System

Abstract

An object of the system according to the embodiment is to prevent misjudgment in a sports game.SOLUTION: A system includes a game video analysis unit and a determination unit. The match video analysis unit analyzes the match video in real time. The determination unit performs determination based on an analysis result of the game video analyzed by the game video analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques may result in incorrect judgments during sports matches, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent erroneous refereeing in sports matches. [Means for solving the problem]

[0006] The system according to the embodiment includes a game video analysis unit and a judgment unit. The game video analysis unit analyzes game video in real time. The judgment unit makes a judgment based on the analysis result of the game video analyzed by the game video analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can prevent erroneous refereeing in sports matches. [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 system for preventing erroneous refereeing according to the embodiment of the present invention is a system that analyzes game footage in real time and automates judgments, thereby preventing erroneous refereeing during a game as much as possible and realizing fair and accurate judgments.

[0029] The system for preventing refereeing errors according to the embodiment includes a game video analysis unit and a judgment unit. The game video analysis unit analyzes game video in real time. For example, in a soccer game, the game video analysis unit analyzes camera video to determine whether a shot is offside or has crossed the goal line. In a basketball game, the game video analysis unit can also determine whether a shot was taken before time expires. In a tennis game, the game video analysis unit can analyze a player's serve data to detect faults and net touches. For example, the game video analysis unit analyzes camera video in real time to accurately determine the player's movements and the position of the ball. The judgment unit makes a judgment based on the analysis results of the game video analyzed by the game video analysis unit. For example, the judgment unit uses goal line technology to determine whether the ball has completely crossed the goal line. In a basketball game, the judgment unit can also determine whether a shot was taken before time expires. In a tennis game, the judgment unit can also analyze a player's serve data to detect faults and net touches. For example, the judgment unit automatically makes a judgment based on the analysis results. As a result, the erroneous refereeing prevention system according to the embodiment can prevent erroneous refereeing during a match as much as possible, and can achieve fair and accurate judgments.

[0030] The match video analysis unit can analyze camera footage to determine whether a player is offside or has crossed the goal line. The match video analysis unit, for example, analyzes camera footage to make an offside determination. For example, AI analyzes the positions of the players and the ball and makes a determination based on the offside criteria. The match video analysis unit also determines whether a player has crossed the goal line. For example, AI analyzes the position of the ball and the position of the goal line and determines whether the ball has completely crossed the goal line. In this way, by analyzing camera footage and making offside and goal line determinations, incorrect refereeing can be prevented.

[0031] The game video analysis unit can acquire vital data of players in real time and evaluate their playing performance. The game video analysis unit, for example, acquires the player's heart rate in real time and evaluates their playing performance. For example, an increase in heart rate is used as an indicator of the player's concentration level. The game video analysis unit also acquires the player's breathing rate in real time and evaluates their playing performance. For example, a change in breathing rate is used as an indicator of the player's fatigue level. The game video analysis unit also comprehensively analyzes the player's heart rate and breathing rate and evaluates their playing performance. For example, changes in heart rate and breathing rate are used as indicators of the player's physical strength and concentration. This allows for more accurate judgment by analyzing the player's vital data and evaluating their playing performance.

[0032] The game video analysis unit can predict current plays by referencing players' past playing styles and tactical data. The game video analysis unit, for example, analyzes players' past playing styles to predict current plays. For example, it predicts the next move based on a particular player's specialty play pattern. The game video analysis unit also analyzes team tactical data to predict current plays. For example, it predicts the next play based on a situation in which a particular tactic is used. The game video analysis unit also comprehensively analyzes players' past playing styles and tactical data to predict current plays. For example, it predicts the next move by combining the individual characteristics of the players with the team's tactics. In this way, by referencing past data and predicting current plays, more accurate judgments are possible.

[0033] The game video analysis unit can analyze the reactions and cheers of the spectators to evaluate the level of excitement of the game. For example, the game video analysis unit analyzes the cheers of the spectators in real time to evaluate the level of excitement of the game. For example, the level of excitement of the game is quantified based on the volume and frequency of the cheers. The game video analysis unit also analyzes the facial expressions of the spectators in real time to evaluate the level of excitement of the game. For example, the level of excitement of the game is evaluated based on smiling and surprised expressions. The game video analysis unit also analyzes the reactions and cheers of the spectators in an integrated manner to evaluate the level of excitement of the game. For example, the level of excitement of the game is evaluated by combining the volume of the cheers and the facial expressions of the spectators. In this way, the atmosphere of the game can be grasped by analyzing the reactions of the spectators and evaluating the level of excitement of the game.

[0034] The game video analysis unit can integrate footage from different camera angles and generate a 3D model to analyze the details of a play. For example, the game video analysis unit integrates footage from different camera angles and generates a 3D model. For example, a goal scene can be reconstructed from footage from multiple cameras and analyzed in detail. The game video analysis unit also analyzes footage from different camera angles and reproduces player movements in a 3D model. For example, it accurately grasps player movements and positions and analyzes the details of the play. The game video analysis unit also integrates footage from different camera angles and generates a 3D model of the entire game. For example, it visualizes the flow of the game and tactics in a 3D model and performs a detailed analysis. This makes it possible to analyze the details of a play by integrating footage from different camera angles and generating a 3D model.

[0035] The judgment unit can introduce an algorithm for making a consistent judgment by comparing with past judgment data. The judgment unit, for example, analyzes past judgment data and introduces an algorithm for making a consistent judgment. For example, the current judgment is adjusted based on past judgment patterns. The judgment unit also compares past judgment data with current judgment data to make a consistent judgment. For example, it checks whether judgments in the same situation are consistent. The judgment unit also learns from past judgment data and develops an algorithm for making a consistent judgment. For example, it optimizes the current judgment based on past judgment results. In this way, the reliability of the judgment can be improved by making a consistent judgment by comparing with past judgment data.

[0036] The judging unit can make appropriate decisions by taking into account the situation of the match. For example, the judging unit analyzes important aspects of the match and makes appropriate decisions. For example, it adjusts decisions towards the end of the match or in situations where the score difference is large. The judging unit also makes appropriate decisions by taking into account the score difference of the match. For example, it adjusts the strictness of the decision when the score difference is large. The judging unit also performs an integrated analysis of the situation of the match and makes appropriate decisions. For example, it optimizes decisions based on the flow of the match and the performance of the players. In this way, fairness in the match can be maintained by making appropriate decisions by taking into account the situation of the match.

[0037] The judging unit can integrate the rules of different sports to build a judging system that supports multiple sports. The judging unit, for example, analyzes the rules of different sports to build an integrated judging system. For example, the rules of soccer and basketball can be integrated to make judging compatible with multiple sports. The judging unit also learns the rules of different sports to develop a judging system that supports multiple sports. For example, the rules of tennis and rugby can be integrated to improve the accuracy of judging. The judging unit also analyzes the rules of different sports in real time to make judging compatible with multiple sports. For example, matches of different sports can be analyzed simultaneously to make integrated judging. In this way, the rules of different sports can be integrated to build a judging system that supports multiple sports, making it possible to support multiple sports.

[0038] The judgment unit can analyze not only the player's movements but also the audio data to make a comprehensive judgment. The judgment unit, for example, analyzes the sound of the ball in real time and reflects this in its judgment. For example, it detects the sound of the ball crossing the line and makes a judgment. The judgment unit also analyzes the player's voice in real time and reflects this in its judgment. For example, it detects foul play from the player's voice and makes a judgment. The judgment unit also analyzes the sound of the ball and the player's voice in an integrated manner and makes a comprehensive judgment. For example, the sound of the ball and the player's voice are combined to improve the accuracy of the judgment. In this way, the judgment accuracy can be improved by analyzing the player's movements and audio data and making a comprehensive judgment.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The match video analysis unit analyzes not only the movements of the players but also the audio data during the match, allowing for comprehensive judgment. For example, it can detect the sound of the ball crossing the line and reflect this in its judgment. It can also analyze the voices of the players in real time to detect foul play. Furthermore, it can analyze the sound of the ball and the voices of the players in an integrated manner, improving the accuracy of judgment. This allows for the analysis of player movements and audio data to make a comprehensive judgment, thereby improving the accuracy of judgment.

[0041] The match video analysis unit can integrate footage from different camera angles and generate 3D models to analyze the details of plays. For example, a goal scene can be reconstructed from footage from multiple cameras for detailed analysis. It can also reproduce player movements in 3D models, allowing for accurate understanding of player movements and positions. Furthermore, it can generate a 3D model of the entire match, allowing for visualization of the flow of the game and tactics. By integrating footage from different camera angles and generating a 3D model, it is possible to analyze the details of plays.

[0042] The match video analysis unit can analyze the reactions and cheers of the spectators to evaluate the level of excitement of the match. For example, it can analyze the cheers of the spectators in real time and quantify the level of excitement of the match. It can also analyze the facial expressions of the spectators in real time and evaluate the level of excitement of the match based on smiles and surprised expressions. It can also analyze the reactions and cheers of the spectators in an integrated manner to evaluate the level of excitement of the match. In this way, it is possible to grasp the atmosphere of the match by analyzing the reactions of the spectators and evaluating the level of excitement of the match.

[0043] The judgment unit can introduce an algorithm to make a consistent judgment by comparing with past judgment data. For example, it can analyze past judgment data and adjust the current judgment. It can also compare past judgment data with current judgment data to check whether judgments in the same situation are consistent. Furthermore, it can develop an algorithm that learns from past judgment data and makes a consistent judgment. This allows it to make a consistent judgment by comparing with past judgment data, thereby improving the reliability of the judgment.

[0044] The judging unit can integrate the rules of different sports to build a judging system that supports multiple sports. For example, the rules of soccer and basketball can be integrated to make judgments that support multiple sports. The rules of tennis and rugby can also be integrated to improve the accuracy of judgments. Furthermore, it can analyze matches of different sports simultaneously and make integrated judgments. This allows the rules of different sports to be integrated to build a judging system that supports multiple sports, making it possible to support multiple sports.

[0045] The judgment unit can refer to a player's past playing style and tactical data to predict the current play. For example, it can analyze a player's past playing style and predict the next move based on a specific playing pattern. It can also analyze the team's tactical data and predict the next play based on situations in which a specific tactic is used. It can also comprehensively analyze a player's past playing style and tactical data to predict the next move. This allows for more accurate judgment by referring to past data and predicting the current play.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The game video analysis unit analyzes game video in real time. For example, in a soccer game, it analyzes camera footage to determine whether a player is offside or has crossed the goal line. It can also determine whether a shot was taken before time expires in a basketball game, and analyze players' serve data in a tennis game to detect faults and net touches. The game video analysis unit analyzes camera footage in real time to accurately determine players' movements and the position of the ball. Step 2: The judgement unit makes a decision based on the analysis results of the match video analyzed by the match video analysis unit. For example, goal line technology is used to determine whether the ball has completely crossed the goal line. It can also determine whether a shot was taken before time expires in a basketball match, or analyze a player's serve data in a tennis match to detect faults or net touches. The judgement unit automatically makes a decision based on the analysis results.

[0048] (Example 2) The system for preventing erroneous refereeing according to the embodiment of the present invention is a system that analyzes game footage in real time and automates judgments, thereby preventing erroneous refereeing during a game as much as possible and realizing fair and accurate judgments.

[0049] The system for preventing refereeing errors according to the embodiment includes a game video analysis unit and a judgment unit. The game video analysis unit analyzes game video in real time. For example, in a soccer game, the game video analysis unit analyzes camera video to determine whether a shot is offside or has crossed the goal line. In a basketball game, the game video analysis unit can also determine whether a shot was taken before time expires. In a tennis game, the game video analysis unit can analyze a player's serve data to detect faults and net touches. For example, the game video analysis unit analyzes camera video in real time to accurately determine the player's movements and the position of the ball. The judgment unit makes a judgment based on the analysis results of the game video analyzed by the game video analysis unit. For example, the judgment unit uses goal line technology to determine whether the ball has completely crossed the goal line. In a basketball game, the judgment unit can also determine whether a shot was taken before time expires. In a tennis game, the judgment unit can also analyze a player's serve data to detect faults and net touches. For example, the judgment unit automatically makes a judgment based on the analysis results. As a result, the erroneous refereeing prevention system according to the embodiment can prevent erroneous refereeing during a match as much as possible, and can achieve fair and accurate judgments.

[0050] The match video analysis unit can analyze camera footage to determine whether a player is offside or has crossed the goal line. The match video analysis unit, for example, analyzes camera footage to make an offside determination. For example, AI analyzes the positions of the players and the ball and makes a determination based on the offside criteria. The match video analysis unit also determines whether a player has crossed the goal line. For example, AI analyzes the position of the ball and the position of the goal line and determines whether the ball has completely crossed the goal line. In this way, by analyzing camera footage and making offside and goal line determinations, incorrect refereeing can be prevented.

[0051] The game video analysis unit can infer emotions from players' facial expressions and body movements, and analyze their playing intentions based on changes in their emotions. The game video analysis unit, for example, analyzes players' facial expressions in real time to infer changes in emotions. For example, it detects tension just before a goal is scored or disappointment after a goal is conceded, and analyzes their playing intentions. The game video analysis unit also analyzes players' body movements to infer changes in emotions. For example, it analyzes their playing intentions based on changes in the speed of their movements and their posture. The game video analysis unit also comprehensively analyzes players' facial expressions and body movements to infer changes in emotions. For example, it detects their concentration just before a goal is scored or their agitation after a goal is conceded, and analyzes their playing intentions. In this way, analyzing players' emotions and understanding their playing intentions enables more accurate judgment.

[0052] The game video analysis unit can acquire vital data of players in real time and evaluate their playing performance. The game video analysis unit, for example, acquires the player's heart rate in real time and evaluates their playing performance. For example, an increase in heart rate is used as an indicator of the player's concentration level. The game video analysis unit also acquires the player's breathing rate in real time and evaluates their playing performance. For example, a change in breathing rate is used as an indicator of the player's fatigue level. The game video analysis unit also comprehensively analyzes the player's heart rate and breathing rate and evaluates their playing performance. For example, changes in heart rate and breathing rate are used as indicators of the player's physical strength and concentration. This allows for more accurate judgment by analyzing the player's vital data and evaluating their playing performance.

[0053] The game video analysis unit can predict current plays by referencing players' past playing styles and tactical data. The game video analysis unit, for example, analyzes players' past playing styles to predict current plays. For example, it predicts the next move based on a particular player's specialty play pattern. The game video analysis unit also analyzes team tactical data to predict current plays. For example, it predicts the next play based on a situation in which a particular tactic is used. The game video analysis unit also comprehensively analyzes players' past playing styles and tactical data to predict current plays. For example, it predicts the next move by combining the individual characteristics of the players with the team's tactics. In this way, by referencing past data and predicting current plays, more accurate judgments are possible.

[0054] The game video analysis unit can analyze the reactions and cheers of the spectators to evaluate the level of excitement of the game. For example, the game video analysis unit analyzes the cheers of the spectators in real time to evaluate the level of excitement of the game. For example, the level of excitement of the game is quantified based on the volume and frequency of the cheers. The game video analysis unit also analyzes the facial expressions of the spectators in real time to evaluate the level of excitement of the game. For example, the level of excitement of the game is evaluated based on smiling and surprised expressions. The game video analysis unit also analyzes the reactions and cheers of the spectators in an integrated manner to evaluate the level of excitement of the game. For example, the level of excitement of the game is evaluated by combining the volume of the cheers and the facial expressions of the spectators. In this way, the atmosphere of the game can be grasped by analyzing the reactions of the spectators and evaluating the level of excitement of the game.

[0055] The game video analysis unit can integrate footage from different camera angles and generate a 3D model to analyze the details of a play. For example, the game video analysis unit integrates footage from different camera angles and generates a 3D model. For example, a goal scene can be reconstructed from footage from multiple cameras and analyzed in detail. The game video analysis unit also analyzes footage from different camera angles and reproduces player movements in a 3D model. For example, it accurately grasps player movements and positions and analyzes the details of the play. The game video analysis unit also integrates footage from different camera angles and generates a 3D model of the entire game. For example, it visualizes the flow of the game and tactics in a 3D model and performs a detailed analysis. This makes it possible to analyze the details of a play by integrating footage from different camera angles and generating a 3D model.

[0056] The match video analysis unit can use the emotion estimation function to analyze the emotions of spectators in real time and evaluate the atmosphere of the match. The match video analysis unit, for example, analyzes the facial expressions of spectators in real time to estimate their emotions. For example, it evaluates the emotions of spectators based on smiling or surprised expressions. The match video analysis unit also analyzes the cheers of spectators in real time to estimate their emotions. For example, it evaluates the emotions of spectators based on the volume and tone of the cheers. The match video analysis unit also performs an integrated analysis of the facial expressions and cheers of spectators to estimate their emotions. For example, it evaluates the emotions of spectators by combining the volume and facial expressions of the cheers. In this way, it is possible to grasp the excitement of the match by analyzing the emotions of spectators in real time and evaluating the atmosphere of the match.

[0057] The judgment unit can make an emotionally fair judgment by taking into account the player's emotion estimation result. The judgment unit makes an emotionally fair judgment, for example, based on the player's emotion estimation result. For example, the accuracy of the judgment is improved by taking into account the player's tension or impatience. The judgment unit also analyzes the player's emotion estimation result in real time and reflects it in the judgment. For example, the fairness of the judgment is maintained based on the player's emotional changes. The judgment unit also comprehensively analyzes the player's emotion estimation result and makes an emotionally fair judgment. For example, bias in the judgment is eliminated based on the player's emotion score. In this way, the fairness of the match can be maintained by taking into account the player's emotions and making an emotionally fair judgment.

[0058] The judgment unit can introduce an algorithm for making a consistent judgment by comparing with past judgment data. The judgment unit, for example, analyzes past judgment data and introduces an algorithm for making a consistent judgment. For example, the current judgment is adjusted based on past judgment patterns. The judgment unit also compares past judgment data with current judgment data to make a consistent judgment. For example, it checks whether judgments in the same situation are consistent. The judgment unit also learns from past judgment data and develops an algorithm for making a consistent judgment. For example, it optimizes the current judgment based on past judgment results. In this way, the reliability of the judgment can be improved by making a consistent judgment by comparing with past judgment data.

[0059] The judging unit can make appropriate decisions by taking into account the situation of the match. For example, the judging unit analyzes important aspects of the match and makes appropriate decisions. For example, it adjusts decisions towards the end of the match or in situations where the score difference is large. The judging unit also makes appropriate decisions by taking into account the score difference of the match. For example, it adjusts the strictness of the decision when the score difference is large. The judging unit also performs an integrated analysis of the situation of the match and makes appropriate decisions. For example, it optimizes decisions based on the flow of the match and the performance of the players. In this way, fairness in the match can be maintained by making appropriate decisions by taking into account the situation of the match.

[0060] The judging unit can integrate the rules of different sports to build a judging system that supports multiple sports. The judging unit, for example, analyzes the rules of different sports to build an integrated judging system. For example, the rules of soccer and basketball can be integrated to make judging compatible with multiple sports. The judging unit also learns the rules of different sports to develop a judging system that supports multiple sports. For example, the rules of tennis and rugby can be integrated to improve the accuracy of judging. The judging unit also analyzes the rules of different sports in real time to make judging compatible with multiple sports. For example, matches of different sports can be analyzed simultaneously to make integrated judging. In this way, the rules of different sports can be integrated to build a judging system that supports multiple sports, making it possible to support multiple sports.

[0061] The judgment unit can analyze not only the player's movements but also the audio data to make a comprehensive judgment. The judgment unit, for example, analyzes the sound of the ball in real time and reflects this in its judgment. For example, it detects the sound of the ball crossing the line and makes a judgment. The judgment unit also analyzes the player's voice in real time and reflects this in its judgment. For example, it detects foul play from the player's voice and makes a judgment. The judgment unit also analyzes the sound of the ball and the player's voice in an integrated manner and makes a comprehensive judgment. For example, the sound of the ball and the player's voice are combined to improve the accuracy of the judgment. In this way, the judgment accuracy can be improved by analyzing the player's movements and audio data and making a comprehensive judgment.

[0062] The judging unit can use the emotion estimation function to take into account the emotional reactions of players and spectators and make judgments that maintain the fairness of the match. The judging unit, for example, analyzes the emotional reactions of players in real time and reflects this in its judgment. For example, it takes into account the players' nervousness and impatience to improve the accuracy of its judgment. The judging unit also analyzes the emotional reactions of spectators in real time and reflects this in its judgment. For example, it maintains the fairness of its judgment based on the cheers and boos of the spectators. The judging unit also comprehensively analyzes the emotional reactions of players and spectators to make a fair judgment. For example, it eliminates bias in the judgment based on the emotion score. This allows for the reliability of the match to be improved by taking into account the emotional reactions of players and spectators and making a judgment that maintains the fairness of the match.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The match video analysis unit analyzes not only the movements of the players but also the audio data during the match, allowing for comprehensive judgment. For example, it can detect the sound of the ball crossing the line and reflect this in its judgment. It can also analyze the voices of the players in real time to detect foul play. Furthermore, it can analyze the sound of the ball and the voices of the players in an integrated manner, improving the accuracy of judgment. This allows for the analysis of player movements and audio data to make a comprehensive judgment, thereby improving the accuracy of judgment.

[0065] The match video analysis unit can integrate footage from different camera angles and generate 3D models to analyze the details of plays. For example, a goal scene can be reconstructed from footage from multiple cameras for detailed analysis. It can also reproduce player movements in 3D models, allowing for accurate understanding of player movements and positions. Furthermore, it can generate a 3D model of the entire match, allowing for visualization of the flow of the game and tactics. By integrating footage from different camera angles and generating a 3D model, it is possible to analyze the details of plays.

[0066] The match video analysis unit can analyze the reactions and cheers of the spectators to evaluate the level of excitement of the match. For example, it can analyze the cheers of the spectators in real time and quantify the level of excitement of the match. It can also analyze the facial expressions of the spectators in real time and evaluate the level of excitement of the match based on smiles and surprised expressions. It can also analyze the reactions and cheers of the spectators in an integrated manner to evaluate the level of excitement of the match. In this way, it is possible to grasp the atmosphere of the match by analyzing the reactions of the spectators and evaluating the level of excitement of the match.

[0067] The judgment unit can introduce an algorithm to make a consistent judgment by comparing with past judgment data. For example, it can analyze past judgment data and adjust the current judgment. It can also compare past judgment data with current judgment data to check whether judgments in the same situation are consistent. Furthermore, it can develop an algorithm that learns from past judgment data and makes a consistent judgment. This allows it to make a consistent judgment by comparing with past judgment data, thereby improving the reliability of the judgment.

[0068] The judging unit can integrate the rules of different sports to build a judging system that supports multiple sports. For example, the rules of soccer and basketball can be integrated to make judgments that support multiple sports. The rules of tennis and rugby can also be integrated to improve the accuracy of judgments. Furthermore, it can analyze matches of different sports simultaneously and make integrated judgments. This allows the rules of different sports to be integrated to build a judging system that supports multiple sports, making it possible to support multiple sports.

[0069] The match video analysis unit can infer emotions from players' facial expressions and body movements, and analyze their playing intentions based on changes in their emotions. For example, by analyzing a player's facial expressions in real time, it can detect tension just before a goal is scored or disappointment after a goal is conceded, and analyze their playing intentions. It can also analyze players' playing intentions based on the speed of their movements and changes in their posture. Furthermore, it can analyze players' facial expressions and body movements in an integrated manner to infer changes in their emotions. This allows for more accurate judgment by analyzing players' emotions and understanding their playing intentions.

[0070] The match video analysis unit can use the emotion estimation function to analyze the emotions of spectators in real time and evaluate the atmosphere of the match. For example, it can analyze the facial expressions of spectators in real time and evaluate the emotions of spectators based on smiles or surprised expressions. It can also analyze the cheers of spectators in real time and evaluate the emotions of spectators based on the volume and tone of the cheers. It can also evaluate the emotions of spectators by analyzing the facial expressions and cheers in an integrated manner. In this way, it is possible to grasp the excitement of the match by analyzing the emotions of spectators in real time and evaluating the atmosphere of the match.

[0071] The judgement unit can take into account the player's emotion estimation results to make an emotionally fair judgment. For example, the accuracy of judgment can be improved by taking into account the player's nervousness or impatience. In addition, the player's emotion estimation results can be analyzed in real time and reflected in the judgment. Furthermore, the player's emotion estimation results can be analyzed comprehensively to make an emotionally fair judgment. As a result, the fairness of the match can be maintained by taking into account the player's emotions and making an emotionally fair judgment.

[0072] The judgement unit can use the emotion estimation function to take into account the emotional reactions of players and spectators and make judgments that maintain the fairness of the match. For example, the judgement unit can analyze the emotional reactions of players in real time and improve the accuracy of judgments by taking into account tension and impatience. The judgement unit can also analyze the emotional reactions of spectators in real time and maintain the fairness of judgments based on cheers and boos. Furthermore, the judgement unit can perform an integrated analysis of the emotional reactions of players and spectators to make fair judgments. This allows the judgement unit to take into account the emotional reactions of players and spectators and make judgments that maintain the fairness of the match, thereby improving the reliability of the match.

[0073] The judgment unit can refer to a player's past playing style and tactical data to predict the current play. For example, it can analyze a player's past playing style and predict the next move based on a specific playing pattern. It can also analyze the team's tactical data and predict the next play based on situations in which a specific tactic is used. It can also comprehensively analyze a player's past playing style and tactical data to predict the next move. This allows for more accurate judgment by referring to past data and predicting the current play.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The game video analysis unit analyzes game video in real time. For example, in a soccer game, it analyzes camera footage to determine whether a player is offside or has crossed the goal line. It can also determine whether a shot was taken before time expires in a basketball game, and analyze players' serve data in a tennis game to detect faults and net touches. The game video analysis unit analyzes camera footage in real time to accurately determine players' movements and the position of the ball. Step 2: The judgement unit makes a decision based on the analysis results of the match video analyzed by the match video analysis unit. For example, goal line technology is used to determine whether the ball has completely crossed the goal line. It can also determine whether a shot was taken before time expires in a basketball match, or analyze a player's serve data in a tennis match to detect faults or net touches. The judgement unit automatically makes a decision based on the analysis results.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[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 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.

[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 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).

[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] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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]

[0143] 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 match video analysis department that analyzes match video in real time, a determination unit that makes a determination based on the analysis result of the game video analyzed by the game video analysis unit. A system characterized by:

2. The game video analysis unit Analyzes camera footage to determine whether a player is offside or has crossed the goal line 2. The system of claim 1.

3. The game video analysis unit The emotions of the players are estimated from their facial expressions and body movements, and their intentions for playing are analyzed based on the changes in their emotions.

2. The system of claim 1.

4. The game video analysis unit Obtain player vital data in real time and evaluate playing performance 2. The system of claim 1.

5. The game video analysis unit Refer to a player's past playing style and tactical data to predict their current play 2. The system of claim 1.

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