basketball game and offside in a soccer game. The judgement unit can also simultaneously learn rule sets for tennis and volleyball, allowing it to handle both sports. For example, it can judge foot faults in a tennis game and net touches in a volleyball game. The judgement unit can also simultaneously learn rule sets for baseball and cricket, allowing it to handle both sports. For example, it can judge strikes in a baseball game and no-balls in a cricket game. This allows the strike zone to be displayed in a baseball game and strikes and balls to be judged in real time.

[0060] The judgment unit displays the offside line in a soccer game and can make offside judgments in real time. For example, when the generation AI analyzes video of a play, the judgment unit analyzes the reactions of spectators and evaluates the excitement and satisfaction of the spectators. For example, the generation AI analyzes video of a play and estimates the excitement and satisfaction from the facial expressions and movements of the spectators. For example, in a basketball game, the excitement level is evaluated if the spectators are cheering. Furthermore, the judgment unit analyzes video of a soccer game and estimates the excitement and satisfaction from the facial expressions and movements of the spectators. For example, the satisfaction level is evaluated if the spectators are clapping. Furthermore, the judgment unit analyzes video of a tennis game and estimates the excitement and satisfaction from the facial expressions and movements of the spectators. For example, the excitement level is evaluated if the spectators are standing. This allows the offside line to be displayed in a soccer game and offside judgments to be made in real time.

[0061] The judgment unit can be equipped with a function to explain why a certain call was made during play. For example, the judgment unit uses a generation AI to analyze video of a play, analyze the emotional state of the spectators, and evaluate the excitement of the game in real time. For example, the generation AI analyzes video of a play and infers the emotional state from the spectators' facial expressions and actions. For example, in a basketball game, if the spectators are happy, the excitement of the game is evaluated. In addition, the judgment unit uses a generation AI to analyze video of a soccer game and infers the emotional state from the spectators' facial expressions and actions. For example, if the spectators are surprised, the excitement of the game is evaluated. In addition, the judgment unit uses a generation AI to analyze video of a tennis game and infers the emotional state from the spectators' facial expressions and actions. For example, if the spectators are sad, the excitement of the game is evaluated. This makes it easier for participants to understand the rules by explaining why a certain call was made during play.

[0062] The analysis unit learns the movement patterns of players and can automatically determine whether a specific movement violates the rules. For example, the generation AI of the analysis unit analyzes video of a play and learns the movement patterns of players. For example, in a basketball game, it learns dribbling movements and automatically determines double dribbles and traveling. The analysis unit also analyzes video of a soccer game and learns the movement patterns of players. For example, it learns tackling movements and automatically determines whether they are fouls. The analysis unit also analyzes video of a tennis game and learns the movement patterns of players. For example, it learns serving movements and automatically determines foot faults. In this way, it is possible to learn the movement patterns of players and automatically determine whether a specific movement violates the rules.

[0063] The analysis unit can analyze the emotional state of a player and evaluate the impact of emotional factors on play. For example, the analysis unit uses the generation AI to analyze video of a play and estimate the emotional state from the player's facial expressions and movements. For example, in a basketball game, the analysis unit evaluates the impact if a player is feeling anger or impatience. The analysis unit also uses the generation AI to analyze video of a soccer game and estimate the emotional state from the player's facial expressions and movements. For example, the analysis unit evaluates the impact if a player is nervous. The analysis unit also uses the generation AI to analyze video of a tennis game and estimate the emotional state from the player's facial expressions and movements. For example, the analysis unit evaluates the impact if a player is depressed. This makes it possible to analyze the emotional state of a player and evaluate the impact of emotional factors on play.

[0064] The analysis unit can simultaneously learn rule sets for different sports, enabling it to support multiple sports. For example, when the generation AI analyzes video of a play, the analysis unit simultaneously learns rule sets for different sports, enabling it to support multiple sports. For example, the generation AI can simultaneously learn rule sets for basketball and soccer, enabling it to support both sports. For example, determining traveling in a basketball game and determining offside in a soccer game. The analysis unit can also simultaneously learn rule sets for tennis and volleyball, enabling it to support both sports. For example, determining foot faults in a tennis game and determining net touches in a volleyball game. The analysis unit can also simultaneously learn rule sets for baseball and cricket, enabling it to support both sports. For example, determining strikes in a baseball game and no-balls in a cricket game. This allows the generation AI to simultaneously learn rule sets for different sports, enabling it to support multiple sports.

[0065] The analysis unit can analyze the spectators' reactions and evaluate their excitement and satisfaction. For example, when the generation AI analyzes video of a game, the analysis unit analyzes the spectators' reactions and evaluates their excitement and satisfaction. For example, the generation AI analyzes video of a game and estimates the excitement and satisfaction levels from the spectators' facial expressions and movements. For example, the excitement level is evaluated when spectators are cheering in a basketball game. The analysis unit can also analyze video of a soccer game and estimate the excitement and satisfaction levels from the spectators' facial expressions and movements. For example, the satisfaction level is evaluated when spectators are clapping. The analysis unit can also analyze video of a tennis game and estimate the excitement and satisfaction levels from the spectators' facial expressions and movements. For example, the excitement level is evaluated when spectators are standing. In this way, the spectators' reactions can be analyzed and the excitement and satisfaction levels of the spectators can be evaluated.

[0066] The analysis unit analyzes the emotional state of the spectators and can evaluate the excitement of the game in real time. For example, the generation AI analyzes video of the play and infers the emotional state from the spectators' facial expressions and movements. For example, in a basketball game, the analysis unit evaluates the excitement of the game if the spectators are happy. In addition, the analysis unit analyzes video of a soccer game and infers the emotional state from the spectators' facial expressions and movements. For example, the analysis unit evaluates the excitement of the game if the spectators are surprised. In addition, the analysis unit analyzes video of a tennis game and infers the emotional state from the spectators' facial expressions and movements. For example, the analysis unit evaluates the excitement of the game if the spectators are sad. In this way, the emotional state of the spectators can be analyzed and the excitement of the game can be evaluated in real time.

[0067] The analysis unit predicts the movements of players and can predict their future positions. For example, the generation AI in the analysis unit analyzes video of a play and predicts the movements of players. For example, in a basketball game, the future position is predicted when a player starts dribbling. The analysis unit also predicts the movements of players when the generation AI analyzes video of a soccer game. For example, the future position is predicted when a player passes the ball. The analysis unit also predicts the movements of players when the generation AI analyzes video of a tennis game. For example, the future position is predicted when a player serves. This makes it possible to predict the movements of players and predict their future positions.

[0068] The analysis unit can analyze the speed and acceleration of a player's movements and evaluate the quality of the movements. For example, the analysis unit uses the generation AI to analyze video of a play and analyze the speed and acceleration of a player's movements. For example, in a basketball game, the analysis unit evaluates a player's dribbling speed. The analysis unit also uses the generation AI to analyze video of a soccer game and analyze the speed and acceleration of a player's movements. For example, the analysis unit evaluates a player's sprint speed. The analysis unit also uses the generation AI to analyze video of a tennis game and analyze the speed and acceleration of a player's movements. For example, the analysis unit evaluates a player's serve speed. This allows the speed and acceleration of a player's movements to be analyzed and the quality of their movements to be evaluated.

[0069] The analysis unit can analyze emotional reactions to a player's movements and make an emotional evaluation of the quality of the movements. For example, the analysis unit uses a generation AI to analyze video of a play and estimate emotional reactions to a player's movements. For example, in a basketball game, the analysis unit evaluates the emotions of spectators toward a player's dribbling. In addition, the analysis unit uses a generation AI to analyze video of a soccer game and estimate emotional reactions to a player's movements. For example, the analysis unit evaluates the emotions of spectators toward a player's shot. In addition, the analysis unit uses a generation AI to analyze video of a tennis game and estimate emotional reactions to a player's movements. For example, the analysis unit evaluates the emotions of spectators toward a player's serve. In this way, emotional reactions to a player's movements can be analyzed and an emotional evaluation of the quality of the movements can be made.

[0070] The analysis unit can simultaneously analyze footage from different viewpoints and perform three-dimensional movement analysis. For example, the analysis unit uses a generation AI to simultaneously analyze footage from multiple cameras and recognize three-dimensional movement. For example, in a basketball game, the analysis unit analyzes footage from different angles and evaluates the players' movements three-dimensionally. The analysis unit also uses a generation AI to analyze footage of a soccer game from multiple viewpoints and recognize three-dimensional movement. For example, the analysis unit analyzes a scramble in front of the goal three-dimensionally and determines offside. The analysis unit also uses a generation AI to analyze footage of a tennis game from multiple viewpoints and recognize three-dimensional movement. For example, the analysis unit analyzes the trajectory of a serve three-dimensionally and determines whether it is in or out. This allows the analysis of footage from different viewpoints simultaneously and performs three-dimensional movement analysis.

[0071] The analysis unit can analyze the movement of a player's equipment and uniform and evaluate the impact of the equipment. For example, the generation AI analyzes video of a game and recognizes the movement of a player's equipment and uniform. For example, in a basketball game, the movement of shoes is analyzed and the impact on performance is evaluated. In addition, the analysis unit can analyze video of a soccer game and recognize the movement of a player's equipment and uniform. For example, the fit of the uniform is analyzed and the ease of movement is evaluated. In addition, the analysis unit can analyze video of a tennis game and recognize the movement of a player's equipment and uniform. For example, the movement of the racket is analyzed and the quality of the swing is evaluated. This makes it possible to analyze the movement of a player's equipment and uniform and evaluate the impact of the equipment.

[0072] The analysis unit can analyze a player's emotional reactions to their equipment and uniforms and suggest improvements to the equipment. For example, the analysis unit uses a generative AI to analyze video of a game and estimate a player's emotional reactions to their equipment and uniforms. For example, in a basketball game, the analysis unit evaluates a player's feelings toward their shoes and suggests improvements. In addition, the analysis unit uses a generative AI to analyze video of a soccer game and estimate a player's emotional reactions to their equipment and uniforms. For example, the analysis unit evaluates a player's feelings toward the fit of their uniform and suggests improvements. In addition, the analysis unit uses a generative AI to analyze video of a tennis game and estimate a player's emotional reactions to their equipment and uniforms. For example, the analysis unit evaluates a player's feelings toward the weight of their racket and suggests improvements. In this way, the analysis unit can analyze a player's emotional reactions to their equipment and uniforms and suggest improvements to the equipment.

[0073] The analysis unit can learn to adapt to different environmental conditions. For example, the generation AI learns from footage of basketball games under different weather conditions to improve the accuracy of its judgments. For example, it can adapt to lighting conditions in outdoor games. The analysis unit also learns from footage of soccer games under different weather conditions to improve the accuracy of its judgments. For example, it can accurately recognize the movement of the ball in games played in the rain. The analysis unit also learns from footage of tennis games under different lighting conditions to improve the accuracy of its judgments. For example, it can accurately judge the lines in night games. This allows the generation AI to learn to adapt to different environmental conditions.

[0074] The analysis unit can analyze the emotional reactions of players and spectators to a wrong call and evaluate the impact of the wrong call. For example, the analysis unit has the generation AI analyze the emotional reactions of players to a wrong call and evaluate the impact. For example, in a basketball game, the analysis unit analyzes the facial expressions and movements of players after a wrong call. The generation AI also analyzes the emotional reactions of spectators to a wrong call and evaluates the impact. For example, in a soccer game, the analysis unit analyzes the reactions of spectators after a wrong call. The generation AI also analyzes the emotional reactions of players and spectators to a wrong call and evaluates the impact. For example, in a tennis game, the analysis unit analyzes the facial expressions of players and spectators after a wrong call. This makes it possible to analyze the emotional reactions of players and spectators to a wrong call and evaluate the impact of the wrong call.

[0075] The analysis unit can learn the judging criteria for different sports and reduce incorrect calls for each sport. For example, the generation AI can learn the judging criteria for basketball and soccer and reduce incorrect calls in both sports. For example, it can accurately judge traveling in basketball and offside in soccer. The analysis unit can also learn the judging criteria for tennis and volleyball and reduce incorrect calls in both sports. For example, it can accurately judge line calls in tennis and net touches in volleyball. The analysis unit can also learn the judging criteria for baseball and cricket and reduce incorrect calls in both sports. For example, it can accurately judge strikes in baseball and no-balls in cricket. This allows the generation AI to learn the judging criteria for different sports and reduce incorrect calls for each sport.

[0076] The analysis unit can feed back the results of decisions in real time, allowing players and referees to respond immediately. For example, the analysis unit can feed back the results of basketball decisions to the generation AI in real time, allowing players and referees to respond immediately. For example, it can instantly notify a traveling decision. The analysis unit can also feed back the results of soccer decisions to the generation AI in real time, allowing players and referees to respond immediately. For example, it can instantly notify an offside decision. The analysis unit can also feed back the results of tennis decisions to the generation AI in real time, allowing players and referees to respond immediately. For example, it can instantly notify a line decision. This allows for feedback of decisions in real time, allowing players and referees to respond immediately.

[0077] The analysis unit analyzes emotional reactions to incorrect calls in real time, thereby minimizing the impact of incorrect calls. For example, the generation AI in the analysis unit analyzes the emotional reactions of players to incorrect calls in real time, thereby minimizing their impact. For example, in a basketball game, the analysis unit analyzes the facial expressions of players after an incorrect call and suggests an appropriate response. The generation AI also analyzes the emotional reactions of spectators to incorrect calls in real time, thereby minimizing their impact. For example, in a soccer game, the analysis unit analyzes the reactions of spectators after an incorrect call and suggests an appropriate response. The analysis unit also analyzes the emotional reactions of players and spectators to incorrect calls in real time, thereby minimizing their impact. For example, in a tennis game, the analysis unit analyzes the facial expressions of players and spectators after an incorrect call and suggests an appropriate response. In this way, the emotional reactions to incorrect calls can be analyzed in real time, thereby minimizing the impact of incorrect calls.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The analysis unit learns the movement patterns of players and can automatically determine whether a specific movement violates the rules. For example, in a basketball game, it learns dribbling movements and automatically determines double dribbles and traveling. The analysis unit also has a generation AI that analyzes video of a soccer game and learns the movement patterns of players. For example, it learns tackling movements and automatically determines whether they are fouls. The analysis unit also has a generation AI that analyzes video of a tennis game and learns the movement patterns of players. For example, it learns serving movements and automatically determines foot faults. This allows the analysis unit to learn the movement patterns of players and automatically determine whether a specific movement violates the rules.

[0080] The refereeing unit can have a function to explain why a call was made during play. For example, in a basketball game, the refereeing unit can explain why a traveling call was made. In a soccer game, the refereeing unit can also explain why an offside call was made. Furthermore, in a tennis game, the refereeing unit can also explain why a foot fault call was made. This makes it easier for participants to understand the rules by explaining why a call was made during play.

[0081] The analysis unit can simultaneously analyze video from different viewpoints and perform three-dimensional movement analysis. For example, in a basketball game, it analyzes video from different angles and evaluates the players' movements in three dimensions. The analysis unit also uses a generation AI to analyze video of a soccer game from multiple viewpoints and recognize three-dimensional movement. For example, it analyzes the scramble in front of the goal in three dimensions and determines offside. The analysis unit also uses a generation AI to analyze video of a tennis game from multiple viewpoints and recognize three-dimensional movement. For example, it analyzes the trajectory of a serve in three dimensions and determines whether it is in or out. This allows video from different viewpoints to be simultaneously analyzed and three-dimensional movement analysis to be performed.

[0082] The analysis unit can analyze the movement of a player's equipment and uniform to evaluate the impact of the equipment. For example, in a basketball game, the movement of shoes can be analyzed to evaluate the impact on performance. The analysis unit also uses the generation AI to analyze video of a soccer game and recognize the movement of a player's equipment and uniform. For example, it analyzes the fit of the uniform and evaluates the ease of movement. The analysis unit also uses the generation AI to analyze video of a tennis game and recognize the movement of a player's equipment and uniform. For example, it analyzes the movement of the racket and evaluates the quality of the swing. This allows the movement of a player's equipment and uniform to be analyzed and the impact of the equipment to be evaluated.

[0083] The analysis unit can learn to adapt to different environmental conditions. For example, the generation AI learns from footage of basketball games under different weather conditions to improve the accuracy of its judgments. For example, it can adapt to lighting conditions in outdoor games. The analysis unit also allows the generation AI to learn from footage of soccer games under different weather conditions to improve the accuracy of its judgments. For example, it can accurately recognize the movement of the ball in games played in the rain. The analysis unit also allows the generation AI to learn from footage of tennis games under different lighting conditions to improve the accuracy of its judgments. For example, it can accurately judge the lines in night games. This allows the system to learn to adapt to different environmental conditions.

[0084] The judgment unit can analyze the emotional state of a player and evaluate the impact of emotional factors on play. For example, in a basketball game, if a player is feeling anger or impatience, the impact is evaluated. The judgment unit also uses the generation AI to analyze video of a soccer game and estimate the emotional state from the player's facial expressions and movements. For example, if a player is nervous, the impact is evaluated. The judgment unit also uses the generation AI to analyze video of a tennis game and estimate the emotional state from the player's facial expressions and movements. For example, if a player is depressed, the impact is evaluated. This makes it possible to analyze a player's emotional state and evaluate the impact of emotional factors on play.

[0085] The analysis unit can analyze the spectators' reactions and evaluate their excitement and satisfaction levels. For example, the generation AI analyzes video of a game and estimates the excitement and satisfaction levels from the spectators' facial expressions and movements. For example, the excitement level is evaluated if the spectators are cheering at a basketball game. The analysis unit can also analyze video of a soccer game using the generation AI and estimate the excitement and satisfaction levels from the spectators' facial expressions and movements. For example, the satisfaction level is evaluated if the spectators are clapping. The analysis unit can also analyze video of a tennis game using the generation AI and estimate the excitement and satisfaction levels from the spectators' facial expressions and movements. For example, the excitement level is evaluated if the spectators are standing. This makes it possible to analyze the spectators' reactions and evaluate their excitement and satisfaction levels.

[0086] The analysis unit can analyze the emotional reactions of players and spectators to a wrong call and evaluate the impact of the wrong call. For example, the generation AI analyzes the emotional reactions of players to a wrong call and evaluates the impact. For example, in a basketball game, the analysis unit analyzes the facial expressions and movements of players after a wrong call. The generation AI also analyzes the emotional reactions of spectators to a wrong call and evaluates the impact. For example, in a soccer game, the analysis unit analyzes the reactions of spectators after a wrong call. The generation AI also analyzes the emotional reactions of players and spectators to a wrong call and evaluates the impact. For example, in a tennis game, the analysis unit analyzes the facial expressions of players and spectators after a wrong call. This makes it possible to analyze the emotional reactions of players and spectators to a wrong call and evaluate the impact of the wrong call.

[0087] The analysis unit can analyze emotional reactions to a player's movements and make an emotional evaluation of the quality of the movements. For example, the generation AI analyzes video of a play and estimates emotional reactions to a player's movements. For example, in a basketball game, the generation AI evaluates the emotions of spectators toward a player's dribbling. In addition, the analysis unit can analyze video of a soccer game and estimate emotional reactions to a player's movements. For example, the generation AI evaluates the emotions of spectators toward a player's shot. In addition, the analysis unit can analyze video of a tennis game and estimate emotional reactions to a player's movements. For example, the generation AI evaluates the emotions of spectators toward a player's serve. This makes it possible to analyze emotional reactions to a player's movements and make an emotional evaluation of the quality of the movements.

[0088] The analysis unit can analyze a player's emotional reactions to their equipment and uniforms and suggest improvements to the equipment. For example, the generation AI analyzes video of a game and estimates a player's emotional reactions to their equipment and uniforms. For example, in a basketball game, it evaluates a player's feelings about their shoes and suggests improvements. In addition, the analysis unit can analyze video of a soccer game and estimate a player's emotional reactions to their equipment and uniforms. For example, it evaluates a player's feelings about the fit of their uniform and suggests improvements. In addition, the analysis unit can analyze video of a tennis game and estimate a player's emotional reactions to their equipment and uniforms. For example, it evaluates a player's feelings about the weight of their racket and suggests improvements. This allows the system to analyze a player's emotional reactions to their equipment and uniforms and suggest improvements to the equipment.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The video acquisition unit acquires video of the play. For example, the video of the play is captured using a smartphone camera. The video acquisition unit can also acquire video of the play using a fixed camera. Furthermore, the video acquisition unit can also acquire video of the play from the air using a drone. For example, the smartphone camera uses a wide-angle lens to capture the entire play. The fixed camera uses a high-resolution camera to capture specific areas in detail. The drone has an automatic tracking function to track the movement of the play. Step 2: The analysis unit analyzes the video of the play captured by the video capture unit. For example, the generation AI analyzes the video of the play using text generation AI (e.g., LLM). The generation AI can also analyze the video of the play using multimodal generation AI. The generation AI can also analyze the movement and position of objects during play using object recognition technology. For example, the text generation AI describes actions in the video as text. The multimodal generation AI analyzes a combination of video and audio. The object recognition technology identifies the position of the ball and players during play. Step 3: The judgement unit makes a decision based on the video of the play analyzed by the analysis unit. For example, the judgement unit makes a decision such as a strike, a ball, or an out based on the rules. The judgement unit can also make decisions such as offside and foul based on the rules. The judgement unit can also make decisions such as a goal or a score based on the rules. For example, the judgement unit makes a decision such as a strike or a ball based on the strike zone, makes a decision such as offside based on the offside line, and makes a decision such as a goal based on the goal line.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0132] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0141] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0142] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0143] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0145] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0146] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0147] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0148] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0149] 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.

[0150] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0151] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0152] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0153] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0154] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0155] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a video acquisition unit that acquires video of a play; an analysis unit that analyzes the video of the play acquired by the video acquisition unit; a judgment unit that makes a judgment based on the video of the play analyzed by the analysis unit. A system characterized by:

2. The determination unit In baseball games, it analyzes the position of the ball and the movements of the players to make decisions such as strikes, balls, and outs.

2. The system of claim 1.

3. The determination unit Analyzing the position of the ball and the movements of players in soccer matches to make decisions such as offside and fouls 2. The system of claim 1.

4. The determination unit Accurately recognize subtle movements and changes in position during play and make appropriate decisions 2. The system of claim 1.

5. The determination unit Displays the strike zone in baseball games and judges strikes and balls in real time 2. The system of claim 1.

6. The determination unit Display the offside line in soccer matches and make offside decisions in real time 2. The system of claim 1.

Images