System

Abstract

An object of a system according to an embodiment is to improve the efficiency and quality of sports play-by-play commentary and referee work.SOLUTION: A system includes an authentication part, a learning part, an explanation generation part, a voice change part, a judgement part, and a data analysis part. The authentication unit authenticates a face or a registration number of a player. The learning unit learns the past record, the referee determination criterion, and the past moving image. The commentary generation unit generates running commentary based on the data learned by the learning unit. The voice modification unit modifies the voice of the explanation generated by the explanation generation unit. The judgment unit judges the game. The data analysis unit analyzes the game data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there are issues with securing human resources and skill differences in sports commentary and refereeing work, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency and quality of live sports commentary and refereeing work. [Means for solving the problem]

[0006] The system according to the embodiment includes an authentication unit, a learning unit, a commentary generation unit, a voice change unit, a referee unit, and a data analysis unit. The authentication unit authenticates the faces and registration numbers of players. The learning unit learns past results, referee judgment criteria, and past videos. The commentary generation unit generates live commentary based on the data learned by the learning unit. The voice change unit changes the voice of the commentary generated by the commentary generation unit. The referee unit makes judgments on the match. The data analysis unit analyzes match data. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency and quality of live sports commentary and refereeing work. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 Basketball LIVE system according to an embodiment of the present invention authenticates players' faces and registration numbers, learns past performances, refereeing criteria, and past videos, and uses AI to provide live commentary. This allows the Basketball LIVE system to streamline live commentary and refereeing work using AI, contributing to player development and injury prevention.

[0029] The basketball LIVE system according to the embodiment includes an authentication unit, a learning unit, a commentary generation unit, a voice modification unit, a referee unit, and a data analysis unit. The authentication unit authenticates a player's face and registration number. For example, a face recognition algorithm is used to authenticate a player's face. A barcode or QR code can also be used to authenticate a player's registration number. RFID technology can also be used to authenticate a player's registration number. The learning unit learns past performances, referee decision criteria, and past video footage. For example, past game results can be acquired from a database and used by the AI ​​to learn. Referee decision criteria can also be learned from a rulebook or past decision data. The AI ​​can also analyze past game videos and learn from them. The commentary generation unit generates live commentary based on the data learned by the learning unit. For example, when a player scores a point, a commentary such as "This player is currently leading the scoring rankings this season" can be generated. Commentary can also be generated based on a player's past performance. Commentary can also be generated in real time according to the game situation. The voice modification unit modifies the voice of the commentary generated by the commentary generation unit. For example, commentary such as "That was a great shot!" can be provided in the voice of an anime character. Commentary can also be provided in the voice of a popular celebrity. The commentary voice can also be customized to suit user preferences. The refereeing department makes decisions during the game. For example, AI referees accurately determine fouls during the game and notify players. They can also make accurate decisions through video review. They can also make decisions based on the rules of the game. The data analysis department analyzes game data. For example, it evaluates players' performance during the game in real time and displays rankings. It can also analyze player movement data to identify movements that pose a high risk of injury. It can also generate statistical information based on game data. As a result, the Basketball LIVE system according to the embodiment can improve the efficiency of live commentary and refereeing work using AI, contributing to player development and injury prevention. For example, detailed commentary by AI deepens viewer understanding, and AI refereeing improves the fairness of the game. Furthermore, player development and injury prevention are expected to improve the overall level of basketball.

[0030] When a player scores a goal, the commentary generation unit can provide a commentary that the player is at the top of the rankings for this season. For example, when a player scores a goal, the commentary generation unit provides a comment such as, "This player is at the top of the scoring rankings for this season." The commentary generation unit can also generate commentary based on the player's past performance. It can also generate commentary in real time according to the situation of the game. This makes it possible to provide detailed commentary on a player's score.

[0031] The voice changer can provide commentary in the voice of an anime character, such as "That was a great shot!". The voice changer can also provide commentary in the voice of a popular celebrity. The voice of the commentator can also be changed according to the user's preferences. This allows for providing commentary that is familiar to viewers.

[0032] The commentary generation unit can provide commentary during youth matches, such as "This player is a promising talent." The commentary generation unit can also generate commentary based on a player's past performance. It can also generate commentary in real time according to the situation of the match. This can contribute to the discovery and development of promising players.

[0033] The refereeing department can accurately judge fouls during a game and notify players. For example, the refereeing department can use AI referees to accurately judge fouls during a game and notify players. They can also use video review to make accurate decisions. They can also make decisions based on the rules of the game. This improves the fairness of the game.

[0034] The data analysis unit can analyze match data and evaluate the performance of specific players. For example, the data analysis unit can evaluate players' performance during a match in real time and display a ranking. It can also analyze players' movement data and identify movements that pose a high risk of injury. It can also generate statistical information based on the match data. This allows for quick evaluation of players' performance.

[0035] The data analysis unit can analyze the play data of players and identify promising players. For example, the data analysis unit can analyze the play data of players and identify promising players. It can also analyze the movement data of players and identify movements that pose a high risk of injury. It can also generate statistical information based on match data. This allows for the rapid discovery of promising players.

[0036] The data analysis unit can analyze the player's movement data, identify movements that pose a high risk of injury, and warn the player to be careful. The data analysis unit can, for example, analyze the player's movement data, identify movements that pose a high risk of injury, and warn the player to be careful. It can also track the player's health data over the long term and detect injury risks early. It can also generate statistical information based on match data. This allows for prompt injury prevention for players.

[0037] The commentary generation unit can analyze the player's biometric data in real time and provide commentary based on the player's condition. For example, the commentary generation unit monitors the player's heart rate and fatigue level in real time and provides commentary based on that data. For example, when a player's heart rate suddenly rises, the commentary generation unit can provide commentary such as, "This player seems to be very nervous right now." It can also generate commentary based on the player's biometric data. It can also generate commentary in real time according to the situation of the game. This makes it possible to provide detailed commentary based on the player's condition.

[0038] The commentary generation unit can analyze tactics during a match and provide commentary on the intentions behind team tactical changes and player movements. For example, the commentary generation unit can analyze team formations and tactical changes during a match in real time and provide commentary on their intentions. For example, the commentary generation unit can provide commentary such as, "This team has now switched to zone defense." It can also analyze the intentions behind player movements and generate commentary. It can also generate commentary in real time according to the situation of the match. This allows for detailed commentary on tactical changes and the intentions behind player movements during a match.

[0039] The commentary generation unit can also be applied to other sports, generating commentary specific to each sport. For example, in a soccer game, the commentary generation unit analyzes the movements and tactics of players and provides commentary specific to soccer. For example, the commentary generation unit may provide commentary such as, "This player is currently in an offside position." In addition, in a baseball game, the commentary generation unit can analyze the movements and tactics of players and provide commentary specific to baseball. Commentary can also be generated based on game data of other sports. This enables commentary that is compatible with a variety of sports.

[0040] The commentary generation unit can analyze interviews with players during a match in real time and reflect the content of the interviews in the commentary. The commentary generation unit can, for example, analyze interviews with players conducted during a match in real time and reflect the content of the interviews in the commentary. For example, the commentary generation unit can provide a comment such as, "This player said he was very confident before the match." The commentary can also be generated based on the content of the player's interview. The commentary can also be generated in real time according to the situation of the match. This allows the content of the player's interview to be reflected in the commentary in real time.

[0041] The voice modification unit can customize the tone and speaking style of the commentary according to the user's preferences. The voice modification unit, for example, provides an interface that allows the user to customize the tone and speaking style of the commentary. For example, the voice modification unit adds a function to adjust the speed and pitch of the commentary. It can also adjust the emotional expression of the commentary. It can also change the voice of the commentary according to the user's preferences. This makes it possible to provide commentary that suits the user's preferences.

[0042] The voice change unit can provide commentary in an interactive format by simultaneously using multiple characters to provide commentary. The voice change unit develops a system in which multiple characters provide commentary in an interactive format. For example, an anime character and a popular celebrity can alternate between providing commentary. It is also possible for multiple characters to provide commentary in a conversational format. It is also possible to change the commentary character according to the user's preferences. This allows for commentary in an interactive format to be provided.

[0043] The voice changer can automatically translate the commentary voice into different languages ​​to accommodate international audiences. For example, the voice changer develops a system that automatically translates the commentary voice into different languages ​​in real time. For example, it can support multiple languages ​​such as English, French, and Chinese. It can also change the language of the commentary according to the user's selection. It can also translate the content of the commentary into different languages, thereby accommodating international audiences.

[0044] The voice modification unit can link the voice of the commentator with a visual avatar to add a visual entertainment element. The voice modification unit can, for example, develop a system that links the voice of the commentator with a visual avatar. For example, when an animated character gives a commentary, the character moves on the screen. It is also possible to add a visual entertainment element. It is also possible to change the commentator's avatar according to the user's preferences. This allows for the addition of a visual entertainment element.

[0045] The commentary generation unit can analyze growth data of youth and mini-basketball players and include future growth predictions in the commentary. For example, the commentary generation unit tracks growth data of youth and mini-basketball players over the long term and predicts future growth based on that data. For example, the commentary generation unit may provide commentary such as, "This player has the potential to grow even more in the future." It can also generate commentary based on player growth data. It can also generate commentary in real time according to the situation of the game. This makes it possible to include future growth predictions of players in the commentary.

[0046] The commentary generation unit can provide educational commentary by pointing out players' technical mistakes and areas for improvement during a match in real time. For example, the commentary generation unit analyzes players' technical mistakes in real time during a match and provides commentary pointing out areas for improvement. For example, the commentary generation unit may provide commentary such as, "This player is looking at the ball too much when dribbling." Commentary can also be generated based on players' technical mistakes. Commentary can also be generated in real time according to the situation of the match. This makes it possible to provide educational commentary by pointing out players' technical mistakes and areas for improvement in real time.

[0047] The commentary generation unit can provide commentary specialized for parents and coaches of youth and mini-basketball games. The commentary generation unit develops a system that provides commentary specialized for parents and coaches of youth and mini-basketball games, for example. For example, commentary that emphasizes the growth and technical progress of players can be provided. Commentary that includes technical guidance for parents and coaches can also be provided. Commentary can also be generated in real time according to the situation of the game. This makes it possible to provide commentary specialized for parents and coaches.

[0048] The commentary generation unit can evaluate the performance of players during a match and provide information for scouts. For example, the commentary generation unit develops a system that evaluates the performance of players in real time during a match and provides the data to scouts. For example, the commentary generation unit may provide commentary such as, "This player has excellent shooting technique." The commentary can also be generated based on the performance data of players. The commentary can also be generated in real time according to the situation of the match. This makes it possible to provide information for scouts.

[0049] The commentary generation unit can analyze match data for each region and reflect region-specific tactics and playing styles in the commentary. The commentary generation unit, for example, analyzes match data for each region and reflects region-specific tactics and playing styles in the commentary. For example, the commentary generation unit may provide a commentary such as, "Teams in this region frequently use zone defense." The commentary can also be generated based on the match data for the region. The commentary can also be generated in real time according to the situation of the match. This allows region-specific tactics and playing styles to be reflected in the commentary.

[0050] The commentary generation unit can evaluate the performance of players during a match in real time and display a ranking. The commentary generation unit develops a system that evaluates the performance of players in real time during a match and displays a ranking based on that data, for example. For example, the commentary generation unit provides commentary such as "This player has scored the most goals in the current match." It can also generate commentary based on the performance data of players. It can also generate commentary in real time according to the situation of the match. This makes it possible to evaluate the performance of players in real time and display a ranking.

[0051] The commentary generation unit can change the commentary of a general official match to content specialized for educational institutions and sports clubs. The commentary generation unit develops a system that changes the commentary of a general official match to content specialized for educational institutions and sports clubs, for example. For example, the commentary generation unit can emphasize technical commentary or tactical commentary. It can also provide commentary specialized for educational institutions and sports clubs. It can also generate commentary in real time according to the situation of the match. This makes it possible to provide commentary specialized for educational institutions and sports clubs.

[0052] The commentary generation unit can analyze interviews with players during a match in real time and reflect the content of the interviews in the commentary. The commentary generation unit can, for example, analyze interviews with players conducted during a match in real time and reflect the content of the interviews in the commentary. For example, the commentary generation unit can provide a comment such as, "This player said he was very confident before the match." The commentary can also be generated based on the content of the player's interview. The commentary can also be generated in real time according to the situation of the match. This allows the content of the player's interview to be reflected in the commentary in real time.

[0053] The refereeing department will be able to make more accurate decisions by 3D modeling the movements of players during a match. For example, the refereeing department will develop a system that will 3D model the movements of players during a match and make accurate decisions based on that data. For example, it will be used to judge fouls and offsides. It will also be able to analyze players' movements in real time and create 3D modeling. It will also be able to make accurate decisions depending on the situation of the match. This will enable more accurate decisions to be made by 3D modeling players' movements.

[0054] The refereeing department can analyze past decision-making data and develop an algorithm to improve the consistency of decisions. For example, the refereeing department can analyze past decision-making data and develop an algorithm to improve the consistency of decisions based on that data. For example, the algorithm can be improved to ensure that decisions are made consistently in the same situation. The algorithm can also be improved based on past decision-making data. The consistency of decisions can also be improved depending on the situation of the match. This allows the development of an algorithm to improve the consistency of decisions.

[0055] The refereeing department can also be applied to other sports, making judgments specific to each sport. For example, in a soccer game, the refereeing department analyzes the movements and positioning of players and makes judgments specific to soccer. For example, it may make a judgment such as, "This player is in an offside position." In a baseball game, the refereeing department can analyze the movements and positioning of players and make judgments specific to baseball. It can also make judgments based on game data from other sports. This makes it possible to make judgments that are compatible with a variety of sports.

[0056] The data analysis unit can analyze match data in real time and generate statistical information instantly. The data analysis unit develops a system that analyzes match data in real time and generates statistical information instantly based on that data, for example. For example, it generates statistical information such as "this player has scored the most goals in the current match." It can also generate statistical information based on match data. It can also generate statistical information in real time according to the situation of the match. This makes it possible to analyze match data in real time and generate statistical information instantly.

[0057] The data analysis unit can track a player's performance data over the long term and analyze growth trends. The data analysis unit develops a system that, for example, tracks a player's performance data over the long term and analyzes growth trends based on that data. For example, the data analysis unit analyzes growth trends such as "this player's shooting accuracy is improving." Growth trends can also be analyzed based on the player's performance data. Growth trends can also be analyzed according to the situation of the game. This makes it possible to track a player's performance data over the long term and analyze growth trends.

[0058] The data analysis unit can integrate game data with other sports data to perform cross-sport analysis. For example, the data analysis unit develops a system that integrates game data with other sports data to perform cross-sport analysis. For example, basketball and soccer data can be integrated and analyzed. Data between different sports can also be compared. Cross-sport analysis can also be performed depending on the situation of the game. This makes cross-sport analysis possible.

[0059] The data analysis department can share match data with educational institutions and research institutions and use it in sports science research. For example, the data analysis department can share match data with educational institutions and research institutions and develop a system for sports science research based on that data. For example, player performance data can be used for research. Sports science research can also be conducted based on match data. Data can also be shared with educational institutions and research institutions. This allows match data to be shared with educational institutions and research institutions and used in sports science research.

[0060] The data analysis unit can analyze a player's training data and propose an optimal training plan. The data analysis unit, for example, analyzes a player's training data and develops a system that proposes an optimal training plan based on that data. For example, the system may make a proposal such as, "This player should do specific training to improve his shooting accuracy." It can also propose training plans based on a player's training data. It can also propose training plans based on a player's performance data. This makes it possible to analyze a player's training data and propose an optimal training plan.

[0061] The data analysis unit can analyze a player's technical weaknesses and provide individual guidance on areas for improvement. For example, the data analysis unit can analyze a player's technical weaknesses and develop a system that provides individual guidance on areas for improvement based on that data. For example, the system can provide guidance such as, "This player is looking at the ball too much when dribbling." It can also provide guidance on areas for improvement based on a player's technical weaknesses. It can also provide guidance on areas for improvement based on a player's performance data. This allows the system to analyze a player's technical weaknesses and provide individual guidance on areas for improvement.

[0062] The data analysis unit can compare player development data with other sports and propose cross-sport development methods. The data analysis unit, for example, develops a system that compares player development data with other sports and proposes cross-sport development methods based on that data. For example, it integrates development methods for basketball and soccer. It can also compare development data between different sports. It can also propose development methods based on player development data. This makes it possible to propose cross-sport development methods.

[0063] The data analysis unit can share player development data with educational institutions and sports clubs to improve the development program. For example, the data analysis unit develops a system that shares player development data with educational institutions and sports clubs and improves the development program based on that data. For example, the data analysis unit adjusts training programs based on player growth data. It can also share data with educational institutions and sports clubs. It can also improve development programs based on player development data. This allows development data to be shared and development programs to be improved.

[0064] The data analysis unit can analyze the movement data of athletes, identify movements that carry a high risk of injury, and propose preventive measures. For example, the data analysis unit develops a system that analyzes the movement data of athletes and identifies movements that carry a high risk of injury based on that data. For example, it can propose preventive measures such as, "This athlete is putting too much strain on his knees when jumping." It can also propose preventive measures based on the movement data of athletes. It can also identify movements that carry a high risk of injury depending on the situation of the game and propose preventive measures. This makes it possible to identify movements that carry a high risk of injury and propose preventive measures.

[0065] The data analysis department tracks players' health data over the long term and can detect injury risks early. The data analysis department, for example, tracks players' health data over the long term and develops a system that uses that data to detect injury risks early. For example, it can detect risks early, such as "this player is experiencing increased strain on his knee." It can also detect risks early based on players' health data. It can also detect injury risks early depending on the situation of the game. This makes it possible to track players' health data over the long term and detect injury risks early.

[0066] The data analysis unit can share injury prevention data with other sports and propose cross-sport injury prevention measures. For example, the data analysis unit develops a system that shares injury prevention data with other sports and proposes cross-sport injury prevention measures based on that data. For example, the data analysis unit integrates injury prevention measures for basketball and soccer. It can also share injury prevention data between different sports. It can also propose preventive measures based on the injury prevention data. This makes it possible to propose cross-sport injury prevention measures.

[0067] The data analysis unit can share injury prevention data with medical institutions and strengthen medical support. The data analysis unit, for example, develops a system that shares injury prevention data with medical institutions and strengthens medical support based on that data. For example, medical advice is provided based on the player's health data. Data can also be shared with medical institutions. Medical support can also be strengthened based on injury prevention data. In this way, injury prevention data can be shared with medical institutions and strengthen medical support.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The Basketball LIVE system can also be equipped with elements that monitor players' biometric data in real time and manage their health. For example, it can monitor a player's heart rate and oxygen saturation level and issue an alert if an abnormality is detected. It can also analyze a player's fatigue level and suggest appropriate rest times. Furthermore, it can adjust training plans based on player health data and help prevent injuries. This allows for efficient health management of players.

[0070] The Basketball LIVE system can also be equipped with an element that analyzes players' technical mistakes during a game in real time and points out areas for improvement. For example, if a player misses a shot, the system can point out that "this player's arm angle is bad when shooting." It can also analyze players' dribbling and passing errors and suggest specific areas for improvement. Furthermore, it can comprehensively evaluate players' performance after a game and provide guidance on training priorities. This allows for efficient improvement of players' techniques.

[0071] The Basketball LIVE system can also be equipped with an element that tracks a player's performance data over the long term and analyzes their growth trends. For example, a player's shooting success rate and passing success rate can be tracked over the long term to analyze their growth trends. It can also evaluate the effectiveness of training based on a player's physical data. Furthermore, it can predict future growth and propose training plans based on a player's growth data. This allows for efficient support of a player's growth.

[0072] The Basketball LIVE system can also be equipped with elements that allow for 3D modeling of player movements during a game, enabling more accurate judgment. For example, player movements can be 3D modeled in real time to judge fouls and offsides. It can also analyze player movements and make accurate judgments based on the situation in the game. Furthermore, the 3D modeling data can be used for post-game reviews and training, allowing for more accurate judgment of games.

[0073] The Basketball LIVE system can also be equipped with an element that analyzes a player's training data and proposes the optimal training plan. For example, it can propose a training plan to improve shooting accuracy based on a player's training data. It can also propose a strength training plan based on a player's physical data. Furthermore, it can evaluate the effectiveness of training and adjust the plan based on the player's performance data. This allows players to train more efficiently.

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

[0075] Step 1: The authentication unit authenticates the player's face and registration number. For example, a facial recognition algorithm can be used to authenticate the player's face. The registration number can also be authenticated using a barcode or QR code. Furthermore, the player's registration number can also be authenticated using RFID technology. Step 2: The learning unit learns from past results, referee decision criteria, and past video. For example, past match results can be retrieved from a database and the AI ​​learns from them. It is also possible for the AI ​​to learn from the referee decision criteria in rulebooks and past decision data. It is also possible for the AI ​​to learn from analyzing video of past matches. Step 3: The commentary generation unit generates live commentary based on the data learned by the learning unit. For example, when a player scores a goal, it generates commentary such as, "This player is currently at the top of the scoring rankings this season." It can also generate commentary based on a player's past performance. It can also generate commentary in real time according to the situation of the game. Step 4: The voice changer changes the voice of the commentary generated by the commentary generator. For example, the voice changer can provide commentary in the voice of an anime character, such as "That was a great shot!". It is also possible to provide commentary in the voice of a popular celebrity. Furthermore, it is possible to change the voice of the commentary according to the user's preferences. Step 5: The refereeing department makes a decision on the game. For example, an AI referee can accurately judge fouls during the game and notify the players. They can also use video review to make accurate decisions. They can also make decisions based on the rules of the game. Step 6: The data analysis unit analyzes the match data. For example, it evaluates the performance of players during the match in real time and displays rankings. It can also analyze the players' movement data and identify movements that pose a high risk of injury. It can also generate statistical information based on the match data.

[0076] (Example 2) The Basketball LIVE system according to an embodiment of the present invention authenticates players' faces and registration numbers, learns past performances, refereeing criteria, and past videos, and uses AI to provide live commentary. This allows the Basketball LIVE system to streamline live commentary and refereeing work using AI, contributing to player development and injury prevention.

[0077] The basketball LIVE system according to the embodiment includes an authentication unit, a learning unit, a commentary generation unit, a voice modification unit, a referee unit, and a data analysis unit. The authentication unit authenticates a player's face and registration number. For example, a face recognition algorithm is used to authenticate a player's face. A barcode or QR code can also be used to authenticate a player's registration number. RFID technology can also be used to authenticate a player's registration number. The learning unit learns past performances, referee decision criteria, and past video footage. For example, past game results can be acquired from a database and used by the AI ​​to learn. Referee decision criteria can also be learned from a rulebook or past decision data. The AI ​​can also analyze past game videos and learn from them. The commentary generation unit generates live commentary based on the data learned by the learning unit. For example, when a player scores a point, a commentary such as "This player is currently leading the scoring rankings this season" can be generated. Commentary can also be generated based on a player's past performance. Commentary can also be generated in real time according to the game situation. The voice modification unit modifies the voice of the commentary generated by the commentary generation unit. For example, commentary such as "That was a great shot!" can be provided in the voice of an anime character. Commentary can also be provided in the voice of a popular celebrity. The commentary voice can also be customized to suit user preferences. The refereeing department makes decisions during the game. For example, AI referees accurately determine fouls during the game and notify players. They can also make accurate decisions through video review. They can also make decisions based on the rules of the game. The data analysis department analyzes game data. For example, it evaluates players' performance during the game in real time and displays rankings. It can also analyze player movement data to identify movements that pose a high risk of injury. It can also generate statistical information based on game data. As a result, the Basketball LIVE system according to the embodiment can improve the efficiency of live commentary and refereeing work using AI, contributing to player development and injury prevention. For example, detailed commentary by AI deepens viewer understanding, and AI refereeing improves the fairness of the game. Furthermore, player development and injury prevention are expected to improve the overall level of basketball.

[0078] When a player scores a goal, the commentary generation unit can provide a commentary that the player is at the top of the rankings for this season. For example, when a player scores a goal, the commentary generation unit provides a comment such as, "This player is at the top of the scoring rankings for this season." The commentary generation unit can also generate commentary based on the player's past performance. It can also generate commentary in real time according to the situation of the game. This makes it possible to provide detailed commentary on a player's score.

[0079] The voice changer can provide commentary in the voice of an anime character, such as "That was a great shot!". The voice changer can also provide commentary in the voice of a popular celebrity. The voice of the commentator can also be changed according to the user's preferences. This allows for providing commentary that is familiar to viewers.

[0080] The commentary generation unit can provide commentary during youth matches, such as "This player is a promising talent." The commentary generation unit can also generate commentary based on a player's past performance. It can also generate commentary in real time according to the situation of the match. This can contribute to the discovery and development of promising players.

[0081] The refereeing department can accurately judge fouls during a game and notify players. For example, the refereeing department can use AI referees to accurately judge fouls during a game and notify players. They can also use video review to make accurate decisions. They can also make decisions based on the rules of the game. This improves the fairness of the game.

[0082] The data analysis unit can analyze match data and evaluate the performance of specific players. For example, the data analysis unit can evaluate players' performance during a match in real time and display a ranking. It can also analyze players' movement data and identify movements that pose a high risk of injury. It can also generate statistical information based on the match data. This allows for quick evaluation of players' performance.

[0083] The data analysis unit can analyze the play data of players and identify promising players. For example, the data analysis unit can analyze the play data of players and identify promising players. It can also analyze the movement data of players and identify movements that pose a high risk of injury. It can also generate statistical information based on match data. This allows for the rapid discovery of promising players.

[0084] The data analysis unit can analyze the player's movement data, identify movements that pose a high risk of injury, and warn the player to be careful. The data analysis unit can, for example, analyze the player's movement data, identify movements that pose a high risk of injury, and warn the player to be careful. It can also track the player's health data over the long term and detect injury risks early. It can also generate statistical information based on match data. This allows for prompt injury prevention for players.

[0085] The commentary generation unit can analyze the player's biometric data in real time and provide commentary based on the player's condition. For example, the commentary generation unit monitors the player's heart rate and fatigue level in real time and provides commentary based on that data. For example, when a player's heart rate suddenly rises, the commentary generation unit can provide commentary such as, "This player seems to be very nervous right now." It can also generate commentary based on the player's biometric data. It can also generate commentary in real time according to the situation of the game. This makes it possible to provide detailed commentary based on the player's condition.

[0086] The commentary generation unit can analyze tactics during a match and provide commentary on the intentions behind team tactical changes and player movements. For example, the commentary generation unit can analyze team formations and tactical changes during a match in real time and provide commentary on their intentions. For example, the commentary generation unit can provide commentary such as, "This team has now switched to zone defense." It can also analyze the intentions behind player movements and generate commentary. It can also generate commentary in real time according to the situation of the match. This allows for detailed commentary on tactical changes and the intentions behind player movements during a match.

[0087] The commentary generation unit uses the emotion estimation function to analyze the emotional reactions of the spectators and provide commentary according to the spectators' level of excitement. The commentary generation unit, for example, analyzes the spectators' facial expressions and tone of voice to estimate the emotional reactions in real time. For example, if the spectators are excited, the commentary generation unit may provide a commentary such as "The spectators' excitement is at its peak." It is also possible to generate commentary based on the spectators' emotional reactions. It is also possible to generate commentary in real time according to the situation of the match. This makes it possible to provide commentary based on the spectators' emotional reactions.

[0088] The commentary generation unit can also be applied to other sports, generating commentary specific to each sport. For example, in a soccer game, the commentary generation unit analyzes the movements and tactics of players and provides commentary specific to soccer. For example, the commentary generation unit may provide commentary such as, "This player is currently in an offside position." In addition, in a baseball game, the commentary generation unit can analyze the movements and tactics of players and provide commentary specific to baseball. Commentary can also be generated based on game data of other sports. This enables commentary that is compatible with a variety of sports.

[0089] The commentary generation unit can analyze interviews with players during a match in real time and reflect the content of the interviews in the commentary. The commentary generation unit can, for example, analyze interviews with players conducted during a match in real time and reflect the content of the interviews in the commentary. For example, the commentary generation unit can provide a comment such as, "This player said he was very confident before the match." The commentary can also be generated based on the content of the player's interview. The commentary can also be generated in real time according to the situation of the match. This allows the content of the player's interview to be reflected in the commentary in real time.

[0090] The commentary generation unit can use the emotion estimation function to analyze the emotional state of a player and provide commentary based on the player's psychological state. The commentary generation unit, for example, analyzes the player's facial expressions and movements to estimate the emotional state in real time. For example, the commentary generation unit can provide commentary such as, "This player is currently very focused." It can also generate commentary based on the player's emotional state. It can also generate commentary in real time according to the situation of the game. This makes it possible to provide detailed commentary based on the player's psychological state.

[0091] The voice modification unit can customize the tone and speaking style of the commentary according to the user's preferences. The voice modification unit, for example, provides an interface that allows the user to customize the tone and speaking style of the commentary. For example, the voice modification unit adds a function to adjust the speed and pitch of the commentary. It can also adjust the emotional expression of the commentary. It can also change the voice of the commentary according to the user's preferences. This makes it possible to provide commentary that suits the user's preferences.

[0092] The voice change unit can provide commentary in an interactive format by simultaneously using multiple characters to provide commentary. The voice change unit develops a system in which multiple characters provide commentary in an interactive format. For example, an anime character and a popular celebrity can alternate between providing commentary. It is also possible for multiple characters to provide commentary in a conversational format. It is also possible to change the commentary character according to the user's preferences. This allows for commentary in an interactive format to be provided.

[0093] The voice change unit can use the emotion estimation function to select a voice tone that corresponds to the emotional state of the user. For example, the voice change unit analyzes the emotional state of the user in real time and selects the tone of the commentary voice based on the data. For example, a calm tone can be selected when the user is relaxed, or a lively tone can be selected when the user is excited. The commentary voice can also be changed according to the emotional state of the user. This makes it possible to provide a voice tone that corresponds to the emotional state of the user.

[0094] The voice changer can automatically translate the commentary voice into different languages ​​to accommodate international audiences. For example, the voice changer develops a system that automatically translates the commentary voice into different languages ​​in real time. For example, it can support multiple languages ​​such as English, French, and Chinese. It can also change the language of the commentary according to the user's selection. It can also translate the content of the commentary into different languages, thereby accommodating international audiences.

[0095] The voice modification unit can link the voice of the commentator with a visual avatar to add a visual entertainment element. The voice modification unit can, for example, develop a system that links the voice of the commentator with a visual avatar. For example, when an animated character gives a commentary, the character moves on the screen. It is also possible to add a visual entertainment element. It is also possible to change the commentator's avatar according to the user's preferences. This allows for the addition of a visual entertainment element.

[0096] The voice change unit can use the emotion estimation function to select a character according to the user's emotion. For example, the voice change unit will develop a system that analyzes the user's emotional state in real time and selects a character based on that data. For example, a calm character can be selected when the user is relaxed. Also, a lively character can be selected when the user is excited. It is also possible to change the commentary character according to the user's emotional state. This makes it possible to select a character according to the user's emotion.

[0097] The commentary generation unit can analyze growth data of youth and mini-basketball players and include future growth predictions in the commentary. For example, the commentary generation unit tracks growth data of youth and mini-basketball players over the long term and predicts future growth based on that data. For example, the commentary generation unit may provide commentary such as, "This player has the potential to grow even more in the future." It can also generate commentary based on player growth data. It can also generate commentary in real time according to the situation of the game. This makes it possible to include future growth predictions of players in the commentary.

[0098] The commentary generation unit can provide educational commentary by pointing out players' technical mistakes and areas for improvement during a match in real time. For example, the commentary generation unit analyzes players' technical mistakes in real time during a match and provides commentary pointing out areas for improvement. For example, the commentary generation unit may provide commentary such as, "This player is looking at the ball too much when dribbling." Commentary can also be generated based on players' technical mistakes. Commentary can also be generated in real time according to the situation of the match. This makes it possible to provide educational commentary by pointing out players' technical mistakes and areas for improvement in real time.

[0099] The commentary generation unit can use the emotion estimation function to analyze the emotional state of young players and provide commentary that will motivate them. For example, the commentary generation unit analyzes the facial expressions and movements of young players to estimate their emotional state in real time. For example, the commentary generation unit can provide commentary such as, "This player is currently very focused." It can also generate commentary based on the emotional state of young players. It can also generate commentary in real time according to the situation of the game. This makes it possible to provide commentary that will motivate young players.

[0100] The commentary generation unit can provide commentary specialized for parents and coaches of youth and mini-basketball games. The commentary generation unit develops a system that provides commentary specialized for parents and coaches of youth and mini-basketball games, for example. For example, commentary that emphasizes the growth and technical progress of players can be provided. Commentary that includes technical guidance for parents and coaches can also be provided. Commentary can also be generated in real time according to the situation of the game. This makes it possible to provide commentary specialized for parents and coaches.

[0101] The commentary generation unit can evaluate the performance of players during a match and provide information for scouts. For example, the commentary generation unit develops a system that evaluates the performance of players in real time during a match and provides the data to scouts. For example, the commentary generation unit may provide commentary such as, "This player has excellent shooting technique." The commentary can also be generated based on the performance data of players. The commentary can also be generated in real time according to the situation of the match. This makes it possible to provide information for scouts.

[0102] The commentary generation unit uses the emotion estimation function to analyze the emotional reactions of the spectators and provide commentary according to the spectators' level of excitement. The commentary generation unit, for example, analyzes the spectators' facial expressions and tone of voice to estimate the emotional reactions in real time. For example, if the spectators are excited, the commentary generation unit may provide a commentary such as "The spectators' excitement is at its peak." It is also possible to generate commentary based on the spectators' emotional reactions. It is also possible to generate commentary in real time according to the situation of the match. This makes it possible to provide commentary based on the spectators' emotional reactions.

[0103] The commentary generation unit can analyze match data for each region and reflect region-specific tactics and playing styles in the commentary. The commentary generation unit, for example, analyzes match data for each region and reflects region-specific tactics and playing styles in the commentary. For example, the commentary generation unit may provide a commentary such as, "Teams in this region frequently use zone defense." The commentary can also be generated based on the match data for the region. The commentary can also be generated in real time according to the situation of the match. This allows region-specific tactics and playing styles to be reflected in the commentary.

[0104] The commentary generation unit can evaluate the performance of players during a match in real time and display a ranking. The commentary generation unit develops a system that evaluates the performance of players in real time during a match and displays a ranking based on that data, for example. For example, the commentary generation unit provides commentary such as "This player has scored the most goals in the current match." It can also generate commentary based on the performance data of players. It can also generate commentary in real time according to the situation of the match. This makes it possible to evaluate the performance of players in real time and display a ranking.

[0105] The commentary generation unit uses the emotion estimation function to analyze the emotional reactions of local spectators and provide commentary specific to the region. For example, the commentary generation unit analyzes the facial expressions and tone of voice of local spectators to estimate their emotional reactions in real time. For example, if the spectators are excited, the commentary generation unit may provide a comment such as, "The spectators in this region are very enthusiastic." It is also possible to generate commentary based on the emotional reactions of local spectators. It is also possible to generate commentary in real time according to the situation of the game. This makes it possible to provide commentary based on the emotional reactions of local spectators.

[0106] The commentary generation unit can change the commentary of a general official match to content specialized for educational institutions and sports clubs. The commentary generation unit develops a system that changes the commentary of a general official match to content specialized for educational institutions and sports clubs, for example. For example, the commentary generation unit can emphasize technical commentary or tactical commentary. It can also provide commentary specialized for educational institutions and sports clubs. It can also generate commentary in real time according to the situation of the match. This makes it possible to provide commentary specialized for educational institutions and sports clubs.

[0107] The commentary generation unit can analyze interviews with players during a match in real time and reflect the content of the interviews in the commentary. The commentary generation unit can, for example, analyze interviews with players conducted during a match in real time and reflect the content of the interviews in the commentary. For example, the commentary generation unit can provide a comment such as, "This player said he was very confident before the match." The commentary can also be generated based on the content of the player's interview. The commentary can also be generated in real time according to the situation of the match. This allows the content of the player's interview to be reflected in the commentary in real time.

[0108] The commentary generation unit can use the emotion estimation function to provide commentary that corresponds to the emotions of the spectators. For example, the commentary generation unit analyzes the facial expressions and tone of voice of the spectators to estimate their emotional reactions in real time. For example, if the spectators are excited, the commentary generation unit may provide a comment such as, "The spectators' excitement is at its peak." It is also possible to generate commentary based on the emotional reactions of the spectators. It is also possible to generate commentary in real time according to the situation of the match. This makes it possible to provide commentary that corresponds to the emotions of the spectators.

[0109] The refereeing department will be able to make more accurate decisions by 3D modeling the movements of players during a match. For example, the refereeing department will develop a system that will 3D model the movements of players during a match and make accurate decisions based on that data. For example, it will be used to judge fouls and offsides. It will also be able to analyze players' movements in real time and create 3D modeling. It will also be able to make accurate decisions depending on the situation of the match. This will enable more accurate decisions to be made by 3D modeling players' movements.

[0110] The refereeing department can analyze past decision-making data and develop an algorithm to improve the consistency of decisions. For example, the refereeing department can analyze past decision-making data and develop an algorithm to improve the consistency of decisions based on that data. For example, the algorithm can be improved to ensure that decisions are made consistently in the same situation. The algorithm can also be improved based on past decision-making data. The consistency of decisions can also be improved depending on the situation of the match. This allows the development of an algorithm to improve the consistency of decisions.

[0111] The refereeing department can use the emotion estimation function to analyze the emotional state of the players and make decisions that take their emotional reactions into account. For example, the refereeing department can analyze the players' facial expressions and movements to estimate their emotional state in real time. For example, if a player is excited, the refereeing department can make a decision such as "This player is very excited." It can also make decisions based on the players' emotional state. It can also make decisions that take their emotional reactions into account depending on the situation in the game. This makes it possible to make decisions that take the players' emotional states into account.

[0112] The refereeing department can also be applied to other sports, making judgments specific to each sport. For example, in a soccer game, the refereeing department analyzes the movements and positioning of players and makes judgments specific to soccer. For example, it may make a judgment such as, "This player is in an offside position." In a baseball game, the refereeing department can analyze the movements and positioning of players and make judgments specific to baseball. It can also make judgments based on game data from other sports. This makes it possible to make judgments that are compatible with a variety of sports.

[0113] The refereeing department can analyze the reactions of the spectators during the match and make decisions that reflect the opinions of the spectators. For example, the refereeing department can analyze the facial expressions and tone of voice of the spectators during the match to estimate the reactions of the spectators in real time. For example, if the spectators are excited, the refereeing department can make a decision such as "The excitement of the spectators is at its peak." The refereeing department can also make decisions based on the opinions of the spectators. It can also make decisions that reflect the opinions of the spectators depending on the situation of the match. This makes it possible to make decisions that reflect the opinions of the spectators.

[0114] The refereeing department can use the emotion estimation function to analyze the referee's emotional state and support calm judgment. For example, the refereeing department can analyze the referee's facial expressions and movements to estimate the referee's emotional state in real time. For example, if the referee is excited, the department can make a judgment such as "This referee is very excited." It can also make judgments based on the referee's emotional state. It can also support calm judgment according to the situation of the game. This allows the referee's emotional state to be taken into consideration and support calm judgment.

[0115] The data analysis unit can analyze match data in real time and generate statistical information instantly. The data analysis unit develops a system that analyzes match data in real time and generates statistical information instantly based on that data, for example. For example, it generates statistical information such as "this player has scored the most goals in the current match." It can also generate statistical information based on match data. It can also generate statistical information in real time according to the situation of the match. This makes it possible to analyze match data in real time and generate statistical information instantly.

[0116] The data analysis unit can track a player's performance data over the long term and analyze growth trends. The data analysis unit develops a system that, for example, tracks a player's performance data over the long term and analyzes growth trends based on that data. For example, the data analysis unit analyzes growth trends such as "this player's shooting accuracy is improving." Growth trends can also be analyzed based on the player's performance data. Growth trends can also be analyzed according to the situation of the game. This makes it possible to track a player's performance data over the long term and analyze growth trends.

[0117] The data analysis unit can use the emotion estimation function to analyze the emotional state of a player and perform data analysis that takes emotional factors into account. The data analysis unit, for example, analyzes the player's facial expressions and movements to estimate the emotional state in real time. For example, if a player is excited, the data analysis unit can perform data analysis such as "This player is very excited." Data analysis can also be performed based on the player's emotional state. Data analysis can also be performed that takes emotional factors into account depending on the situation of the game. This makes it possible to perform data analysis that takes the player's emotional state into account.

[0118] The data analysis unit can integrate game data with other sports data to perform cross-sport analysis. For example, the data analysis unit develops a system that integrates game data with other sports data to perform cross-sport analysis. For example, basketball and soccer data can be integrated and analyzed. Data between different sports can also be compared. Cross-sport analysis can also be performed depending on the situation of the game. This makes cross-sport analysis possible.

[0119] The data analysis department can share match data with educational institutions and research institutions and use it in sports science research. For example, the data analysis department can share match data with educational institutions and research institutions and develop a system for sports science research based on that data. For example, player performance data can be used for research. Sports science research can also be conducted based on match data. Data can also be shared with educational institutions and research institutions. This allows match data to be shared with educational institutions and research institutions and used in sports science research.

[0120] The data analysis unit uses the emotion estimation function to analyze the emotional reactions of spectators and reflect the level of excitement of the spectators in the data. The data analysis unit, for example, analyzes the facial expressions and tone of voice of spectators to estimate the emotional reactions in real time. For example, if the spectators are excited, it generates data such as "The excitement of the spectators is at its peak." It is also possible to generate data based on the emotional reactions of spectators. It is also possible to reflect the level of excitement of the spectators in the data depending on the situation of the game. In this way, the emotional reactions of spectators can be reflected in the data.

[0121] The data analysis unit can analyze a player's training data and propose an optimal training plan. The data analysis unit, for example, analyzes a player's training data and develops a system that proposes an optimal training plan based on that data. For example, the system may make a proposal such as, "This player should do specific training to improve his shooting accuracy." It can also propose training plans based on a player's training data. It can also propose training plans based on a player's performance data. This makes it possible to analyze a player's training data and propose an optimal training plan.

[0122] The data analysis unit can analyze a player's technical weaknesses and provide individual guidance on areas for improvement. For example, the data analysis unit can analyze a player's technical weaknesses and develop a system that provides individual guidance on areas for improvement based on that data. For example, the system can provide guidance such as, "This player is looking at the ball too much when dribbling." It can also provide guidance on areas for improvement based on a player's technical weaknesses. It can also provide guidance on areas for improvement based on a player's performance data. This allows the system to analyze a player's technical weaknesses and provide individual guidance on areas for improvement.

[0123] The data analysis unit can use the emotion estimation function to analyze the motivation of players and provide coaching to increase their motivation. The data analysis unit, for example, analyzes the player's facial expressions and movements to estimate the player's emotional state in real time. For example, the data analysis unit can provide guidance such as, "This player is currently very focused." Coaching to increase motivation can also be provided based on the player's emotional state. Coaching to increase motivation can also be provided according to the situation of the game. This makes it possible to provide coaching to increase players' motivation.

[0124] The data analysis unit can compare player development data with other sports and propose cross-sport development methods. The data analysis unit, for example, develops a system that compares player development data with other sports and proposes cross-sport development methods based on that data. For example, it integrates development methods for basketball and soccer. It can also compare development data between different sports. It can also propose development methods based on player development data. This makes it possible to propose cross-sport development methods.

[0125] The data analysis unit can share player development data with educational institutions and sports clubs to improve the development program. For example, the data analysis unit develops a system that shares player development data with educational institutions and sports clubs and improves the development program based on that data. For example, the data analysis unit adjusts training programs based on player growth data. It can also share data with educational institutions and sports clubs. It can also improve development programs based on player development data. This allows development data to be shared and development programs to be improved.

[0126] The data analysis unit can use the emotion estimation function to analyze the emotional state of a player and propose a development plan that takes emotional factors into consideration. The data analysis unit, for example, analyzes the player's facial expressions and movements to estimate the emotional state in real time. For example, if a player is excited, the data analysis unit can propose a development plan such as "This player is very excited." It can also propose a development plan based on the player's emotional state. It can also propose a development plan that takes emotional factors into consideration depending on the situation of the game. This makes it possible to propose a development plan that takes the player's emotional state into consideration.

[0127] The data analysis unit can analyze the movement data of athletes, identify movements that carry a high risk of injury, and propose preventive measures. For example, the data analysis unit develops a system that analyzes the movement data of athletes and identifies movements that carry a high risk of injury based on that data. For example, it can propose preventive measures such as, "This athlete is putting too much strain on his knees when jumping." It can also propose preventive measures based on the movement data of athletes. It can also identify movements that carry a high risk of injury depending on the situation of the game and propose preventive measures. This makes it possible to identify movements that carry a high risk of injury and propose preventive measures.

[0128] The data analysis department tracks players' health data over the long term and can detect injury risks early. The data analysis department, for example, tracks players' health data over the long term and develops a system that uses that data to detect injury risks early. For example, it can detect risks early, such as "this player is experiencing increased strain on his knee." It can also detect risks early based on players' health data. It can also detect injury risks early depending on the situation of the game. This makes it possible to track players' health data over the long term and detect injury risks early.

[0129] The data analysis unit can use the emotion estimation function to analyze the stress levels of players and support stress management. For example, the data analysis unit analyzes the players' facial expressions and movements to estimate their stress levels in real time. For example, if a player is feeling stressed, the data analysis unit can support stress management by stating, "This player is feeling very stressed." It can also support stress management based on the player's stress level. It can also support stress management according to the situation of the game. This makes it possible to analyze the players' stress levels and support stress management.

[0130] The data analysis unit can share injury prevention data with other sports and propose cross-sport injury prevention measures. For example, the data analysis unit develops a system that shares injury prevention data with other sports and proposes cross-sport injury prevention measures based on that data. For example, the data analysis unit integrates injury prevention measures for basketball and soccer. It can also share injury prevention data between different sports. It can also propose preventive measures based on the injury prevention data. This makes it possible to propose cross-sport injury prevention measures.

[0131] The data analysis unit can share injury prevention data with medical institutions and strengthen medical support. The data analysis unit, for example, develops a system that shares injury prevention data with medical institutions and strengthens medical support based on that data. For example, medical advice is provided based on the player's health data. Data can also be shared with medical institutions. Medical support can also be strengthened based on injury prevention data. In this way, injury prevention data can be shared with medical institutions and strengthen medical support.

[0132] The data analysis unit can use the emotion estimation function to analyze the emotional state of a player and propose injury prevention measures that take emotional factors into consideration. The data analysis unit, for example, analyzes the player's facial expressions and movements to estimate the emotional state in real time. For example, if a player is excited, the data analysis unit can propose injury prevention measures such as "This player is very excited." It can also propose injury prevention measures based on the player's emotional state. It can also propose injury prevention measures that take emotional factors into consideration depending on the situation of the game. This makes it possible to propose injury prevention measures that take the player's emotional state into consideration.

[0133] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0134] The Basketball LIVE system can also be equipped with elements that estimate the emotions of the spectators and create excitement during the game based on their emotions. For example, if the spectators are excited, the system can adjust the stadium lighting and sound to create excitement. It can also display cheering messages based on the spectators' emotions. Furthermore, it can automatically edit highlights of the game based on the spectators' emotional data and share them on social media. This makes it possible to create a game that is based on the spectators' emotions.

[0135] The Basketball LIVE system can also be equipped with elements that monitor players' biometric data in real time and manage their health. For example, it can monitor a player's heart rate and oxygen saturation level and issue an alert if an abnormality is detected. It can also analyze a player's fatigue level and suggest appropriate rest times. Furthermore, it can adjust training plans based on player health data and help prevent injuries. This allows for efficient health management of players.

[0136] The Basketball LIVE system can also be equipped with an element that analyzes players' technical mistakes during a game in real time and points out areas for improvement. For example, if a player misses a shot, the system can point out that "this player's arm angle is bad when shooting." It can also analyze players' dribbling and passing errors and suggest specific areas for improvement. Furthermore, it can comprehensively evaluate players' performance after a game and provide guidance on training priorities. This allows for efficient improvement of players' techniques.

[0137] The Basketball LIVE system can also include an element that estimates the emotions of the spectators and adjusts the tone of the commentary based on the emotions of the spectators. For example, if the spectators are excited, the tone of the commentary can be made more energetic. Alternatively, if the spectators are relaxed, the tone of the commentary can be made calmer. Furthermore, the content of the commentary can be adjusted based on the emotional data of the spectators to attract the viewer's attention. This allows the system to provide commentary that is tailored to the emotions of the spectators.

[0138] The Basketball LIVE system can also be equipped with an element that tracks a player's performance data over the long term and analyzes their growth trends. For example, a player's shooting success rate and passing success rate can be tracked over the long term to analyze their growth trends. It can also evaluate the effectiveness of training based on a player's physical data. Furthermore, it can predict future growth and propose training plans based on a player's growth data. This allows for efficient support of a player's growth.

[0139] The Basketball LIVE system can also be equipped with an element that estimates the emotions of the spectators and displays cheering messages based on their emotions. For example, if the spectators are excited, a cheering message such as "That was a great play!" can be displayed. If the spectators are depressed, an encouraging message such as "Do your best next time!" can be displayed. Furthermore, the content of the cheering message can be adjusted based on the spectators' emotional data to heighten the viewers' emotions. This allows the system to provide cheering messages that correspond to the spectators' emotions.

[0140] The Basketball LIVE system can also be equipped with elements that allow for 3D modeling of player movements during a game, enabling more accurate judgment. For example, player movements can be 3D modeled in real time to judge fouls and offsides. It can also analyze player movements and make accurate judgments based on the situation in the game. Furthermore, the 3D modeling data can be used for post-game reviews and training, allowing for more accurate judgment of games.

[0141] The Basketball LIVE system can also be equipped with an element that estimates the emotions of the spectators and edits highlight scenes of the game based on those emotions. For example, it can prioritize scenes that excite the spectators and create a highlight video. It can also adjust the order of highlight scenes based on the spectators' emotional data. Furthermore, it can share highlight videos on social media to attract viewers' interest. This allows it to provide highlight scenes based on the spectators' emotions.

[0142] The Basketball LIVE system can also be equipped with an element that analyzes a player's training data and proposes the optimal training plan. For example, it can propose a training plan to improve shooting accuracy based on a player's training data. It can also propose a strength training plan based on a player's physical data. Furthermore, it can evaluate the effectiveness of training and adjust the plan based on the player's performance data. This allows players to train more efficiently.

[0143] The Basketball LIVE system can also be equipped with elements that estimate the emotions of the spectators and create excitement during the game based on their emotions. For example, if the spectators are excited, the system can adjust the stadium lighting and sound to create excitement. It can also display cheering messages based on the spectators' emotions. Furthermore, it can automatically edit highlights of the game based on the spectators' emotional data and share them on social media. This makes it possible to create a game that is based on the spectators' emotions.

[0144] The processing flow of the second embodiment will be briefly explained below.

[0145] Step 1: The authentication unit authenticates the player's face and registration number. For example, a facial recognition algorithm can be used to authenticate the player's face. The registration number can also be authenticated using a barcode or QR code. Furthermore, the player's registration number can also be authenticated using RFID technology. Step 2: The learning unit learns from past results, referee decision criteria, and past video. For example, past match results can be retrieved from a database and the AI ​​learns from them. It is also possible for the AI ​​to learn from the referee decision criteria in rulebooks and past decision data. It is also possible for the AI ​​to learn from analyzing video of past matches. Step 3: The commentary generation unit generates live commentary based on the data learned by the learning unit. For example, when a player scores a goal, it generates commentary such as, "This player is currently at the top of the scoring rankings this season." It can also generate commentary based on a player's past performance. It can also generate commentary in real time according to the situation of the game. Step 4: The voice changer changes the voice of the commentary generated by the commentary generator. For example, the voice changer can provide commentary in the voice of an anime character, such as "That was a great shot!". It is also possible to provide commentary in the voice of a popular celebrity. Furthermore, it is possible to change the voice of the commentary according to the user's preferences. Step 5: The refereeing department makes a decision on the game. For example, an AI referee can accurately judge fouls during the game and notify the players. They can also use video review to make accurate decisions. They can also make decisions based on the rules of the game. Step 6: The data analysis unit analyzes the match data. For example, it evaluates the performance of players during the match in real time and displays rankings. It can also analyze the players' movement data and identify movements that pose a high risk of injury. It can also generate statistical information based on the match data.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0159] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0174] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0180] 7, a 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0190] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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."

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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]

[0213] 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. An authentication unit that authenticates the player's face and registration number, A learning department that studies past results, referee judgment criteria, and past videos. a commentary generating unit that generates a running commentary based on the data learned by the learning unit; a voice changer that changes the voice of the commentary generated by the commentary generator; The refereeing team will be responsible for judging the match, and a data analysis unit that analyzes match data. A system characterized by:

2. The explanation generation unit Apply this to other sports and generate sport-specific commentary for each of them.

2. The system of claim 1.

3. The voice changer is Customizing the tone and manner of delivery of the commentary according to the user's preferences.

2. The system of claim 1.

4. The said Board of Appeals: The 3D modeling of the player's movements during the game is used to make more accurate decisions.

2. The system of claim 1.

5. The data analysis unit Analyze the match data in real time and generate statistics instantly 2. The system of claim 1.

6. The explanation generation unit Analyzing the emotional response of the audience and providing the commentary according to the excitement level of the audience 2. The system of claim 1.

Images