System

Abstract

An object of a system according to an embodiment is to provide a high-quality sports live broadcast in real time on the basis of video analysis.SOLUTION: A system according to an embodiment includes a video analysis unit, a live commentary generation unit, a rule explanation unit, and a viewer customization unit. The video analysis unit analyzes the video. The play-by-play generation unit generates play-by-play contents on the basis of the progress of the game or the movements of the players analyzed by the video analysis unit. The rule explanation unit gives a detailed rule explanation on the basis of the live commentary content generated by the live commentary generation unit. The viewer customizing section customizes the live commentary in accordance with the viewer's preference.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, the quality of sports commentary depends on the announcer's knowledge and expression, which can lead to variations in the viewing experience.

[0005] The system according to the embodiment aims to provide high-quality live sports commentary in real time based on video analysis. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a commentary generation unit, a rule explanation unit, and a viewer customization unit. The video analysis unit analyzes video. The commentary generation unit generates commentary content based on the progress of the match or the movements of the players analyzed by the video analysis unit. The rule explanation unit provides detailed rule explanations based on the commentary content generated by the commentary generation unit. The viewer customization unit customizes the commentary content to suit the viewer's preferences. [Effects of the Invention]

[0007] The system according to the embodiment can provide high-quality sports commentary in real time based on video analysis. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A sports commentary system according to an embodiment of the present invention is a system that uses generative AI to provide real-time commentary based on video analysis. This system analyzes sports video, understands the progress of the game and the movements of the players, and provides detailed explanations of the rules and commentary tailored to the viewer's preferences. This allows the sports commentary system to provide detailed explanations of the rules and commentary tailored to the viewer's preferences for any sport, thereby improving the viewing experience.

[0029] A sports commentary system according to an embodiment includes a video analysis unit, a commentary generation unit, a rule explanation unit, and a viewer customization unit. The video analysis unit analyzes video of a sport. For example, the video analysis unit analyzes player movements using image recognition technology. The video analysis unit can also grasp the progress of a game using a motion analysis algorithm. For example, in a soccer game, the video analysis unit analyzes the position of the ball and the movements of players to detect important events such as goals and fouls. The commentary generation unit generates commentary content based on the progress of the game and the movements of players analyzed by the video analysis unit. For example, the commentary generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to provide commentary according to the progress of the game. The commentary generation unit can also use template-based generation technology to provide commentary according to the progress of the game. The rule explanation unit provides detailed rule explanations based on the commentary content generated by the commentary generation unit. For example, the rule explanation unit has knowledge of the rules of the sport and provides appropriate rule explanations for various situations that occur during the game. Furthermore, for example, if a foul occurs in a basketball game, the rule explanation unit explains the type of foul and how to handle it based on the rules. The viewer customization unit customizes the commentary content to suit the viewer's preferences. For example, the viewer customization unit can customize the commentary content according to the viewer's preferences and interests. The viewer customization unit also provides content in response to viewer requests, such as commentary that focuses on a specific player or commentary that emphasizes tactical commentary. This allows the sports commentary system to improve the quality of the sports commentary and enhance the viewer's experience.

[0030] The video analysis unit can refer to past match data and highlight differences compared to the current match situation. For example, the generation AI analyzes past match data and highlights different tactics and playing styles compared to the current match situation. For example, it explains the differences between tactics that were successful in past matches and those in the current match. The generation AI can also refer to past player performance data and highlight differences compared to performance in the current match. For example, it can explain changes in a player's shooting success rate or pass success rate. The generation AI can also refer to important events (goals, fouls, etc.) in past matches and highlight differences compared to similar events in the current match. For example, it can explain the differences between past goal scenes and current goal scenes. This allows viewers to compare past match data with the current match situation and provide them with a new perspective.

[0031] The video analysis unit can simultaneously analyze footage from different camera angles and select the optimal angle for commentary. For example, the generation AI in the video analysis unit simultaneously analyzes footage from multiple camera angles and selects the angle that captures the most important scenes. For example, it captures a goal scene from the optimal angle. The video analysis unit also analyzes footage from different camera angles and selects the angle that is easiest for viewers to view. For example, it selects an angle that gives the best view of the players' movements. The video analysis unit also analyzes footage from multiple camera angles and captures important events (goals, fouls, etc.) from the optimal angle. For example, it captures a foul scene from the angle that makes it easiest to understand. This allows the generation AI to select the optimal camera angle for commentary, thereby providing viewers with footage that is easy to view.

[0032] The video analysis unit analyzes audio data during a match and can translate instructions from players or coaches in real time to incorporate them into the commentary. For example, the video analysis unit uses a generation AI to analyze audio data from players and coaches during a match and translate it in real time to incorporate it into the commentary. For example, it translates the conversations when players ask their coaches for instructions. The video analysis unit also uses a generation AI to analyze audio data during a match and translate the tactical instructions from players and coaches in real time to incorporate it into the commentary. For example, it translates the conversations when a coach gives tactical instructions to players. The video analysis unit also uses a generation AI to analyze audio data during a match and translate the emotional reactions of players and coaches in real time to incorporate it into the commentary. For example, it translates the voices of joy when a player scores a goal. In this way, by translating the instructions of players and coaches in real time and incorporating them into the commentary, it is possible to provide a commentary that is easy for viewers to understand.

[0033] The rule commentary unit can detect rule violations during a match in real time and provide commentary on the spot. For example, the generation AI in the rule commentary unit detects rule violations during a match in real time and provides commentary on the spot. For example, it detects offsides in soccer and explains the rule. The rule commentary unit also detects rule violations during a match and explains the type of violation and the rule-based procedure in real time. For example, it detects fouls in basketball and explains the procedure. The rule commentary unit also detects rule violations during a match and explains the impact of the violation on the match in real time. For example, it detects knock-ons in rugby and explains their impact. This allows viewers to deepen their understanding by providing commentary on rule violations during a match in real time.

[0034] The rule explanation unit can refer to past data and introduce cases where similar rule violations have occurred. For example, the generation AI may refer to past match data and introduce cases where similar rule violations have occurred. For example, it may introduce cases of offsides that occurred in past matches. The rule explanation unit may also analyze past match data and introduce measures taken when similar rule violations have occurred. For example, it may introduce measures taken for fouls that occurred in past matches. The rule explanation unit may also refer to past match data and introduce the impact that similar rule violations have had on matches. For example, it may introduce the impact that knock-ons that occurred in past matches had on matches. In this way, by introducing past cases, viewers' understanding can be deepened.

[0035] The rule explanation section can compare the rules of different sports and provide easy-to-understand explanations for viewers. For example, the generation AI can compare the rules of soccer and rugby and provide easy-to-understand explanations for viewers. For example, it can explain the differences in offside rules. The rule explanation section can also compare the rules of basketball and handball and provide easy-to-understand explanations for viewers. For example, it can explain the differences in foul rules. The rule explanation section can also compare the rules of tennis and badminton and provide easy-to-understand explanations for viewers. For example, it can explain the differences in the scoring systems. In this way, by comparing and explaining the rules of different sports, viewers can deepen their understanding.

[0036] The rule explanation unit can visually display rule explanations during a match, allowing viewers to intuitively understand. For example, the rule explanation unit may use a generation AI to visually display rule explanations during a match, allowing viewers to intuitively understand. For example, the offside line may be displayed graphically. The rule explanation unit may also use a generation AI to visually display rule explanations during a match, allowing viewers to intuitively understand the rules. For example, the location of fouls may be displayed on a map. The rule explanation unit may also use a generation AI to visually display rule explanations during a match, allowing viewers to intuitively understand how the rules apply. For example, the penalty area may be highlighted. This visual display of rule explanations can deepen viewers' understanding.

[0037] The viewer customization unit can analyze the viewer's viewing history and provide commentary content tailored to their preferences. For example, the generation AI analyzes the viewer's past viewing history and provides commentary content tailored to their preferences. For example, a commentary focusing on a specific player is provided. The viewer customization unit also provides commentary focusing on tactics and playing styles that interest the viewer based on the viewer's viewing history. For example, a commentary emphasizing an offensive playing style is provided. The viewer customization unit also analyzes the viewer's past viewing history and provides commentary tailored to their preferences. For example, a commentary on the tactics of the viewer's favorite team is provided. This allows for the provision of commentary tailored to the viewer's preferences based on the viewer's past viewing history, thereby improving viewer satisfaction.

[0038] The viewer customization unit can provide information on players or teams according to the viewer's preferences. In the viewer customization unit, for example, the generation AI provides information on specific players or teams according to the viewer's preferences. For example, the viewer may introduce past performances and episodes of a player that the viewer likes. In addition, the viewer customization unit provides the latest information on players and teams based on the viewer's preferences. For example, the viewer may introduce the latest news and interviews on a team that the viewer supports. In addition, the viewer customization unit provides information on the tactics and playing styles of players and teams according to the viewer's preferences. For example, the generation AI may provide tactical commentary that interests the viewer. In this way, viewer satisfaction can be improved by providing information on players and teams according to the viewer's preferences.

[0039] The viewer customization unit can provide tactical commentary according to the viewer's preferences. In the viewer customization unit, for example, the generation AI provides commentary on specific tactics according to the viewer's preferences. For example, it provides detailed commentary on offensive and defensive tactics that interest the viewer. In addition, the viewer customization unit provides commentary on tactical moves during a match based on the viewer's preferences. For example, it provides detailed commentary on the tactical plays of the viewer's favorite team. In addition, the viewer customization unit provides commentary on tactics used in past matches according to the viewer's preferences and explains their effectiveness. For example, it introduces successful examples of tactics that the viewer is interested in. In this way, viewer satisfaction can be improved by providing tactical commentary according to the viewer's preferences.

[0040] The system can learn slang or technical terms in different languages ​​and provide natural commentary. For example, the generation AI of the system can learn slang and technical terms in different languages ​​and provide natural commentary. For example, it can learn English soccer terms and Spanish basketball terms. The generation AI can also learn slang and technical terms in different languages ​​and provide commentary that is natural for viewers. For example, it can learn Japanese baseball terms and French tennis terms. The generation AI can also learn slang and technical terms in different languages ​​and provide commentary that is easy for viewers to understand. For example, it can learn Chinese badminton terms and German handball terms. In this way, by learning slang and technical terms in different languages, it is possible to provide natural commentary.

[0041] The system can generate subtitles in different languages ​​in real time and provide them to viewers. For example, the system uses a generation AI to generate subtitles in different languages ​​in real time and provide them to viewers. For example, English commentary is translated into Japanese subtitles and provided. The system also uses a generation AI to generate subtitles in different languages ​​in real time and provide subtitles that are easy for viewers to understand. For example, Spanish commentary is translated into French subtitles and provided. The system also uses a generation AI to generate subtitles in different languages ​​in real time and provide subtitles that are natural for viewers. For example, Chinese commentary is translated into German subtitles and provided. This allows subtitles in different languages ​​to be generated in real time, deepening viewers' understanding.

[0042] The system can synthesize speech in different languages ​​and provide it to viewers. For example, the generation AI in the system synthesizes speech in different languages ​​and provides it to viewers. For example, English commentary is translated into Japanese speech and provided. The system also synthesizes speech in different languages ​​and provides speech that is easy for viewers to understand. For example, Spanish commentary is translated into French speech and provided. The system also synthesizes speech in different languages ​​and provides speech that is natural for viewers. For example, Chinese commentary is translated into German speech and provided. This allows viewers to deepen their understanding by synthesizing speech in different languages.

[0043] The system can analyze data during a match in real time and provide it to viewers. For example, the system uses a generating AI to analyze data during a match in real time and provide it to viewers. For example, it displays a player's shooting success rate and pass success rate in real time. The system also uses a generating AI to analyze data during a match and provide statistical information that is important to viewers. For example, it displays a player's running distance and ball possession rate in real time. The system also uses a generating AI to analyze data during a match in real time and provide it in a format that is easy for viewers to understand. For example, it displays statistical information using graphs and charts. This allows viewers to deepen their understanding by analyzing data during a match in real time.

[0044] The system can refer to past match data and compare it with the current match situation to provide statistical information. For example, the system's generation AI refers to past match data and compares it with the current match situation to provide statistical information. For example, it compares the shooting success rate in past matches with the shooting success rate in the current match. The system also has the generation AI analyze past match data and evaluate player performance by comparing it with the current match situation. For example, it compares the pass success rate in past matches with the pass success rate in the current match. The system also has the generation AI refer to past match data and evaluate the team's tactical moves by comparing it with the current match situation. For example, it compares the ball possession rate in past matches with the ball possession rate in the current match. This allows viewers to deepen their understanding by comparing past match data with the current match situation and providing statistical information.

[0045] The system can compare statistical information from different sports and provide it to viewers. For example, the system's generating AI can compare statistical information from soccer and basketball and provide it to viewers. For example, it can explain the differences in shooting success rate and passing success rate. The system can also compare statistical information from tennis and badminton and provide it to viewers. For example, it can explain the differences in the scoring systems and the pace of the game. The system can also compare statistical information from rugby and American football and provide it to viewers. For example, it can explain the differences in tackle success rate and ball possession time. This allows viewers to deepen their understanding by comparing statistical information from different sports.

[0046] The system can visually display data during a game, allowing viewers to intuitively understand it. For example, the system may have a generating AI visually display data during a game, allowing viewers to intuitively understand it. For example, the system may display shooting success rate in a graph. The system may also have a generating AI visually display data during a game, allowing viewers to intuitively understand the progress of the game. For example, the system may display ball possession rate in a chart. The system may also have a generating AI visually display data during a game, allowing viewers to intuitively understand player performance. For example, the system may display running distance on a map. This allows viewers to deepen their understanding by visually displaying data during a game.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The sports commentary system may further include a health management unit that monitors the health status of players. The health management unit may, for example, monitor a player's heart rate and oxygen saturation in real time and issue a warning if an abnormality is detected. The health management unit may also refer to a player's past health data and compare it with their current health status to detect abnormalities. For example, it may compare heart rate fluctuations in past games with heart rate fluctuations in the current game. The health management unit may also predict performance during a game based on the player's health status. For example, a high heart rate may indicate the possibility of accumulated fatigue. This allows for real-time monitoring of a player's health status and rapid response if an abnormality is detected.

[0049] The sports commentary system may further include a tactical analysis unit that analyzes players' tactical movements. The tactical analysis unit, for example, analyzes player position data to understand the team's tactical movements. For example, it analyzes changes in formation and player positioning. The tactical analysis unit may also refer to past game data and compare it with the current game situation to highlight tactical differences. For example, it may explain the differences between tactics that were successful in past games and tactics in the current game. The tactical analysis unit may also analyze players' movement patterns and infer tactical intentions. For example, it may predict what movements a specific player will make. This allows the tactical movements of players to be analyzed and provides viewers with a tactical perspective.

[0050] Sports commentary systems can also analyze weather data during a game and reflect it in their commentary. For example, they can obtain weather data in real time and explain how it will affect the progress of the game. For example, if it is raining, they can explain how the ball will slide and the condition of the players' feet. They can also analyze wind speed and direction and explain how it will affect the tactics of the game. For example, they can explain that if the wind is strong, it may affect the accuracy of long passes and shots. They can also analyze temperature and humidity and explain how it will affect the physical strength of the players. In this way, they can analyze weather data during a game and help viewers understand how it will affect the progress of the game.

[0051] Sports commentary systems can also analyze audio data during a game, translating instructions from players and coaches in real time and incorporating them into the commentary. For example, the system can translate the conversations when players ask their coaches for instructions. It can also analyze audio data during a game, translating tactical instructions from players and coaches in real time and incorporating them into the commentary. For example, it can translate the conversations when a coach gives tactical instructions to players. It can also analyze audio data during a game, translating the emotional reactions of players and coaches in real time and incorporating them into the commentary. For example, it can translate the voices of joy when a player scores a goal. In this way, by translating instructions from players and coaches in real time and incorporating them into the commentary, it is possible to provide commentary that is easy for viewers to understand.

[0052] Sports commentary systems can further visually display data during a game to allow viewers to intuitively understand it. For example, shooting success rate can be displayed in a graph. Data during a game can also be visually displayed to allow viewers to intuitively understand the progress of the game. For example, ball possession rate can be displayed in a chart. Data during a game can also be visually displayed to allow viewers to intuitively understand player performance. For example, running distance can be displayed on a map. In this way, by visually displaying data during a game, viewers can deepen their understanding.

[0053] Sports commentary systems can also compare statistical information between different sports and provide it to viewers. For example, statistical information between soccer and basketball can be compared and provided to viewers, explaining the differences in shooting success rate and passing success rate, for example. Statistical information between tennis and badminton can also be compared and provided to viewers, explaining the differences in scoring systems and the pace of the game, for example. Statistical information between rugby and American football can also be compared and provided to viewers, explaining the differences in tackle success rate and ball possession time, for example. This allows viewers to deepen their understanding by comparing statistical information between different sports.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The video analysis unit analyzes sports footage. For example, the video analysis unit uses image recognition technology to analyze player movements and motion analysis algorithms to understand the progress of the game. In soccer games, the unit analyzes the position of the ball and the movements of players to detect important events such as goals and fouls. Step 2: The commentary generation unit generates commentary content based on the progress of the game and the movements of the players analyzed by the video analysis unit. For example, it uses generative AI (text generation AI or multimodal generation AI) or template-based generation technology to provide commentary according to the progress of the game. Step 3: The rule explanation unit provides detailed rule explanations based on the commentary content generated by the commentary generation unit. For example, it has knowledge of sports rules and provides appropriate rule explanations for various situations that occur during a game. If a foul occurs in a basketball game, it will explain the type of foul and how it should be handled based on the rules. Step 4: The viewer customization unit customizes the commentary content to suit the viewer's preferences. For example, it provides content that meets the viewer's requests, such as commentary that focuses on a specific player or tactical commentary, based on the viewer's preferences and interests.

[0056] (Example 2) A sports commentary system according to an embodiment of the present invention is a system that uses generative AI to provide real-time commentary based on video analysis. This system analyzes sports video, understands the progress of the game and the movements of the players, and provides detailed explanations of the rules and commentary tailored to the viewer's preferences. This allows the sports commentary system to provide detailed explanations of the rules and commentary tailored to the viewer's preferences for any sport, thereby improving the viewing experience.

[0057] A sports commentary system according to an embodiment includes a video analysis unit, a commentary generation unit, a rule explanation unit, and a viewer customization unit. The video analysis unit analyzes video of a sport. For example, the video analysis unit analyzes player movements using image recognition technology. The video analysis unit can also grasp the progress of a game using a motion analysis algorithm. For example, in a soccer game, the video analysis unit analyzes the position of the ball and the movements of players to detect important events such as goals and fouls. The commentary generation unit generates commentary content based on the progress of the game and the movements of players analyzed by the video analysis unit. For example, the commentary generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to provide commentary according to the progress of the game. The commentary generation unit can also use template-based generation technology to provide commentary according to the progress of the game. The rule explanation unit provides detailed rule explanations based on the commentary content generated by the commentary generation unit. For example, the rule explanation unit has knowledge of the rules of the sport and provides appropriate rule explanations for various situations that occur during the game. Furthermore, for example, if a foul occurs in a basketball game, the rule explanation unit explains the type of foul and how to handle it based on the rules. The viewer customization unit customizes the commentary content to suit the viewer's preferences. For example, the viewer customization unit can customize the commentary content according to the viewer's preferences and interests. The viewer customization unit also provides content in response to viewer requests, such as commentary that focuses on a specific player or commentary that emphasizes tactical commentary. This allows the sports commentary system to improve the quality of the sports commentary and enhance the viewer's experience.

[0058] The video analysis unit can estimate emotions from players' facial expressions or body movements and provide commentary based on those emotions. For example, the video analysis unit uses a generation AI to analyze players' facial expressions and estimate emotions such as joy or tension. For example, it can detect the expression of joy on the face of a player who has scored a goal and provide commentary based on that emotion. The video analysis unit also uses a generation AI to analyze players' body movements and estimate emotions such as fatigue or loss of concentration. For example, it can detect sluggishness in a player's movements towards the end of a game and provide commentary based on that emotion. The video analysis unit also uses a generation AI to analyze players' tone of voice and audio data to estimate emotions. For example, it can detect tension from the tone of a player's voice when asking for instructions from their coach and provide commentary based on that emotion. This enables real-time commentary based on players' emotions.

[0059] The video analysis unit can refer to past match data and highlight differences compared to the current match situation. For example, the generation AI analyzes past match data and highlights different tactics and playing styles compared to the current match situation. For example, it explains the differences between tactics that were successful in past matches and those in the current match. The generation AI can also refer to past player performance data and highlight differences compared to performance in the current match. For example, it can explain changes in a player's shooting success rate or pass success rate. The generation AI can also refer to important events (goals, fouls, etc.) in past matches and highlight differences compared to similar events in the current match. For example, it can explain the differences between past goal scenes and current goal scenes. This allows viewers to compare past match data with the current match situation and provide them with a new perspective.

[0060] The video analysis unit analyzes the reactions of spectators during a match and can provide commentary based on the spectators' level of excitement. In the video analysis unit, for example, the generation AI analyzes the spectators' facial expressions and estimates the level of excitement. For example, it detects the moment when spectators cheer and provides commentary based on that level of excitement. In addition, the video analysis unit uses the generation AI to analyze the spectators' movements and estimate the level of excitement. For example, it detects the moment when spectators stand up to cheer and provides commentary based on that level of excitement. In addition, the video analysis unit uses the generation AI to analyze the spectators' audio data and estimate the level of excitement. For example, it analyzes the volume of the spectators' cheers and boos and provides commentary based on that level of excitement. This makes it possible to provide commentary based on the spectators' level of excitement, improving the viewer's sense of realism.

[0061] The video analysis unit can simultaneously analyze footage from different camera angles and select the optimal angle for commentary. For example, the generation AI in the video analysis unit simultaneously analyzes footage from multiple camera angles and selects the angle that captures the most important scenes. For example, it captures a goal scene from the optimal angle. The video analysis unit also analyzes footage from different camera angles and selects the angle that is easiest for viewers to view. For example, it selects an angle that gives the best view of the players' movements. The video analysis unit also analyzes footage from multiple camera angles and captures important events (goals, fouls, etc.) from the optimal angle. For example, it captures a foul scene from the angle that makes it easiest to understand. This allows the generation AI to select the optimal camera angle for commentary, thereby providing viewers with footage that is easy to view.

[0062] The video analysis unit analyzes audio data during a match and can translate instructions from players or coaches in real time to incorporate them into the commentary. For example, the video analysis unit uses a generation AI to analyze audio data from players and coaches during a match and translate it in real time to incorporate it into the commentary. For example, it translates the conversations when players ask their coaches for instructions. The video analysis unit also uses a generation AI to analyze audio data during a match and translate the tactical instructions from players and coaches in real time to incorporate it into the commentary. For example, it translates the conversations when a coach gives tactical instructions to players. The video analysis unit also uses a generation AI to analyze audio data during a match and translate the emotional reactions of players and coaches in real time to incorporate it into the commentary. For example, it translates the voices of joy when a player scores a goal. In this way, by translating the instructions of players and coaches in real time and incorporating them into the commentary, it is possible to provide a commentary that is easy for viewers to understand.

[0063] The video analysis unit can use the emotion estimation function to select a commentary style that corresponds to the viewer's emotions. For example, the generation AI in the video analysis unit analyzes the viewer's facial expressions, estimates their emotions, and selects a commentary style. For example, if the viewer is excited, the commentary will be more energetic. The video analysis unit also analyzes the viewer's audio data, estimates their emotions, and selects a commentary style. For example, if the viewer is relaxed, the commentary will be more calm. The video analysis unit also analyzes the viewer's real-time reactions, and selects a commentary style that corresponds to their emotions. For example, if the viewer is surprised, the commentary will emphasize the surprise. This allows the commentary style to be selected according to the viewer's emotions, thereby improving viewer satisfaction.

[0064] The rule commentary unit can detect rule violations during a match in real time and provide commentary on the spot. For example, the generation AI in the rule commentary unit detects rule violations during a match in real time and provides commentary on the spot. For example, it detects offsides in soccer and explains the rule. The rule commentary unit also detects rule violations during a match and explains the type of violation and the rule-based procedure in real time. For example, it detects fouls in basketball and explains the procedure. The rule commentary unit also detects rule violations during a match and explains the impact of the violation on the match in real time. For example, it detects knock-ons in rugby and explains their impact. This allows viewers to deepen their understanding by providing commentary on rule violations during a match in real time.

[0065] The rule explanation unit can refer to past data and introduce cases where similar rule violations have occurred. For example, the generation AI may refer to past match data and introduce cases where similar rule violations have occurred. For example, it may introduce cases of offsides that occurred in past matches. The rule explanation unit may also analyze past match data and introduce measures taken when similar rule violations have occurred. For example, it may introduce measures taken for fouls that occurred in past matches. The rule explanation unit may also refer to past match data and introduce the impact that similar rule violations have had on matches. For example, it may introduce the impact that knock-ons that occurred in past matches had on matches. In this way, by introducing past cases, viewers' understanding can be deepened.

[0066] The rule explanation section can compare the rules of different sports and provide easy-to-understand explanations for viewers. For example, the generation AI can compare the rules of soccer and rugby and provide easy-to-understand explanations for viewers. For example, it can explain the differences in offside rules. The rule explanation section can also compare the rules of basketball and handball and provide easy-to-understand explanations for viewers. For example, it can explain the differences in foul rules. The rule explanation section can also compare the rules of tennis and badminton and provide easy-to-understand explanations for viewers. For example, it can explain the differences in the scoring systems. In this way, by comparing and explaining the rules of different sports, viewers can deepen their understanding.

[0067] The rule explanation unit can visually display rule explanations during a match, allowing viewers to intuitively understand. For example, the rule explanation unit may use a generation AI to visually display rule explanations during a match, allowing viewers to intuitively understand. For example, the offside line may be displayed graphically. The rule explanation unit may also use a generation AI to visually display rule explanations during a match, allowing viewers to intuitively understand the rules. For example, the location of fouls may be displayed on a map. The rule explanation unit may also use a generation AI to visually display rule explanations during a match, allowing viewers to intuitively understand how the rules apply. For example, the penalty area may be highlighted. This visual display of rule explanations can deepen viewers' understanding.

[0068] The rule explanation unit can use the emotion estimation function to select a style of rule explanation that is easy for viewers to understand. For example, the generation AI in the rule explanation unit estimates the viewer's emotions and selects a style of rule explanation that is easy to understand. For example, if the viewer is relaxed, the explanation will be slow. The rule explanation unit also analyzes the viewer's real-time reactions and selects a style of rule explanation that is easy to understand. For example, if the viewer is excited, the explanation will be concise. The rule explanation unit also selects a style of rule explanation that is easy to understand based on the viewer's emotional data. For example, if the viewer is confused, the explanation will be detailed. In this way, by selecting a style of rule explanation that corresponds to the viewer's emotions, the viewer's understanding can be deepened.

[0069] The viewer customization unit can analyze the viewer's viewing history and provide commentary content tailored to their preferences. For example, the generation AI analyzes the viewer's past viewing history and provides commentary content tailored to their preferences. For example, a commentary focusing on a specific player is provided. The viewer customization unit also provides commentary focusing on tactics and playing styles that interest the viewer based on the viewer's viewing history. For example, a commentary emphasizing an offensive playing style is provided. The viewer customization unit also analyzes the viewer's past viewing history and provides commentary tailored to their preferences. For example, a commentary on the tactics of the viewer's favorite team is provided. This allows for the provision of commentary tailored to the viewer's preferences based on the viewer's past viewing history, thereby improving viewer satisfaction.

[0070] The viewer customization unit can analyze the viewer's real-time reactions and dynamically change the commentary content. In the viewer customization unit, for example, the generation AI analyzes the viewer's real-time reactions and dynamically changes the commentary content. For example, if the viewer is excited, a more energetic commentary is provided. In addition, the viewer customization unit analyzes the viewer's real-time reactions and changes the commentary content to match the viewer's interests. For example, if the viewer is paying attention to a particular player, more information about that player is provided. In addition, the viewer customization unit analyzes the viewer's real-time reactions and adjusts the commentary content according to the viewer's emotions. For example, if the viewer is relaxed, a calm commentary is provided. In this way, by dynamically changing the commentary content based on the viewer's real-time reactions, viewer satisfaction can be improved.

[0071] The viewer customization unit can estimate the viewer's emotions and select a commentary style that corresponds to the emotions. In the viewer customization unit, for example, the generation AI analyzes the viewer's facial expressions, estimates the emotions, and selects a commentary style. For example, if the viewer is excited, a more energetic commentary is provided. In addition, the viewer customization unit can analyze the viewer's voice data, estimate the emotions, and select a commentary style. For example, if the viewer is relaxed, a calm commentary is provided. In addition, the viewer customization unit can analyze the viewer's real-time reactions and select a commentary style that corresponds to the emotions. For example, if the viewer is surprised, a commentary that emphasizes the surprise is provided. In this way, by selecting a commentary style that corresponds to the viewer's emotions, viewer satisfaction can be improved.

[0072] The viewer customization unit can provide information on players or teams according to the viewer's preferences. In the viewer customization unit, for example, the generation AI provides information on specific players or teams according to the viewer's preferences. For example, the viewer may introduce past performances and episodes of a player that the viewer likes. In addition, the viewer customization unit provides the latest information on players and teams based on the viewer's preferences. For example, the viewer may introduce the latest news and interviews on a team that the viewer supports. In addition, the viewer customization unit provides information on the tactics and playing styles of players and teams according to the viewer's preferences. For example, the generation AI may provide tactical commentary that interests the viewer. In this way, viewer satisfaction can be improved by providing information on players and teams according to the viewer's preferences.

[0073] The viewer customization unit can provide tactical commentary according to the viewer's preferences. In the viewer customization unit, for example, the generation AI provides commentary on specific tactics according to the viewer's preferences. For example, it provides detailed commentary on offensive and defensive tactics that interest the viewer. In addition, the viewer customization unit provides commentary on tactical moves during a match based on the viewer's preferences. For example, it provides detailed commentary on the tactical plays of the viewer's favorite team. In addition, the viewer customization unit provides commentary on tactics used in past matches according to the viewer's preferences and explains their effectiveness. For example, it introduces successful examples of tactics that the viewer is interested in. In this way, viewer satisfaction can be improved by providing tactical commentary according to the viewer's preferences.

[0074] The viewer customization unit can use the emotion estimation function to provide commentary content that corresponds to the viewer's emotions. In the viewer customization unit, for example, the generation AI estimates the viewer's emotions and provides commentary content that corresponds to those emotions. For example, if the viewer is excited, a more energetic commentary is provided. In addition, the viewer customization unit has the generation AI analyze the viewer's real-time reactions and provide commentary content that corresponds to the viewer's emotions. For example, if the viewer is relaxed, a calm commentary is provided. In addition, the viewer customization unit has the generation AI provide commentary content that corresponds to the viewer's emotions based on the viewer's emotion data. For example, if the viewer is surprised, a commentary that emphasizes surprise is provided. In this way, by providing commentary content that corresponds to the viewer's emotions, viewer satisfaction can be improved.

[0075] The system can learn slang or technical terms in different languages ​​and provide natural commentary. For example, the generation AI of the system can learn slang and technical terms in different languages ​​and provide natural commentary. For example, it can learn English soccer terms and Spanish basketball terms. The generation AI can also learn slang and technical terms in different languages ​​and provide commentary that is natural for viewers. For example, it can learn Japanese baseball terms and French tennis terms. The generation AI can also learn slang and technical terms in different languages ​​and provide commentary that is easy for viewers to understand. For example, it can learn Chinese badminton terms and German handball terms. In this way, by learning slang and technical terms in different languages, it is possible to provide natural commentary.

[0076] The system can use the emotion estimation function to select language expressions that correspond to the viewer's emotions. For example, the generation AI of the system estimates the viewer's emotions and selects language expressions that correspond to those emotions. For example, if the viewer is excited, energetic language expressions are used. The generation AI of the system also analyzes the viewer's real-time reactions and selects language expressions that correspond to the emotions. For example, if the viewer is relaxed, calm language expressions are used. The generation AI of the system also selects language expressions that correspond to the emotions based on the viewer's emotional data. For example, if the viewer is surprised, language expressions that emphasize surprise are used. In this way, by selecting language expressions that correspond to the viewer's emotions, viewer satisfaction can be improved.

[0077] The system can generate subtitles in different languages ​​in real time and provide them to viewers. For example, the system uses a generation AI to generate subtitles in different languages ​​in real time and provide them to viewers. For example, English commentary is translated into Japanese subtitles and provided. The system also uses a generation AI to generate subtitles in different languages ​​in real time and provide subtitles that are easy for viewers to understand. For example, Spanish commentary is translated into French subtitles and provided. The system also uses a generation AI to generate subtitles in different languages ​​in real time and provide subtitles that are natural for viewers. For example, Chinese commentary is translated into German subtitles and provided. This allows subtitles in different languages ​​to be generated in real time, deepening viewers' understanding.

[0078] The system can synthesize speech in different languages ​​and provide it to viewers. For example, the generation AI in the system synthesizes speech in different languages ​​and provides it to viewers. For example, English commentary is translated into Japanese speech and provided. The system also synthesizes speech in different languages ​​and provides speech that is easy for viewers to understand. For example, Spanish commentary is translated into French speech and provided. The system also synthesizes speech in different languages ​​and provides speech that is natural for viewers. For example, Chinese commentary is translated into German speech and provided. This allows viewers to deepen their understanding by synthesizing speech in different languages.

[0079] The system can use the emotion estimation function to select language expressions that correspond to the viewer's emotions. For example, the generation AI of the system estimates the viewer's emotions and selects language expressions that correspond to those emotions. For example, if the viewer is excited, energetic language expressions are used. The generation AI of the system also analyzes the viewer's real-time reactions and selects language expressions that correspond to the emotions. For example, if the viewer is relaxed, calm language expressions are used. The generation AI of the system also selects language expressions that correspond to the emotions based on the viewer's emotional data. For example, if the viewer is surprised, language expressions that emphasize surprise are used. In this way, by selecting language expressions that correspond to the viewer's emotions, viewer satisfaction can be improved.

[0080] The system can analyze data during a match in real time and provide it to viewers. For example, the system uses a generating AI to analyze data during a match in real time and provide it to viewers. For example, it displays a player's shooting success rate and pass success rate in real time. The system also uses a generating AI to analyze data during a match and provide statistical information that is important to viewers. For example, it displays a player's running distance and ball possession rate in real time. The system also uses a generating AI to analyze data during a match in real time and provide it in a format that is easy for viewers to understand. For example, it displays statistical information using graphs and charts. This allows viewers to deepen their understanding by analyzing data during a match in real time.

[0081] The system can refer to past match data and compare it with the current match situation to provide statistical information. For example, the system's generation AI refers to past match data and compares it with the current match situation to provide statistical information. For example, it compares the shooting success rate in past matches with the shooting success rate in the current match. The system also has the generation AI analyze past match data and evaluate player performance by comparing it with the current match situation. For example, it compares the pass success rate in past matches with the pass success rate in the current match. The system also has the generation AI refer to past match data and evaluate the team's tactical moves by comparing it with the current match situation. For example, it compares the ball possession rate in past matches with the ball possession rate in the current match. This allows viewers to deepen their understanding by comparing past match data with the current match situation and providing statistical information.

[0082] The system can use the emotion estimation function to provide statistical information according to the viewer's emotions. For example, the generation AI of the system estimates the viewer's emotions and provides statistical information according to those emotions. For example, if the viewer is excited, important statistical information is highlighted. The system also has the generation AI analyze the viewer's real-time reactions and provide statistical information according to the emotion. For example, if the viewer is relaxed, detailed statistical information is provided. The system also has the generation AI provide statistical information according to the viewer's emotions based on the viewer's emotional data. For example, if the viewer is surprised, statistical information that emphasizes surprise is provided. This allows for a deeper understanding of the viewer by providing statistical information according to the viewer's emotions.

[0083] The system can compare statistical information from different sports and provide it to viewers. For example, the system's generating AI can compare statistical information from soccer and basketball and provide it to viewers. For example, it can explain the differences in shooting success rate and passing success rate. The system can also compare statistical information from tennis and badminton and provide it to viewers. For example, it can explain the differences in the scoring systems and the pace of the game. The system can also compare statistical information from rugby and American football and provide it to viewers. For example, it can explain the differences in tackle success rate and ball possession time. This allows viewers to deepen their understanding by comparing statistical information from different sports.

[0084] The system can visually display data during a game, allowing viewers to intuitively understand it. For example, the system may have a generating AI visually display data during a game, allowing viewers to intuitively understand it. For example, the system may display shooting success rate in a graph. The system may also have a generating AI visually display data during a game, allowing viewers to intuitively understand the progress of the game. For example, the system may display ball possession rate in a chart. The system may also have a generating AI visually display data during a game, allowing viewers to intuitively understand player performance. For example, the system may display running distance on a map. This allows viewers to deepen their understanding by visually displaying data during a game.

[0085] The system can use the emotion estimation function to provide statistical information according to the viewer's emotions. For example, the generation AI of the system estimates the viewer's emotions and provides statistical information according to those emotions. For example, if the viewer is excited, important statistical information is highlighted. The system also has the generation AI analyze the viewer's real-time reactions and provide statistical information according to the emotion. For example, if the viewer is relaxed, detailed statistical information is provided. The system also has the generation AI provide statistical information according to the viewer's emotions based on the viewer's emotional data. For example, if the viewer is surprised, statistical information that emphasizes surprise is provided. This allows for a deeper understanding of the viewer by providing statistical information according to the viewer's emotions.

[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 sports commentary system may further include a health management unit that monitors the health status of players. The health management unit may, for example, monitor a player's heart rate and oxygen saturation in real time and issue a warning if an abnormality is detected. The health management unit may also refer to a player's past health data and compare it with their current health status to detect abnormalities. For example, it may compare heart rate fluctuations in past games with heart rate fluctuations in the current game. The health management unit may also predict performance during a game based on the player's health status. For example, a high heart rate may indicate the possibility of accumulated fatigue. This allows for real-time monitoring of a player's health status and rapid response if an abnormality is detected.

[0088] Sports commentary systems can also estimate the emotions of spectators and evaluate the excitement of a match based on their emotions. For example, they can analyze spectators' facial expressions to estimate emotions such as joy or excitement. They can detect the moment when spectators cheer and evaluate the excitement of a match based on their emotions. They can also analyze spectator movements to estimate the level of excitement. For example, they can detect the moment when spectators stand up to cheer and evaluate the excitement of a match based on their level of excitement. They can also analyze spectator audio data to analyze the volume of cheers and boos. This makes it possible to evaluate the excitement of a match based on spectators' emotions and provide viewers with a sense of realism.

[0089] The sports commentary system may further include a tactical analysis unit that analyzes players' tactical movements. The tactical analysis unit, for example, analyzes player position data to understand the team's tactical movements. For example, it analyzes changes in formation and player positioning. The tactical analysis unit may also refer to past game data and compare it with the current game situation to highlight tactical differences. For example, it may explain the differences between tactics that were successful in past games and tactics in the current game. The tactical analysis unit may also analyze players' movement patterns and infer tactical intentions. For example, it may predict what movements a specific player will make. This allows the tactical movements of players to be analyzed and provides viewers with a tactical perspective.

[0090] Sports commentary systems can also analyze weather data during a game and reflect it in their commentary. For example, they can obtain weather data in real time and explain how it will affect the progress of the game. For example, if it is raining, they can explain how the ball will slide and the condition of the players' feet. They can also analyze wind speed and direction and explain how it will affect the tactics of the game. For example, they can explain that if the wind is strong, it may affect the accuracy of long passes and shots. They can also analyze temperature and humidity and explain how it will affect the physical strength of the players. In this way, they can analyze weather data during a game and help viewers understand how it will affect the progress of the game.

[0091] Sports commentary systems can also estimate the psychological state of players and provide commentary based on psychological factors. For example, by analyzing a player's facial expression and movements, the system can estimate psychological states such as tension or a lack of concentration. For example, it can detect tension from a player's facial expression during pre-match warm-ups and provide commentary based on that psychological state. It can also analyze a player's tone of voice and audio data to estimate their psychological state. For example, it can detect tension from the tone of a player's voice when asking for instructions from their coach and provide commentary based on that psychological state. It can also analyze a player's movement patterns to infer psychological factors. For example, it can infer fatigue or a lack of concentration from a player's sluggish movements toward the end of a match. This makes it possible to provide real-time commentary based on the player's psychological state.

[0092] Sports commentary systems can also analyze audio data during a game, translating instructions from players and coaches in real time and incorporating them into the commentary. For example, the system can translate the conversations when players ask their coaches for instructions. It can also analyze audio data during a game, translating tactical instructions from players and coaches in real time and incorporating them into the commentary. For example, it can translate the conversations when a coach gives tactical instructions to players. It can also analyze audio data during a game, translating the emotional reactions of players and coaches in real time and incorporating them into the commentary. For example, it can translate the voices of joy when a player scores a goal. In this way, by translating instructions from players and coaches in real time and incorporating them into the commentary, it is possible to provide commentary that is easy for viewers to understand.

[0093] The sports commentary system can further estimate the viewer's emotions and select a commentary style that corresponds to the emotions. For example, the system can analyze the viewer's facial expressions to estimate the emotions and select a commentary style accordingly. For example, if the viewer is excited, a more energetic commentary can be provided. The system can also analyze the viewer's voice data to estimate the emotions and select a commentary style accordingly. For example, if the viewer is relaxed, a more calm commentary can be provided. The system can also analyze the viewer's real-time reactions and select a commentary style that corresponds to the emotions. For example, if the viewer is surprised, a commentary that emphasizes surprise can be provided. In this way, by selecting a commentary style that corresponds to the viewer's emotions, viewer satisfaction can be improved.

[0094] Sports commentary systems can further visually display data during a game to allow viewers to intuitively understand it. For example, shooting success rate can be displayed in a graph. Data during a game can also be visually displayed to allow viewers to intuitively understand the progress of the game. For example, ball possession rate can be displayed in a chart. Data during a game can also be visually displayed to allow viewers to intuitively understand player performance. For example, running distance can be displayed on a map. In this way, by visually displaying data during a game, viewers can deepen their understanding.

[0095] The sports commentary system can further estimate the viewer's emotions and provide statistical information according to those emotions. For example, the system can estimate the viewer's emotions and provide statistical information according to those emotions. For example, if the viewer is excited, important statistical information can be highlighted. The system can also analyze the viewer's real-time reactions and provide statistical information according to their emotions. For example, if the viewer is relaxed, detailed statistical information can be provided. The system can also provide statistical information according to the viewer's emotions based on the viewer's emotional data. For example, if the viewer is surprised, statistical information that emphasizes surprise can be provided. In this way, by providing statistical information according to the viewer's emotions, the viewer's understanding can be deepened.

[0096] Sports commentary systems can also compare statistical information between different sports and provide it to viewers. For example, statistical information between soccer and basketball can be compared and provided to viewers, explaining the differences in shooting success rate and passing success rate, for example. Statistical information between tennis and badminton can also be compared and provided to viewers, explaining the differences in scoring systems and the pace of the game, for example. Statistical information between rugby and American football can also be compared and provided to viewers, explaining the differences in tackle success rate and ball possession time, for example. This allows viewers to deepen their understanding by comparing statistical information between different sports.

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

[0098] Step 1: The video analysis unit analyzes sports footage. For example, the video analysis unit uses image recognition technology to analyze player movements and motion analysis algorithms to understand the progress of the game. In soccer games, the unit analyzes the position of the ball and the movements of players to detect important events such as goals and fouls. Step 2: The commentary generation unit generates commentary content based on the progress of the game and the movements of the players analyzed by the video analysis unit. For example, it uses generative AI (text generation AI or multimodal generation AI) or template-based generation technology to provide commentary according to the progress of the game. Step 3: The rule explanation unit provides detailed rule explanations based on the commentary content generated by the commentary generation unit. For example, it has knowledge of sports rules and provides appropriate rule explanations for various situations that occur during a game. If a foul occurs in a basketball game, it will explain the type of foul and how it should be handled based on the rules. Step 4: The viewer customization unit customizes the commentary content to suit the viewer's preferences. For example, it provides content that meets the viewer's requests, such as commentary that focuses on a specific player or tactical commentary, based on the viewer's preferences and interests.

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

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

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

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

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

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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. a video analysis unit that analyzes the video; a commentary generation unit that generates commentary content based on the progress of the game or the movements of the players analyzed by the video analysis unit; a rule explanation unit that provides detailed rule explanations based on the commentary content generated by the commentary generation unit; A viewer customization unit that customizes the live content to suit the viewer's preferences. A system characterized by:

2. The video analysis unit The video footage from different camera angles is analyzed simultaneously, and the optimal angle is selected for commentary.

2. The system of claim 1.

3. The rule explanation unit Detect rule violations during the game in real time and provide commentary on the spot 2. The system of claim 1.

4. The viewer customization unit Analyzing the viewing history of the viewer and providing the live content according to the viewer's preferences 2. The system of claim 1.

5. The system comprises: Learn slang or jargon in different languages ​​and give natural commentary 2. The system of claim 1.

6. The video analysis unit Estimating the emotions of the players from their facial expressions or body movements and providing commentary based on the emotions 2. The system of claim 1.

Images