System

Abstract

An object of the system according to the embodiment is to perform live commentary and explanation of a sport in real time without a professional announcer.SOLUTION: A system according to an embodiment includes a drone camera unit, an identification unit, a learning unit, and a play-by-play unit. The drone camera unit captures an image of a player using a drone camera. The identification unit identifies a player from an image captured by the drone camera unit. The learning unit learns rules of sports and past live data. The play-by-play unit performs play-by-play and commentary in real time based on the motion of the player identified by the identification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to provide real-time commentary of elementary school sports or amateur baseball games, and there was a problem in that a professional announcer was required.

[0005] The system according to the embodiment aims to provide live commentary and commentary on sports in real time without the need for a professional announcer. [Means for solving the problem]

[0006] The system according to the embodiment includes a drone camera unit, an identification unit, a learning unit, and a commentary unit. The drone camera unit captures images of players using a drone camera. The identification unit identifies players from the images captured by the drone camera unit. The learning unit learns the rules of the sport and past commentary data. The commentary unit provides commentary and explanation in real time based on the movements of the players identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide live commentary and commentary on sports in real time without the need for a professional announcer. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The commentary system according to an embodiment of the present invention uses a drone camera to identify players, and a generation AI learns the rules of the sport and past commentaries, then provides commentary and commentary in real time. This allows the commentary system to identify player movements in real time and provide appropriate commentary and commentary.

[0029] A commentary system according to an embodiment includes a drone camera unit, an identification unit, a learning unit, and a commentary unit. The drone camera unit captures images of players. For example, it uses a camera mounted on a drone to capture images of players on the field. For example, the drone camera unit captures images of the entire field from above and tracks the movements of players in real time. The identification unit identifies players from the images captured by the drone camera unit. For example, it identifies players based on facial recognition data and uniform numbers input in advance. For example, the identification unit recognizes players' faces using facial recognition technology and identifies players based on their uniform numbers. The learning unit learns the rules of sports and past commentary data. For example, it learns the rules of baseball and commentary data from past games, and provides appropriate commentary and commentary as the game progresses. For example, the learning unit uses a generation AI to learn the rules of sports and past commentary data in advance, and provides commentary and commentary in real time. The commentary unit provides commentary and commentary in real time based on the movements of players identified by the identification unit. For example, the commentary will be in the form of, "The batter hits the ball thrown by the pitcher. It flies to center field!" The commentary section adds appropriate commentary depending on, for example, the movements of the players and the situation of the game. This allows the commentary system according to the embodiment to identify the movements of the players in real time and provide appropriate commentary and commentary.

[0030] The identification unit can identify players based on their facial recognition and uniform number. The identification unit, for example, uses facial recognition technology to recognize the player's face and identify the player based on the uniform number. For example, it detects the player's face from video captured by the drone camera unit and identifies the player using a facial recognition algorithm. The identification unit also uses uniform number recognition technology to read the player's uniform number and identify the player. For example, it uses video analysis technology to recognize the player's uniform number and identify the player. This allows for accurate identification based on the player's facial recognition and uniform number.

[0031] The learning unit can learn the individual playing styles and past performance data of players in addition to the rules of sports and past commentary data. The learning unit, for example, learns the individual playing styles and past performance data of players. For example, commentary is provided based on the players' favorite plays and past results. The learning unit also learns the movement patterns and tactical characteristics of players to provide more personalized commentary. For example, commentary is provided based on the characteristics of players. Furthermore, the learning unit learns the players' past match data and evaluates the players' performance. For example, commentary is provided based on the players' past results. In this way, more personalized commentary is possible by learning the individual playing styles and past performance data of players.

[0032] The drone camera unit can track players' movements in more detail by 3D modeling and analyzing their movements. For example, the drone camera unit can 3D model players' movements in real time based on drone camera footage and perform motion analysis. For example, it can analyze players' running speeds and movement patterns and reflect this in the commentary. The drone camera unit can also use 3D modeling technology to analyze players' movements in detail and extract characteristics of their movements. For example, it can analyze the height of players' steps and jumps and reflect this in the commentary. Furthermore, the drone camera unit can 3D model players' movements and perform motion analysis to track players' movements in more detail. For example, it can analyze the trajectory of players' movements and reflect this in the commentary. This allows players' movements to be tracked in more detail, enabling accurate commentary.

[0033] The drone camera unit acquires biometric information such as the player's heart rate and body temperature in real time, allowing the player's condition to be understood. For example, in addition to the drone's camera footage, the drone camera unit acquires biometric information such as the player's heart rate and body temperature in real time. For example, if a player's heart rate rises, that information is communicated in the commentary. The drone camera unit also acquires biometric information in real time to understand the player's condition. For example, if a player's body temperature rises, that information is communicated in the commentary. The drone camera unit also acquires biometric information of the player in real time to understand the player's condition. For example, changes in the player's heart rate and body temperature are analyzed and reflected in the commentary. This allows the player's condition to be understood in real time, allowing for appropriate commentary.

[0034] The drone camera unit also integrates footage from fixed cameras installed around the field, allowing it to identify players from multiple perspectives. For example, the drone camera unit integrates footage from a drone camera with footage from fixed cameras installed around the field to identify players from multiple perspectives. For example, it combines footage from different angles to analyze player movements. The drone camera unit also integrates footage from fixed cameras with footage from the drone camera to track player movements in more detail. For example, it analyzes player movements from multiple perspectives and reflects this in the commentary. Furthermore, the drone camera unit integrates footage from a drone camera with footage from fixed cameras to identify players from multiple perspectives. For example, it analyzes player movements from different angles and reflects this in the commentary. This allows for more accurate commentary by identifying players from multiple perspectives.

[0035] The drone camera unit can provide tactical advice based on the movements of players. For example, the drone camera unit analyzes drone camera footage in real time and provides tactical advice based on the movements of players. For example, it analyzes the patterns of players' movements and suggests appropriate tactics. The drone camera unit also analyzes players' movements in real time and provides tactical advice. For example, it analyzes the characteristics of players' movements and suggests appropriate tactics. The drone camera unit also analyzes players' movements in real time and provides tactical advice. For example, it analyzes the trajectory of players' movements and suggests appropriate tactics. In this way, by providing tactical advice based on players' movements, it is possible to suggest appropriate tactics according to the progress of the game.

[0036] The learning unit learns the rules and commentary data of different sports, and can provide commentary for multiple sports. The learning unit, for example, learns the rules and commentary data of different sports. For example, it learns the rules and commentary data of not only baseball, but also soccer and basketball. Furthermore, the learning unit allows the generation AI to learn the rules and commentary data of different sports, and provide commentary for multiple sports. For example, it provides commentary for baseball, soccer, and basketball. Furthermore, the learning unit learns the rules and commentary data of different sports, and provides commentary for multiple sports. For example, it provides commentary for not only baseball, but also soccer and basketball. This makes it possible to provide commentary for multiple sports.

[0037] The learning unit can learn information about the tactics and strategies of a match and provide tactical commentary. The learning unit, for example, learns information about the tactics and strategies of a match. For example, commentary is provided based on player movements and team tactics. The learning unit also learns information about tactics and strategies, and the generation AI provides tactical commentary. For example, commentary is provided based on tactical changes and player movements during a match. The learning unit also learns information about the tactics and strategies of a match and provides tactical commentary. For example, commentary is provided based on player movements and team tactics. This makes tactical commentary possible.

[0038] The commentary unit can analyze players' movements in real time and provide commentary that predicts future plays. For example, the commentary unit uses a generating AI to analyze players' movements in real time and provide commentary that predicts future plays. For example, the commentary unit analyzes the patterns of players' movements and predicts the next play. The commentary unit also analyzes players' movements in real time and provides commentary that predicts future plays. For example, the commentary unit analyzes the characteristics of players' movements and predicts the next play. The commentary unit also analyzes players' movements in real time and provides commentary that predicts future plays. For example, the commentary unit analyzes the trajectory of players' movements and predicts the next play. This makes it possible to provide commentary that predicts future plays.

[0039] The commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. In the commentary unit, for example, a generation AI analyzes the audience's reactions in real time and provides commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's reactions and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. This makes it possible to provide commentary based on the audience's reactions.

[0040] The commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, the generative AI in the commentary section analyzes the progress of the match in real time and provides tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. This makes it possible to provide tactical commentary based on the progress of the match.

[0041] The commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. In the commentary unit, for example, a generation AI analyzes the audience's reactions in real time and provides commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's reactions and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. This makes it possible to provide commentary based on the audience's reactions.

[0042] The commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, the generative AI in the commentary section analyzes the progress of the match in real time and provides tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. This makes it possible to provide tactical commentary based on the progress of the match.

[0043] The commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. In the commentary unit, for example, a generation AI analyzes the audience's reactions in real time and provides commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's reactions and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. This makes it possible to provide commentary based on the audience's reactions.

[0044] The commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, the generative AI in the commentary section analyzes the progress of the match in real time and provides tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. This makes it possible to provide tactical commentary based on the progress of the match.

[0045] The commentary department can analyze players' movements in real time and provide tactical advice. For example, the generative AI analyzes players' movements in real time and provides tactical advice. For example, it analyzes the patterns of players' movements and suggests appropriate tactics. The commentary department also analyzes players' movements in real time and provides tactical advice. For example, it analyzes the characteristics of players' movements and suggests appropriate tactics. The commentary department also analyzes players' movements in real time and provides tactical advice. For example, it analyzes the trajectory of players' movements and suggests appropriate tactics. In this way, by providing tactical advice based on players' movements, it is possible to suggest appropriate tactics according to the progress of the game.

[0046] The commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. In the commentary unit, for example, a generation AI analyzes the audience's reactions in real time and provides commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's reactions and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. This makes it possible to provide commentary based on the audience's reactions.

[0047] The commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, the generative AI in the commentary section analyzes the progress of the match in real time and provides tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. This makes it possible to provide tactical commentary based on the progress of the match.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The commentary system can further include a prediction unit that predicts the player's movements. The prediction unit predicts the player's next movement based on, for example, past movement data of the player. For example, it predicts which direction the batter will hit next and reflects this in the commentary. The prediction unit also analyzes the player's movement patterns and predicts the player's next movement. For example, it predicts which type of pitch the pitcher will throw next and reflects this in the commentary. Furthermore, the prediction unit analyzes the player's movements in real time and predicts the player's next movement. For example, it predicts which base a runner will advance to next and reflects this in the commentary. In this way, predicting the player's movements enables more realistic commentary.

[0050] The commentary system can further include a reaction analysis unit that analyzes audience reactions. The reaction analysis unit, for example, analyzes audience cheers and applause and reflects the reactions in the commentary. For example, when the audience cheers, the information is reflected in the commentary. The reaction analysis unit also analyzes audience reactions in real time and reflects the information in the commentary. For example, when the audience claps, the information is reflected in the commentary. The reaction analysis unit also analyzes audience reactions and reflects the information in the commentary. For example, when the audience boos, the information is reflected in the commentary. In this way, by reflecting audience reactions in the commentary, a more realistic commentary is possible.

[0051] The commentary system may further include a health monitoring unit that monitors the health status of the players. The health monitoring unit, for example, monitors the player's heart rate and body temperature in real time and reflects the information in the commentary. For example, when a player's heart rate rises, the information is reflected in the commentary. The health monitoring unit also monitors the player's health status in real time and reflects the information in the commentary. For example, when a player's body temperature rises, the information is reflected in the commentary. The health monitoring unit also monitors the player's health status and reflects the information in the commentary. For example, when a player is fatigued, the information is reflected in the commentary. In this way, by monitoring the player's health status, more accurate commentary is possible.

[0052] The commentary system may further include a 3D analysis unit that 3D models the player's movements and performs motion analysis. The 3D analysis unit, for example, 3D models the player's movements in real time and performs motion analysis. For example, it analyzes the player's running speed and movement patterns and reflects this in the commentary. The 3D analysis unit also analyzes the player's movements in detail and extracts movement characteristics. For example, it analyzes the player's step and jump height and reflects this in the commentary. Furthermore, the 3D analysis unit 3D models the player's movements and performs motion analysis to track the player's movements in more detail. For example, it analyzes the trajectory of the player's movement and reflects this in the commentary. This allows for more detailed tracking of the player's movements and enables accurate commentary.

[0053] The commentary system may further include a biometric information acquisition unit that acquires biometric information such as a player's heart rate and body temperature in real time and grasps the player's condition. The biometric information acquisition unit, for example, acquires a player's heart rate and body temperature in real time and reflects that information in the commentary. For example, when a player's heart rate rises, that information is reflected in the commentary. The biometric information acquisition unit also grasps a player's condition in real time and reflects that information in the commentary. For example, when a player's body temperature rises, that information is reflected in the commentary. The biometric information acquisition unit also acquires a player's biometric information in real time and grasps the player's condition. For example, it analyzes changes in a player's heart rate and body temperature and reflects that information in the commentary. This makes it possible to grasp a player's condition in real time and provide appropriate commentary.

[0054] The commentary system may further include a viewpoint integration unit that integrates video from fixed cameras installed around the field and identifies players from multiple viewpoints. The viewpoint integration unit, for example, integrates video from a drone camera with video from a fixed camera to identify players from multiple viewpoints. For example, it combines video from different angles to analyze player movements. The viewpoint integration unit also integrates video from a fixed camera with video from a drone camera to track player movements in more detail. For example, it analyzes player movements from multiple viewpoints and reflects the analysis in the commentary. The viewpoint integration unit also integrates video from a drone camera with video from a fixed camera to identify players from multiple viewpoints. For example, it analyzes player movements from different angles and reflects the analysis in the commentary. This allows for more accurate commentary by identifying players from multiple viewpoints.

[0055] The commentary system may further include a tactical advice unit that provides tactical advice based on player movements. The tactical advice unit, for example, analyzes player movements in real time and provides tactical advice. For example, it analyzes the patterns of player movements and suggests appropriate tactics. The tactical advice unit also analyzes player movements in real time and provides tactical advice. For example, it analyzes the characteristics of player movements and suggests appropriate tactics. The tactical advice unit also analyzes player movements in real time and provides tactical advice. For example, it analyzes the trajectory of player movements and suggests appropriate tactics. In this way, by providing tactical advice based on player movements, it is possible to suggest appropriate tactics according to the progress of the game.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The drone camera unit takes pictures of the players. For example, it uses a camera mounted on a drone to take pictures of the players on the field. The drone camera unit takes pictures of the entire field from above and tracks the players' movements in real time. Step 2: The identification unit identifies players from the footage captured by the drone camera unit. For example, players are identified based on pre-entered facial recognition data and uniform numbers. The identification unit recognizes the players' faces using facial recognition technology and identifies them based on their uniform numbers. Step 3: The learning unit learns the rules of the sport and past play-by-play data. For example, it learns the rules of baseball and play-by-play data from past games, and provides appropriate play-by-play and commentary as the game progresses. The learning unit allows the generation AI to learn the rules of the sport and past play-by-play data in advance, and then provides real-time play-by-play and commentary. Step 4: The commentary team provides real-time commentary and commentary based on the player movements identified by the classification team. For example, the commentary may say, "The batter hits the ball thrown by the pitcher. It flies to center field!" The commentary team provides appropriate commentary based on the player movements and the situation of the game.

[0058] (Example 2) The commentary system according to an embodiment of the present invention uses a drone camera to identify players, and a generation AI learns the rules of the sport and past commentaries, then provides commentary and commentary in real time. This allows the commentary system to identify player movements in real time and provide appropriate commentary and commentary.

[0059] A commentary system according to an embodiment includes a drone camera unit, an identification unit, a learning unit, and a commentary unit. The drone camera unit captures images of players. For example, it uses a camera mounted on a drone to capture images of players on the field. For example, the drone camera unit captures images of the entire field from above and tracks the movements of players in real time. The identification unit identifies players from the images captured by the drone camera unit. For example, it identifies players based on facial recognition data and uniform numbers input in advance. For example, the identification unit recognizes players' faces using facial recognition technology and identifies players based on their uniform numbers. The learning unit learns the rules of sports and past commentary data. For example, it learns the rules of baseball and commentary data from past games, and provides appropriate commentary and commentary as the game progresses. For example, the learning unit uses a generation AI to learn the rules of sports and past commentary data in advance, and provides commentary and commentary in real time. The commentary unit provides commentary and commentary in real time based on the movements of players identified by the identification unit. For example, the commentary will be in the form of, "The batter hits the ball thrown by the pitcher. It flies to center field!" The commentary section adds appropriate commentary depending on, for example, the movements of the players and the situation of the game. This allows the commentary system according to the embodiment to identify the movements of the players in real time and provide appropriate commentary and commentary.

[0060] The identification unit can identify players based on their facial recognition and uniform number. The identification unit, for example, uses facial recognition technology to recognize the player's face and identify the player based on the uniform number. For example, it detects the player's face from video captured by the drone camera unit and identifies the player using a facial recognition algorithm. The identification unit also uses uniform number recognition technology to read the player's uniform number and identify the player. For example, it uses video analysis technology to recognize the player's uniform number and identify the player. This allows for accurate identification based on the player's facial recognition and uniform number.

[0061] The learning unit can learn the individual playing styles and past performance data of players in addition to the rules of sports and past commentary data. The learning unit, for example, learns the individual playing styles and past performance data of players. For example, commentary is provided based on the players' favorite plays and past results. The learning unit also learns the movement patterns and tactical characteristics of players to provide more personalized commentary. For example, commentary is provided based on the characteristics of players. Furthermore, the learning unit learns the players' past match data and evaluates the players' performance. For example, commentary is provided based on the players' past results. In this way, more personalized commentary is possible by learning the individual playing styles and past performance data of players.

[0062] The commentary unit can provide real-time commentary and analysis based on the player's movements, as well as estimate the player's emotions and provide commentary based on those emotions. The commentary unit, for example, uses an emotion estimation function to estimate the player's emotions and provide commentary based on those emotions. For example, when a player is happy, that emotion is reflected in the commentary. The commentary unit also analyzes the player's facial expressions and movements to estimate the player's emotions. For example, the commentary unit estimates the player's emotions based on changes in the player's facial expressions and reflects that in the commentary. Furthermore, the commentary unit analyzes the player's audio data to estimate the player's emotions. For example, the commentary unit analyzes the tone and speed of the player's voice to estimate the player's emotions. This allows commentary based on the player's emotions, making it possible to provide commentary with a more realistic feel.

[0063] The drone camera unit can track players' movements in more detail by 3D modeling and analyzing their movements. For example, the drone camera unit can 3D model players' movements in real time based on drone camera footage and perform motion analysis. For example, it can analyze players' running speeds and movement patterns and reflect this in the commentary. The drone camera unit can also use 3D modeling technology to analyze players' movements in detail and extract characteristics of their movements. For example, it can analyze the height of players' steps and jumps and reflect this in the commentary. Furthermore, the drone camera unit can 3D model players' movements and perform motion analysis to track players' movements in more detail. For example, it can analyze the trajectory of players' movements and reflect this in the commentary. This allows players' movements to be tracked in more detail, enabling accurate commentary.

[0064] The drone camera unit acquires biometric information such as the player's heart rate and body temperature in real time, allowing the player's condition to be understood. For example, in addition to the drone's camera footage, the drone camera unit acquires biometric information such as the player's heart rate and body temperature in real time. For example, if a player's heart rate rises, that information is communicated in the commentary. The drone camera unit also acquires biometric information in real time to understand the player's condition. For example, if a player's body temperature rises, that information is communicated in the commentary. The drone camera unit also acquires biometric information of the player in real time to understand the player's condition. For example, changes in the player's heart rate and body temperature are analyzed and reflected in the commentary. This allows the player's condition to be understood in real time, allowing for appropriate commentary.

[0065] The drone camera unit can estimate emotions from the players' facial expressions and reflect those emotions in the commentary. For example, the drone camera unit analyzes the players' facial expressions based on the drone's camera footage and estimates their emotions. For example, when it detects a player's happy expression, it conveys that information in the commentary. The drone camera unit also uses the emotion estimation function to estimate emotions from the players' facial expressions and reflect that information in the commentary. For example, when it detects a player's nervous expression, it conveys that information in the commentary. Furthermore, the drone camera unit analyzes the players' facial expressions and estimates their emotions. For example, when it detects a player's surprised expression, it conveys that information in the commentary. In this way, by reflecting the players' emotions in the commentary, a more realistic commentary is possible.

[0066] The drone camera unit also integrates footage from fixed cameras installed around the field, allowing it to identify players from multiple perspectives. For example, the drone camera unit integrates footage from a drone camera with footage from fixed cameras installed around the field to identify players from multiple perspectives. For example, it combines footage from different angles to analyze player movements. The drone camera unit also integrates footage from fixed cameras with footage from the drone camera to track player movements in more detail. For example, it analyzes player movements from multiple perspectives and reflects this in the commentary. Furthermore, the drone camera unit integrates footage from a drone camera with footage from fixed cameras to identify players from multiple perspectives. For example, it analyzes player movements from different angles and reflects this in the commentary. This allows for more accurate commentary by identifying players from multiple perspectives.

[0067] The drone camera unit can provide tactical advice based on the movements of players. For example, the drone camera unit analyzes drone camera footage in real time and provides tactical advice based on the movements of players. For example, it analyzes the patterns of players' movements and suggests appropriate tactics. The drone camera unit also analyzes players' movements in real time and provides tactical advice. For example, it analyzes the characteristics of players' movements and suggests appropriate tactics. The drone camera unit also analyzes players' movements in real time and provides tactical advice. For example, it analyzes the trajectory of players' movements and suggests appropriate tactics. In this way, by providing tactical advice based on players' movements, it is possible to suggest appropriate tactics according to the progress of the game.

[0068] The drone camera unit can estimate emotions from the spectators' facial expressions and reflect those emotions in the commentary. For example, the drone camera unit analyzes the spectators' facial expressions based on the drone's camera footage and estimates their emotions. For example, when it detects a happy expression on the spectators' faces, it conveys that information in the commentary. The drone camera unit also uses an emotion estimation function to estimate emotions from the spectators' facial expressions and reflects that information in the commentary. For example, when it detects a surprised expression on the spectators' faces, it conveys that information in the commentary. The drone camera unit also analyzes the spectators' facial expressions and estimates their emotions. For example, when it detects a nervous expression on the spectators' faces, it conveys that information in the commentary. In this way, by reflecting the spectators' emotions in the commentary, a more realistic commentary is possible.

[0069] The learning unit learns the rules and commentary data of different sports, and can provide commentary for multiple sports. The learning unit, for example, learns the rules and commentary data of different sports. For example, it learns the rules and commentary data of not only baseball, but also soccer and basketball. Furthermore, the learning unit allows the generation AI to learn the rules and commentary data of different sports, and provide commentary for multiple sports. For example, it provides commentary for baseball, soccer, and basketball. Furthermore, the learning unit learns the rules and commentary data of different sports, and provides commentary for multiple sports. For example, it provides commentary for not only baseball, but also soccer and basketball. This makes it possible to provide commentary for multiple sports.

[0070] The learning unit can learn information about the tactics and strategies of a match and provide tactical commentary. The learning unit, for example, learns information about the tactics and strategies of a match. For example, commentary is provided based on player movements and team tactics. The learning unit also learns information about tactics and strategies, and the generation AI provides tactical commentary. For example, commentary is provided based on tactical changes and player movements during a match. The learning unit also learns information about the tactics and strategies of a match and provides tactical commentary. For example, commentary is provided based on player movements and team tactics. This makes tactical commentary possible.

[0071] The commentary unit can analyze players' movements in real time and provide commentary that predicts future plays. For example, the commentary unit uses a generating AI to analyze players' movements in real time and provide commentary that predicts future plays. For example, the commentary unit analyzes the patterns of players' movements and predicts the next play. The commentary unit also analyzes players' movements in real time and provides commentary that predicts future plays. For example, the commentary unit analyzes the characteristics of players' movements and predicts the next play. The commentary unit also analyzes players' movements in real time and provides commentary that predicts future plays. For example, the commentary unit analyzes the trajectory of players' movements and predicts the next play. This makes it possible to provide commentary that predicts future plays.

[0072] The commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. In the commentary unit, for example, a generation AI analyzes the audience's reactions in real time and provides commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's reactions and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. This makes it possible to provide commentary based on the audience's reactions.

[0073] The commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, the generative AI in the commentary section analyzes the progress of the match in real time and provides tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. This makes it possible to provide tactical commentary based on the progress of the match.

[0074] The commentary unit can estimate the emotions of the audience in real time and provide commentary based on those emotions. The commentary unit, for example, uses an emotion estimation function to estimate the emotions of the audience in real time and provide commentary based on those emotions. For example, when the audience is excited, that emotion is reflected in the commentary. The commentary unit can also estimate the emotions of the audience in real time and provide commentary based on those emotions. For example, when the audience is surprised, that emotion is reflected in the commentary. The commentary unit can also estimate the emotions of the audience in real time using the emotion estimation function and provide commentary based on those emotions. For example, when the audience is happy, that emotion is reflected in the commentary. This makes it possible to provide commentary based on the emotions of the audience.

[0075] The commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. In the commentary unit, for example, a generation AI analyzes the audience's reactions in real time and provides commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's reactions and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. This makes it possible to provide commentary based on the audience's reactions.

[0076] The commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, the generative AI in the commentary section analyzes the progress of the match in real time and provides tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. This makes it possible to provide tactical commentary based on the progress of the match.

[0077] The commentary unit can estimate the emotions of the audience in real time and provide commentary based on those emotions. The commentary unit, for example, uses an emotion estimation function to estimate the emotions of the audience in real time and provide commentary based on those emotions. For example, when the audience is excited, that emotion is reflected in the commentary. The commentary unit can also estimate the emotions of the audience in real time and provide commentary based on those emotions. For example, when the audience is surprised, that emotion is reflected in the commentary. The commentary unit can also estimate the emotions of the audience in real time using the emotion estimation function and provide commentary based on those emotions. For example, when the audience is happy, that emotion is reflected in the commentary. This makes it possible to provide commentary based on the emotions of the audience.

[0078] The commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. In the commentary unit, for example, a generation AI analyzes the audience's reactions in real time and provides commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's reactions and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. This makes it possible to provide commentary based on the audience's reactions.

[0079] The commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, the generative AI in the commentary section analyzes the progress of the match in real time and provides tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. This makes it possible to provide tactical commentary based on the progress of the match.

[0080] The commentary unit can estimate the emotions of the audience in real time and provide commentary based on those emotions. The commentary unit, for example, uses an emotion estimation function to estimate the emotions of the audience in real time and provide commentary based on those emotions. For example, when the audience is excited, that emotion is reflected in the commentary. The commentary unit can also estimate the emotions of the audience in real time and provide commentary based on those emotions. For example, when the audience is surprised, that emotion is reflected in the commentary. The commentary unit can also estimate the emotions of the audience in real time using the emotion estimation function and provide commentary based on those emotions. For example, when the audience is happy, that emotion is reflected in the commentary. This makes it possible to provide commentary based on the emotions of the audience.

[0081] The commentary department can analyze players' movements in real time and provide tactical advice. For example, the generative AI analyzes players' movements in real time and provides tactical advice. For example, it analyzes the patterns of players' movements and suggests appropriate tactics. The commentary department also analyzes players' movements in real time and provides tactical advice. For example, it analyzes the characteristics of players' movements and suggests appropriate tactics. The commentary department also analyzes players' movements in real time and provides tactical advice. For example, it analyzes the trajectory of players' movements and suggests appropriate tactics. In this way, by providing tactical advice based on players' movements, it is possible to suggest appropriate tactics according to the progress of the game.

[0082] The commentary unit can estimate the emotions of the players in real time and provide commentary based on those emotions. The commentary unit, for example, uses an emotion estimation function to estimate the emotions of the players in real time and provides commentary based on those emotions. For example, when a player is happy, that emotion is reflected in the commentary. The commentary unit can also estimate the emotions of the players in real time and provide commentary based on those emotions. For example, when a player is nervous, that emotion is reflected in the commentary. The commentary unit can also estimate the emotions of the players in real time using the emotion estimation function and provide commentary based on those emotions. For example, when a player is surprised, that emotion is reflected in the commentary. This makes it possible to provide commentary based on the emotions of the players.

[0083] The commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. In the commentary unit, for example, a generation AI analyzes the audience's reactions in real time and provides commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's reactions and reflects those reactions in the commentary. In addition, the commentary unit can analyze the audience's reactions in real time and provide commentary based on the audience's reactions. For example, the commentary unit analyzes the audience's cheers and applause and reflects those reactions in the commentary. This makes it possible to provide commentary based on the audience's reactions.

[0084] The commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, the generative AI in the commentary section analyzes the progress of the match in real time and provides tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. Furthermore, the commentary section can analyze the progress of the match in real time and provide tactical commentary. For example, it analyzes the progress of the match and explains appropriate tactics. This makes it possible to provide tactical commentary based on the progress of the match.

[0085] The commentary unit can estimate the emotions of the audience in real time and provide commentary based on those emotions. The commentary unit, for example, uses an emotion estimation function to estimate the emotions of the audience in real time and provide commentary based on those emotions. For example, when the audience is excited, that emotion is reflected in the commentary. The commentary unit can also estimate the emotions of the audience in real time and provide commentary based on those emotions. For example, when the audience is surprised, that emotion is reflected in the commentary. The commentary unit can also estimate the emotions of the audience in real time using the emotion estimation function and provide commentary based on those emotions. For example, when the audience is happy, that emotion is reflected in the commentary. This makes it possible to provide commentary based on the emotions of the audience.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The commentary system can further include a prediction unit that predicts the player's movements. The prediction unit predicts the player's next movement based on, for example, past movement data of the player. For example, it predicts which direction the batter will hit next and reflects this in the commentary. The prediction unit also analyzes the player's movement patterns and predicts the player's next movement. For example, it predicts which type of pitch the pitcher will throw next and reflects this in the commentary. Furthermore, the prediction unit analyzes the player's movements in real time and predicts the player's next movement. For example, it predicts which base a runner will advance to next and reflects this in the commentary. In this way, predicting the player's movements enables more realistic commentary.

[0088] The commentary system can further include a reaction analysis unit that analyzes audience reactions. The reaction analysis unit, for example, analyzes audience cheers and applause and reflects the reactions in the commentary. For example, when the audience cheers, the information is reflected in the commentary. The reaction analysis unit also analyzes audience reactions in real time and reflects the information in the commentary. For example, when the audience claps, the information is reflected in the commentary. The reaction analysis unit also analyzes audience reactions and reflects the information in the commentary. For example, when the audience boos, the information is reflected in the commentary. In this way, by reflecting audience reactions in the commentary, a more realistic commentary is possible.

[0089] The commentary system may further include a health monitoring unit that monitors the health status of the players. The health monitoring unit, for example, monitors the player's heart rate and body temperature in real time and reflects the information in the commentary. For example, when a player's heart rate rises, the information is reflected in the commentary. The health monitoring unit also monitors the player's health status in real time and reflects the information in the commentary. For example, when a player's body temperature rises, the information is reflected in the commentary. The health monitoring unit also monitors the player's health status and reflects the information in the commentary. For example, when a player is fatigued, the information is reflected in the commentary. In this way, by monitoring the player's health status, more accurate commentary is possible.

[0090] The commentary system may further include an emotion analysis unit that estimates the emotions of the players and provides commentary based on those emotions. The emotion analysis unit, for example, analyzes the facial expressions and movements of the players to estimate their emotions. For example, if a player is happy, that emotion is reflected in the commentary. The emotion analysis unit also analyzes the players' audio data to estimate their emotions. For example, it analyzes the tone and speed of the player's voice to estimate their emotions. Furthermore, the emotion analysis unit estimates the players' emotions in real time and provides commentary based on those emotions. For example, if a player is nervous, that emotion is reflected in the commentary. This allows commentary to be based on the players' emotions, making it possible to provide a more realistic commentary.

[0091] The commentary system may further include an emotion estimation unit that estimates the emotions of the audience and provides commentary based on those emotions. The emotion estimation unit may, for example, analyze the facial expressions and movements of the audience to estimate their emotions. For example, if the audience is happy, that emotion may be reflected in the commentary. The emotion estimation unit may also analyze audio data of the audience to estimate their emotions. For example, it may analyze the tone and speed of the audience's voices to estimate their emotions. The emotion estimation unit may also estimate the emotions of the audience in real time and provide commentary based on those emotions. For example, if the audience is surprised, that emotion may be reflected in the commentary. This allows for commentary based on the audience's emotions, making the commentary more realistic.

[0092] The commentary system may further include a 3D analysis unit that 3D models the player's movements and performs motion analysis. The 3D analysis unit, for example, 3D models the player's movements in real time and performs motion analysis. For example, it analyzes the player's running speed and movement patterns and reflects this in the commentary. The 3D analysis unit also analyzes the player's movements in detail and extracts movement characteristics. For example, it analyzes the player's step and jump height and reflects this in the commentary. Furthermore, the 3D analysis unit 3D models the player's movements and performs motion analysis to track the player's movements in more detail. For example, it analyzes the trajectory of the player's movement and reflects this in the commentary. This allows for more detailed tracking of the player's movements and enables accurate commentary.

[0093] The commentary system may further include a biometric information acquisition unit that acquires biometric information such as a player's heart rate and body temperature in real time and grasps the player's condition. The biometric information acquisition unit, for example, acquires a player's heart rate and body temperature in real time and reflects that information in the commentary. For example, when a player's heart rate rises, that information is reflected in the commentary. The biometric information acquisition unit also grasps a player's condition in real time and reflects that information in the commentary. For example, when a player's body temperature rises, that information is reflected in the commentary. The biometric information acquisition unit also acquires a player's biometric information in real time and grasps the player's condition. For example, it analyzes changes in a player's heart rate and body temperature and reflects that information in the commentary. This makes it possible to grasp a player's condition in real time and provide appropriate commentary.

[0094] The commentary system may further include a viewpoint integration unit that integrates video from fixed cameras installed around the field and identifies players from multiple viewpoints. The viewpoint integration unit, for example, integrates video from a drone camera with video from a fixed camera to identify players from multiple viewpoints. For example, it combines video from different angles to analyze player movements. The viewpoint integration unit also integrates video from a fixed camera with video from a drone camera to track player movements in more detail. For example, it analyzes player movements from multiple viewpoints and reflects the analysis in the commentary. The viewpoint integration unit also integrates video from a drone camera with video from a fixed camera to identify players from multiple viewpoints. For example, it analyzes player movements from different angles and reflects the analysis in the commentary. This allows for more accurate commentary by identifying players from multiple viewpoints.

[0095] The commentary system may further include a tactical advice unit that provides tactical advice based on player movements. The tactical advice unit, for example, analyzes player movements in real time and provides tactical advice. For example, it analyzes the patterns of player movements and suggests appropriate tactics. The tactical advice unit also analyzes player movements in real time and provides tactical advice. For example, it analyzes the characteristics of player movements and suggests appropriate tactics. The tactical advice unit also analyzes player movements in real time and provides tactical advice. For example, it analyzes the trajectory of player movements and suggests appropriate tactics. In this way, by providing tactical advice based on player movements, it is possible to suggest appropriate tactics according to the progress of the game.

[0096] The commentary system may further include an emotion estimation unit that estimates emotions from the facial expressions of spectators and reflects the emotions in the commentary. The emotion estimation unit, for example, analyzes the facial expressions of spectators to estimate emotions. For example, when a happy expression of a spectator is detected, the information is reflected in the commentary. The emotion estimation unit also estimates emotions from the facial expressions of spectators and reflects the information in the commentary. For example, when a surprised expression of a spectator is detected, the information is reflected in the commentary. The emotion estimation unit also analyzes the facial expressions of spectators to estimate emotions. For example, when a nervous expression of a spectator is detected, the information is reflected in the commentary. In this way, by reflecting the emotions of the spectators in the commentary, a more realistic commentary is possible.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The drone camera unit takes pictures of the players. For example, it uses a camera mounted on a drone to take pictures of the players on the field. The drone camera unit takes pictures of the entire field from above and tracks the players' movements in real time. Step 2: The identification unit identifies players from the footage captured by the drone camera unit. For example, players are identified based on pre-entered facial recognition data and uniform numbers. The identification unit recognizes the players' faces using facial recognition technology and identifies them based on their uniform numbers. Step 3: The learning unit learns the rules of the sport and past play-by-play data. For example, it learns the rules of baseball and play-by-play data from past games, and provides appropriate play-by-play and commentary as the game progresses. The learning unit allows the generation AI to learn the rules of the sport and past play-by-play data in advance, and then provides real-time play-by-play and commentary. Step 4: The commentary team provides real-time commentary and commentary based on the player movements identified by the classification team. For example, the commentary may say, "The batter hits the ball thrown by the pitcher. It flies to center field!" The commentary team provides appropriate commentary based on the player movements and the situation of the game.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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. The drone camera department will film the athletes using drone cameras, an identification unit that identifies players from the images captured by the drone camera unit; A learning department that learns sports rules and past live data, a commentary unit that provides commentary and explanation in real time based on the movements of the players identified by the identification unit. A system characterized by:

2. The identification unit Identifying the player based on the player's face recognition and uniform number 2. The system of claim 1.

3. The learning unit In addition to learning the rules of the sport and the past play-by-play data, the system learns the individual playing styles and past performance data of the players.

2. The system of claim 1.

4. The commentary section In addition to providing real-time commentary and commentary based on the player's movements, the system estimates the player's emotions and provides commentary based on those emotions.

2. The system of claim 1.

5. The drone camera unit includes: Tracking the player's movements in more detail by 3D modeling and motion analysis of the player's movements 2. The system of claim 1.

Images