System

Abstract

An object of a system according to an embodiment is to automatically add a live commentary to a moving image of an individual or an amateur sport.SOLUTION: A system according to an embodiment includes a video analysis section, a live commentary generation section, and an output section. The video analysis unit analyzes a video of a sport video. The live commentary generation unit generates live commentary based on the video data of the sport video analyzed by the video analysis unit. The output unit outputs the live commentary content generated by the live commentary generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to add commentary to videos of individual or amateur sports, which meant that the viewing experience was not fully enjoyable.

[0005] The system according to the embodiment aims to automatically add commentary to videos of individual and amateur sports. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a commentary generation unit, and an output unit. The video analysis unit analyzes video of the sports video. The commentary generation unit generates commentary content based on the video data of the sports video analyzed by the video analysis unit. The output unit outputs the commentary content generated by the commentary generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically add commentary to videos of individual and amateur sports. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​announcer system according to an embodiment of the present invention automatically provides commentary on personally filmed children's sports videos and student amateur sports. This system analyzes the sports video footage, and the generation AI generates and outputs commentary content. This allows the AI ​​announcer system to add commentary to sports videos in real time, attracting spectators' interest and increasing the appeal of sports.

[0029] The AI ​​announcer system according to the embodiment includes a video analysis unit, a commentary generation unit, and an output unit. The video analysis unit analyzes video of a sports video. For example, the video analysis unit analyzes player movements using image recognition technology. The video analysis unit can also analyze the flow of a game using a motion analysis algorithm. The video analysis unit can also analyze player position information to grasp the progress of a game, for example. The commentary generation unit generates commentary content based on video data of the sports video analyzed by the video analysis unit. For example, the commentary generation unit generates commentary content using a text generation algorithm. The commentary generation unit can also generate the commentary content as audio using speech synthesis technology. The commentary generation unit can also generate appropriate commentary content based on, for example, player movements and the situation of the game. The output unit outputs the commentary content generated by the commentary generation unit. For example, the output unit outputs the commentary content as audio using an audio output device. The output unit can also display the commentary content as text using a text display device. The output unit can also display commentary in synchronization with the video, for example. This allows the AI ​​announcer system to add commentary to sports videos in real time. For example, the AI ​​announcer system can analyze a goal scene in a soccer game and commentate, "Goal! What a great shot!". The AI ​​announcer system can also analyze the flow of passes in a basketball game and commentate, "The pass went through, now's our chance to attack!". The AI ​​announcer system can also analyze the movements of players in a baseball game and comment, "Home run! What a great hit!". This allows the AI ​​announcer system to add commentary to sports videos in real time, attracting the interest of spectators and increasing the appeal of sports.

[0030] The commentary generation unit can provide more detailed and personalized commentary based on a player's past performance or characteristics. For example, the commentary generation unit uses a generation AI to analyze a player's past performance and characteristics in real time and reflect them in the commentary content. For example, it can provide information such as, "This player has scored three goals in the past five games." The commentary generation unit can also provide personalized commentary based on a player's playing style and physical characteristics. For example, it can provide information such as, "This player has speed and is good at breaking through the opponent's defense." The commentary generation unit can also predict the flow of a game based on a player's past match data and reflect this in the commentary content. For example, it can provide information such as, "This player tends to be strong in the second half." This enables detailed commentary based on a player's personal data.

[0031] The commentary generation unit can predict the flow of the game and provide expectations for the next play or strategic commentary in real time. The commentary generation unit, for example, uses a generative AI to analyze the flow of the game and provide commentary on expectations for the next play. For example, it provides a prediction such as, "The next attack has a high chance of scoring a goal." The commentary generation unit can also analyze the flow of the game and provide strategic commentary. For example, it provides commentary such as, "In this situation, it is important to solidify the defense." The commentary generation unit can also analyze the flow of the game and generate commentary content based on player movements and team tactics. For example, it provides information such as, "This player has scored many goals from set plays this season." This makes it possible to predict the flow of the game and provide expectations for the next play or strategic commentary.

[0032] Generative AI can be equipped with multilingual capabilities to support different sports, making it usable at international sporting events. Generative AI can be equipped with multilingual capabilities to support different sports. For example, the generative AI can provide commentary in multiple languages, such as soccer, basketball, and baseball. Generative AI can also enhance its multilingual capabilities to support international sporting events. For example, the generative AI can provide commentary in multiple languages, such as English, Spanish, and French. Generative AI can also learn the terminology and rules of each sport to support different sports. For example, the generative AI can understand the offside rule in soccer and the foul rule in basketball and provide appropriate commentary. This multilingual capability makes it usable at international sporting events.

[0033] The generation AI can automatically generate post-match highlights or analysis reports. The generation AI, for example, automatically generates post-match highlights. For example, the generation AI provides a highlight video that summarizes important scenes and scoring scenes. The generation AI can also automatically generate post-match analysis reports. For example, the generation AI analyzes player performance and team tactics and provides a detailed report. The generation AI can also evaluate players and teams based on post-match data. For example, the generation AI evaluates players' running distance and shooting success rate and reflects this in a report. This makes it possible to automatically generate post-match highlights and analysis reports.

[0034] The generation AI can automatically highlight important moments of a match and notify spectators. The generation AI can, for example, automatically highlight important moments of a match. For example, the generation AI can automatically extract scoring scenes and fine plays and notify spectators. The generation AI can also analyze important moments of a match in real time and provide them as highlights. For example, the generation AI can analyze the progress of a match and select important scenes. The generation AI can also provide important moments of a match as a highlight video. For example, the generation AI can edit highlight scenes of a match and notify spectators. This allows the generation AI to automatically highlight important moments of a match and notify spectators.

[0035] The generation AI can analyze game data and display scores that evaluate player or team performance in real time. The generation AI can, for example, analyze game data in real time and display scores that evaluate player or team performance. For example, the generation AI can score a player's running distance and shooting success rate. The generation AI can also analyze a team's tactics and strategy and evaluate performance. For example, the generation AI can evaluate a team's pass success rate and ball possession rate. The generation AI can also evaluate players and teams based on game data. For example, the generation AI can analyze a player's personal data and a team's game data and calculate a score. This makes it possible to evaluate player and team performance in real time and display a score.

[0036] The generation AI can automatically generate interviews with players or coaches before and after a match, providing background information about the match. The generation AI can, for example, automatically generate interviews with players and coaches before and after a match. For example, the generation AI can provide pre-match enthusiasm and post-match impressions in interview format. The generation AI can also provide background information about the match. For example, the generation AI generates background information about the match based on players' past performances and team tactics. The generation AI can also automatically generate comments from players and coaches before and after a match. For example, the generation AI can provide comments about pre-match strategies and post-match points. This makes it possible to automatically generate interviews with players and coaches before and after a match, providing background information.

[0037] Generative AI can automatically generate training plans based on match data and help improve player performance. Generative AI can automatically generate training plans based on match data. For example, generative AI can provide a training menu to strengthen a player's weaknesses. Generative AI can also generate training plans to improve a player's performance. For example, generative AI can analyze a player's physical data and match data to provide an optimal training plan. Generative AI can also evaluate a player's performance and adjust the training plan. For example, generative AI can monitor a player's progress and update the training plan. This allows for the automatic generation of training plans based on match data and helps improve a player's performance.

[0038] The generation AI can evaluate the commentary of new announcers in real time and provide feedback. For example, the generation AI can evaluate the commentary of new announcers in real time and provide feedback. For example, the generation AI can evaluate the accuracy and expressiveness of the commentary and point out areas for improvement. The generation AI can also evaluate the commentary skills of new announcers and provide feedback. For example, the generation AI can analyze the content and tone of the commentary and suggest specific areas for improvement. The generation AI can also monitor the commentary of new announcers in real time and provide feedback. For example, the generation AI can point out mistakes made during the commentary and areas for improvement in real time. This allows the generation AI to evaluate the commentary of new announcers in real time and provide feedback.

[0039] Generative AI can analyze past famous commentaries and automatically generate teaching materials to teach those techniques or expressions to new announcers. For example, generative AI can analyze past famous commentaries and automatically generate teaching materials to teach those techniques and expressions to new announcers. For example, generative AI can analyze audio data of famous commentaries and extract important points. Generative AI can also analyze text data of past famous commentaries and extract techniques and expressions. For example, generative AI can analyze phrases and expressions from famous commentaries and reflect them in teaching materials. Generative AI can also analyze video data of past famous commentaries and extract techniques and expressions. For example, generative AI can analyze scenes from famous commentaries and reflect them in teaching materials. This makes it possible to analyze past famous commentaries and automatically generate teaching materials to teach those techniques and expressions to new announcers.

[0040] The generation AI can automatically generate virtual game simulations for new announcers to use for their commentary practice. For example, the generation AI can automatically generate virtual game simulations for new announcers to use for their commentary practice. For example, the generation AI can generate a virtual soccer game and practice its commentary. The generation AI can also generate a virtual basketball game and practice its commentary. For example, the generation AI can provide a realistic game simulation based on virtual game data. The generation AI can also evaluate the commentary skills of new announcers based on the virtual game simulation. For example, the generation AI can analyze the commentary content of the virtual game and point out areas for improvement. This allows the automatic generation of virtual game simulations for new announcers to use for their commentary practice.

[0041] The generation AI can automatically generate a curriculum to gradually improve the commentary skills of a new announcer. For example, the generation AI can provide a curriculum that includes steps from basic to advanced. The generation AI can also evaluate the skill level of a new announcer and provide an appropriate curriculum. For example, the generation AI can provide a curriculum that includes everything from basic commentary techniques to advanced expression techniques. The generation AI can also monitor the progress of a new announcer and adjust the curriculum. For example, the generation AI can update the curriculum as skills improve. This makes it possible to automatically generate a curriculum to gradually improve the commentary skills of a new announcer.

[0042] The generation AI can provide customizable commentary templates for consumers and generate commentary that meets individual needs. The generation AI, for example, provides customizable commentary templates for consumers. For example, the generation AI generates commentary content based on a template selected by the user. The generation AI can also provide commentary templates that meet user needs. For example, the generation AI provides templates that meet the type of sport or the situation of the game. The generation AI can also customize commentary content according to the user's preferences. For example, the generation AI generates commentary that reflects phrases and tones specified by the user. This allows the generation AI to provide customizable commentary templates for consumers and generate commentary that meets individual needs.

[0043] The generation AI can provide a commentary service specialized for a specific sporting event or tournament for a corporation. The generation AI can provide a commentary service specialized for a specific sporting event or tournament for a corporation. For example, the generation AI can provide commentary tailored to a specific league or tournament. The generation AI can also provide a commentary service customized for a corporation. For example, the generation AI can provide commentary content tailored to the needs of a corporation. The generation AI can also provide commentary templates specialized for a specific sporting event or tournament. For example, the generation AI can provide commentary that reflects the rules and characteristics of a specific sporting event. This makes it possible to provide a commentary service specialized for a specific sporting event or tournament for a corporation.

[0044] The generation AI can provide a commentary service for events other than sports. The generation AI can provide a commentary service for events other than sports (for example, concerts or plays). For example, the generation AI can provide commentary on performance scenes at a concert or highlights from a play. The generation AI can also provide commentary templates specialized for events other than sports. For example, the generation AI can provide commentary that reflects the characteristics of a concert or a play. The generation AI can also customize the commentary content for events other than sports. For example, the generation AI can provide commentary content that suits the user's preferences. This makes it possible to provide a commentary service for events other than sports.

[0045] The generation AI can automatically generate commentary content to maximize the effectiveness of sponsorships and advertising. The generation AI can automatically generate commentary content to maximize the effectiveness of sponsorships and advertising. For example, the generation AI can provide commentary that includes the names of sponsors and advertising messages. The generation AI can also measure the effectiveness of sponsorships and advertising and generate optimal commentary content. For example, the generation AI can adjust the commentary content based on advertising placement methods and effectiveness measurement criteria. The generation AI can also propose strategies to maximize the effectiveness of sponsorships and advertising. For example, the generation AI can analyze the effectiveness of advertising and propose effective placement methods. This makes it possible to automatically generate commentary content to maximize the effectiveness of sponsorships and advertising.

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

[0047] The AI ​​announcer system can also automatically generate training plans based on match data to help improve player performance. For example, the generating AI can provide training menus to strengthen a player's weaknesses. The generating AI can also analyze a player's physical data and match data to provide an optimal training plan. Furthermore, the generating AI can monitor a player's progress and update the training plan. This allows the system to automatically generate training plans based on match data and help improve a player's performance.

[0048] The AI ​​announcer system can also automatically generate interviews with players and coaches before and after a match to provide background information about the match. For example, the generation AI can provide pre-match enthusiasm and post-match impressions in interview format. The generation AI can also generate background information about the match based on players' past performances and team tactics. Furthermore, the generation AI can provide comments about pre-match strategies and post-match points for improvement. This allows the system to automatically generate interviews with players and coaches before and after a match to provide background information.

[0049] The AI ​​announcer system can also display performance evaluations based on match data in real time. For example, the generating AI can score a player's running distance and shooting success rate. The generating AI can also evaluate a team's passing success rate and ball possession rate. Furthermore, the generating AI can analyze players' personal data and team match data to calculate scores. This allows the performance of players and teams to be evaluated in real time and scores to be displayed.

[0050] The AI ​​announcer system can also automatically highlight important moments in a match and notify spectators. For example, the generation AI can automatically extract scoring scenes and fine plays and notify spectators. The generation AI can also analyze the progress of a match and select important scenes. Furthermore, the generation AI can compile highlight scenes of the match and notify spectators. This allows the system to automatically highlight important moments in a match and notify spectators.

[0051] The AI ​​announcer system can also be equipped with multilingual capabilities to support different sports, making it suitable for use at international sporting events. For example, the generating AI can provide commentary in multiple languages ​​for soccer, basketball, baseball, etc. The generating AI can also provide commentary in multiple languages, such as English, Spanish, and French. Furthermore, the generating AI can learn the terminology and rules of each sport to provide appropriate commentary. This multilingual capability makes it suitable for use at international sporting events.

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

[0053] Step 1: The video analysis unit analyzes sports video footage. For example, the video analysis unit uses image recognition technology to analyze the movements of players. The video analysis unit can also analyze the flow of the game using motion analysis algorithms. Furthermore, the video analysis unit can analyze the positional information of players to understand the progress of the game. Step 2: The commentary generation unit generates commentary content based on the video data of the sports video analyzed by the video analysis unit. For example, the commentary generation unit generates commentary content using a text generation algorithm. The commentary generation unit can also generate commentary content as audio using voice synthesis technology. Furthermore, the commentary generation unit can also generate appropriate commentary content according to the movements of players and the situation of the game. Step 3: The output unit outputs the commentary content generated by the commentary generation unit. For example, the output unit outputs the commentary content as audio using an audio output device. The output unit can also display the commentary content as text using a text display device. Furthermore, the output unit can display the commentary content in synchronization with the video.

[0054] (Example 2) The AI ​​announcer system according to an embodiment of the present invention automatically provides commentary on personally filmed children's sports videos and student amateur sports. This system analyzes the sports video footage, and the generation AI generates and outputs commentary content. This allows the AI ​​announcer system to add commentary to sports videos in real time, attracting spectators' interest and increasing the appeal of sports.

[0055] The AI ​​announcer system according to the embodiment includes a video analysis unit, a commentary generation unit, and an output unit. The video analysis unit analyzes video of a sports video. For example, the video analysis unit analyzes player movements using image recognition technology. The video analysis unit can also analyze the flow of a game using a motion analysis algorithm. The video analysis unit can also analyze player position information to grasp the progress of a game, for example. The commentary generation unit generates commentary content based on video data of the sports video analyzed by the video analysis unit. For example, the commentary generation unit generates commentary content using a text generation algorithm. The commentary generation unit can also generate the commentary content as audio using speech synthesis technology. The commentary generation unit can also generate appropriate commentary content based on, for example, player movements and the situation of the game. The output unit outputs the commentary content generated by the commentary generation unit. For example, the output unit outputs the commentary content as audio using an audio output device. The output unit can also display the commentary content as text using a text display device. The output unit can also display commentary in synchronization with the video, for example. This allows the AI ​​announcer system to add commentary to sports videos in real time. For example, the AI ​​announcer system can analyze a goal scene in a soccer game and commentate, "Goal! What a great shot!". The AI ​​announcer system can also analyze the flow of passes in a basketball game and commentate, "The pass went through, now's our chance to attack!". The AI ​​announcer system can also analyze the movements of players in a baseball game and comment, "Home run! What a great hit!". This allows the AI ​​announcer system to add commentary to sports videos in real time, attracting the interest of spectators and increasing the appeal of sports.

[0056] The commentary generation unit can provide more detailed and personalized commentary based on a player's past performance or characteristics. For example, the commentary generation unit uses a generation AI to analyze a player's past performance and characteristics in real time and reflect them in the commentary content. For example, it can provide information such as, "This player has scored three goals in the past five games." The commentary generation unit can also provide personalized commentary based on a player's playing style and physical characteristics. For example, it can provide information such as, "This player has speed and is good at breaking through the opponent's defense." The commentary generation unit can also predict the flow of a game based on a player's past match data and reflect this in the commentary content. For example, it can provide information such as, "This player tends to be strong in the second half." This enables detailed commentary based on a player's personal data.

[0057] The commentary generation unit can predict the flow of the game and provide expectations for the next play or strategic commentary in real time. The commentary generation unit, for example, uses a generative AI to analyze the flow of the game and provide commentary on expectations for the next play. For example, it provides a prediction such as, "The next attack has a high chance of scoring a goal." The commentary generation unit can also analyze the flow of the game and provide strategic commentary. For example, it provides commentary such as, "In this situation, it is important to solidify the defense." The commentary generation unit can also analyze the flow of the game and generate commentary content based on player movements and team tactics. For example, it provides information such as, "This player has scored many goals from set plays this season." This makes it possible to predict the flow of the game and provide expectations for the next play or strategic commentary.

[0058] The commentary generation unit can use the emotion estimation function to analyze the emotions of the spectators in real time and generate commentary content according to the emotions. The commentary generation unit, for example, uses the emotion estimation function to analyze the emotions of the spectators in real time and generate commentary content according to the emotions. For example, if the spectators are excited, the commentary generation unit provides a more exciting commentary. The commentary generation unit can also analyze the emotions of the spectators and adjust the tone of the commentary according to the emotions. For example, if the spectators are nervous, the commentary generation unit provides a calm tone. The commentary generation unit can also analyze the emotions of the spectators and change the content of the commentary according to the emotions. For example, if the spectators are happy, the commentary generation unit provides a positive commentary. This enables personalized commentary according to the emotions of the spectators.

[0059] Generative AI can be equipped with multilingual capabilities to support different sports, making it usable at international sporting events. Generative AI can be equipped with multilingual capabilities to support different sports. For example, the generative AI can provide commentary in multiple languages, such as soccer, basketball, and baseball. Generative AI can also enhance its multilingual capabilities to support international sporting events. For example, the generative AI can provide commentary in multiple languages, such as English, Spanish, and French. Generative AI can also learn the terminology and rules of each sport to support different sports. For example, the generative AI can understand the offside rule in soccer and the foul rule in basketball and provide appropriate commentary. This multilingual capability makes it usable at international sporting events.

[0060] The generation AI can automatically generate post-match highlights or analysis reports. The generation AI, for example, automatically generates post-match highlights. For example, the generation AI provides a highlight video that summarizes important scenes and scoring scenes. The generation AI can also automatically generate post-match analysis reports. For example, the generation AI analyzes player performance and team tactics and provides a detailed report. The generation AI can also evaluate players and teams based on post-match data. For example, the generation AI evaluates players' running distance and shooting success rate and reflects this in a report. This makes it possible to automatically generate post-match highlights and analysis reports.

[0061] The emotion estimation function can monitor the emotions of spectators in real time during commentary and adjust the tone or content of the commentary according to changes in their emotions. The emotion estimation function, for example, monitors the emotions of spectators in real time during commentary. For example, the emotion estimation function captures the spectators' facial expressions with a camera and analyzes their emotions. The emotion estimation function can also record the spectators' voices and estimate their emotions using voice analysis technology. The emotion estimation function can also collect the spectators' biometric data (heart rate and electrodermal activity) with a sensor and analyze their emotions. For example, it can calculate an emotion score based on fluctuations in heart rate. The emotion estimation function can adjust the tone of the commentary according to changes in the spectators' emotions. For example, if the spectators are excited, the commentary tone can be increased. The emotion estimation function can also adjust the content of the commentary according to changes in the spectators' emotions. For example, if the spectators are nervous, the commentary can be calmed down. This makes it possible to adjust the tone and content of the commentary according to the spectators' emotions.

[0062] The generation AI can automatically highlight important moments of a match and notify spectators. The generation AI can, for example, automatically highlight important moments of a match. For example, the generation AI can automatically extract scoring scenes and fine plays and notify spectators. The generation AI can also analyze important moments of a match in real time and provide them as highlights. For example, the generation AI can analyze the progress of a match and select important scenes. The generation AI can also provide important moments of a match as a highlight video. For example, the generation AI can edit highlight scenes of a match and notify spectators. This allows the generation AI to automatically highlight important moments of a match and notify spectators.

[0063] The generation AI can analyze game data and display scores that evaluate player or team performance in real time. The generation AI can, for example, analyze game data in real time and display scores that evaluate player or team performance. For example, the generation AI can score a player's running distance and shooting success rate. The generation AI can also analyze a team's tactics and strategy and evaluate performance. For example, the generation AI can evaluate a team's pass success rate and ball possession rate. The generation AI can also evaluate players and teams based on game data. For example, the generation AI can analyze a player's personal data and a team's game data and calculate a score. This makes it possible to evaluate player and team performance in real time and display a score.

[0064] The emotion estimation function can provide commentary that focuses on specific players or plays based on the emotions of the spectators. The emotion estimation function can provide commentary that focuses on specific players or plays based on the emotions of the spectators. For example, if the spectators are excited, the function can emphasize notable players or plays. The emotion estimation function can also analyze the emotions of the spectators and generate commentary content that corresponds to their emotions. For example, if the spectators are happy, the function can provide commentary that is positive. The emotion estimation function can also analyze the emotions of the spectators and adjust the tone of the commentary based on their emotions. For example, if the spectators are nervous, the function can provide commentary in a calm tone. This makes it possible to provide commentary that focuses on specific players or plays based on the emotions of the spectators.

[0065] The generation AI can automatically generate interviews with players or coaches before and after a match, providing background information about the match. The generation AI can, for example, automatically generate interviews with players and coaches before and after a match. For example, the generation AI can provide pre-match enthusiasm and post-match impressions in interview format. The generation AI can also provide background information about the match. For example, the generation AI generates background information about the match based on players' past performances and team tactics. The generation AI can also automatically generate comments from players and coaches before and after a match. For example, the generation AI can provide comments about pre-match strategies and post-match points. This makes it possible to automatically generate interviews with players and coaches before and after a match, providing background information.

[0066] Generative AI can automatically generate training plans based on match data and help improve player performance. Generative AI can automatically generate training plans based on match data. For example, generative AI can provide a training menu to strengthen a player's weaknesses. Generative AI can also generate training plans to improve a player's performance. For example, generative AI can analyze a player's physical data and match data to provide an optimal training plan. Generative AI can also evaluate a player's performance and adjust the training plan. For example, generative AI can monitor a player's progress and update the training plan. This allows for the automatic generation of training plans based on match data and helps improve a player's performance.

[0067] The emotion estimation function can automatically provide replays or slow-motion footage according to the emotions of the spectator. The emotion estimation function can automatically provide replays or slow-motion footage according to the emotions of the spectator. For example, if the spectator is excited, a replay of an important scene is provided. The emotion estimation function can also analyze the emotions of the spectator and provide slow-motion footage according to the emotion. For example, if the spectator is moved, a slow-motion footage of an emotional scene is provided. The emotion estimation function can also analyze the emotions of the spectator and select replays or slow-motion footage according to the emotion. For example, if the spectator is surprised, a replay of a surprising scene is provided. In this way, replays and slow-motion footage according to the emotions of the spectator can be automatically provided.

[0068] The generation AI can evaluate the commentary of new announcers in real time and provide feedback. For example, the generation AI can evaluate the commentary of new announcers in real time and provide feedback. For example, the generation AI can evaluate the accuracy and expressiveness of the commentary and point out areas for improvement. The generation AI can also evaluate the commentary skills of new announcers and provide feedback. For example, the generation AI can analyze the content and tone of the commentary and suggest specific areas for improvement. The generation AI can also monitor the commentary of new announcers in real time and provide feedback. For example, the generation AI can point out mistakes made during the commentary and areas for improvement in real time. This allows the generation AI to evaluate the commentary of new announcers in real time and provide feedback.

[0069] Generative AI can analyze past famous commentaries and automatically generate teaching materials to teach those techniques or expressions to new announcers. For example, generative AI can analyze past famous commentaries and automatically generate teaching materials to teach those techniques and expressions to new announcers. For example, generative AI can analyze audio data of famous commentaries and extract important points. Generative AI can also analyze text data of past famous commentaries and extract techniques and expressions. For example, generative AI can analyze phrases and expressions from famous commentaries and reflect them in teaching materials. Generative AI can also analyze video data of past famous commentaries and extract techniques and expressions. For example, generative AI can analyze scenes from famous commentaries and reflect them in teaching materials. This makes it possible to analyze past famous commentaries and automatically generate teaching materials to teach those techniques and expressions to new announcers.

[0070] The emotion estimation function can analyze the emotional reactions of spectators to a rookie announcer's commentary and point out areas for improvement. The emotion estimation function can, for example, analyze the emotional reactions of spectators to a rookie announcer's commentary and point out areas for improvement. For example, the emotion estimation function can analyze the spectators' facial expressions to evaluate which parts of the commentary were effective. The emotion estimation function can also analyze the spectators' voices to evaluate which parts of the commentary evoked emotions. For example, the emotion estimation function can analyze the spectators' tone and speed of voice to point out areas for improvement in the commentary. The emotion estimation function can also analyze the spectators' biometric data to evaluate which parts of the commentary evoked emotions. For example, the emotion estimation function can analyze the spectators' heart rate and electrodermal activity to point out areas for improvement in the commentary. In this way, the emotional reactions of spectators to a rookie announcer's commentary can be analyzed and points out areas for improvement.

[0071] The generation AI can automatically generate virtual game simulations for new announcers to use for their commentary practice. For example, the generation AI can automatically generate virtual game simulations for new announcers to use for their commentary practice. For example, the generation AI can generate a virtual soccer game and practice its commentary. The generation AI can also generate a virtual basketball game and practice its commentary. For example, the generation AI can provide a realistic game simulation based on virtual game data. The generation AI can also evaluate the commentary skills of new announcers based on the virtual game simulation. For example, the generation AI can analyze the commentary content of the virtual game and point out areas for improvement. This allows the automatic generation of virtual game simulations for new announcers to use for their commentary practice.

[0072] The generation AI can automatically generate a curriculum to gradually improve the commentary skills of a new announcer. For example, the generation AI can provide a curriculum that includes steps from basic to advanced. The generation AI can also evaluate the skill level of a new announcer and provide an appropriate curriculum. For example, the generation AI can provide a curriculum that includes everything from basic commentary techniques to advanced expression techniques. The generation AI can also monitor the progress of a new announcer and adjust the curriculum. For example, the generation AI can update the curriculum as skills improve. This makes it possible to automatically generate a curriculum to gradually improve the commentary skills of a new announcer.

[0073] The emotion estimation function can provide real-time feedback on the spectators' emotions toward the rookie announcer's commentary, thereby improving the quality of the commentary. The emotion estimation function, for example, provides real-time feedback on the spectators' emotions toward the rookie announcer's commentary. For example, the emotion estimation function points out areas for improvement in the commentary based on the spectators' emotion scores. The emotion estimation function can also analyze the spectators' emotions and provide feedback to improve the quality of the commentary. For example, the emotion estimation function analyzes the spectators' facial expressions and voices and suggests specific areas for improvement. The emotion estimation function can also monitor the spectators' emotions in real time and provide feedback to improve the quality of the commentary. For example, the emotion estimation function points out mistakes made during the commentary and areas for improvement in real time. This allows real-time feedback on the spectators' emotions toward the rookie announcer's commentary, thereby improving the quality of the commentary.

[0074] The generation AI can provide customizable commentary templates for consumers and generate commentary that meets individual needs. The generation AI, for example, provides customizable commentary templates for consumers. For example, the generation AI generates commentary content based on a template selected by the user. The generation AI can also provide commentary templates that meet user needs. For example, the generation AI provides templates that meet the type of sport or the situation of the game. The generation AI can also customize commentary content according to the user's preferences. For example, the generation AI generates commentary that reflects phrases and tones specified by the user. This allows the generation AI to provide customizable commentary templates for consumers and generate commentary that meets individual needs.

[0075] The generation AI can provide a commentary service specialized for a specific sporting event or tournament for a corporation. The generation AI can provide a commentary service specialized for a specific sporting event or tournament for a corporation. For example, the generation AI can provide commentary tailored to a specific league or tournament. The generation AI can also provide a commentary service customized for a corporation. For example, the generation AI can provide commentary content tailored to the needs of a corporation. The generation AI can also provide commentary templates specialized for a specific sporting event or tournament. For example, the generation AI can provide commentary that reflects the rules and characteristics of a specific sporting event. This makes it possible to provide a commentary service specialized for a specific sporting event or tournament for a corporation.

[0076] The generation AI can provide a commentary service for events other than sports. The generation AI can provide a commentary service for events other than sports (for example, concerts or plays). For example, the generation AI can provide commentary on performance scenes at a concert or highlights from a play. The generation AI can also provide commentary templates specialized for events other than sports. For example, the generation AI can provide commentary that reflects the characteristics of a concert or a play. The generation AI can also customize the commentary content for events other than sports. For example, the generation AI can provide commentary content that suits the user's preferences. This makes it possible to provide a commentary service for events other than sports.

[0077] The generation AI can automatically generate commentary content to maximize the effectiveness of sponsorships and advertising. The generation AI can automatically generate commentary content to maximize the effectiveness of sponsorships and advertising. For example, the generation AI can provide commentary that includes the names of sponsors and advertising messages. The generation AI can also measure the effectiveness of sponsorships and advertising and generate optimal commentary content. For example, the generation AI can adjust the commentary content based on advertising placement methods and effectiveness measurement criteria. The generation AI can also propose strategies to maximize the effectiveness of sponsorships and advertising. For example, the generation AI can analyze the effectiveness of advertising and propose effective placement methods. This makes it possible to automatically generate commentary content to maximize the effectiveness of sponsorships and advertising.

[0078] The emotion estimation function can display personalized advertisements in real time based on the emotions of spectators. The emotion estimation function can, for example, display personalized advertisements in real time based on the emotions of spectators. For example, if a spectator is excited, an advertisement that matches that emotion is displayed. The emotion estimation function can also analyze the emotions of spectators and generate advertisement content according to the emotions. For example, if a spectator is happy, an advertisement with positive content is displayed. The emotion estimation function can also monitor the emotions of spectators in real time and adjust the timing of advertisement display. For example, if there is a large concentration of spectators, the advertisement display is refrained from. This makes it possible to display personalized advertisements in real time based on the emotions of spectators.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The AI ​​announcer system can also estimate the emotions of spectators and adjust the commentary based on the estimated emotions. For example, if the spectators are excited, the commentary can be more exciting. If the spectators are nervous, the commentary can be calmer. Furthermore, if the spectators are happy, the commentary can be more positive. This makes it possible to provide personalized commentary based on the spectators' emotions.

[0081] The AI ​​announcer system can also estimate the emotions of spectators and emphasize the performance of players based on the estimated emotions. For example, if the spectators are excited, it can highlight a player's great play. If the spectators are moved, it can provide detailed explanations of the player's efforts and background. Furthermore, if the spectators are surprised, it can provide detailed commentary on the surprising play. This enables detailed commentary that reflects the spectators' emotions.

[0082] The AI ​​announcer system can also estimate the emotions of spectators and predict the flow of the game based on the estimated emotions. For example, if spectators are excited, the system can provide commentary that builds anticipation for the next play. If spectators are nervous, the system can emphasize the importance of the next play. Furthermore, if spectators are happy, the system can provide positive predictions. This makes it possible to predict the flow of the game based on spectators' emotions.

[0083] The AI ​​announcer system can also estimate the emotions of spectators and provide highlights of the match based on the estimated emotions. For example, if the spectator is excited, it can provide replays of important scenes. If the spectator is moved, it can provide slow-motion footage of moving scenes. Furthermore, if the spectator is surprised, it can provide replays of surprising scenes. This makes it possible to provide highlights that correspond to the spectator's emotions.

[0084] The AI ​​announcer system can also estimate the emotions of the spectators and adjust the tone of the commentary based on the estimated emotions. For example, if the spectators are excited, the commentary tone can be raised. If the spectators are nervous, the commentary can be given in a calmer tone. Furthermore, if the spectators are happy, the commentary can be given in a more positive tone. This makes it possible to adjust the tone of the commentary according to the emotions of the spectators.

[0085] The AI ​​announcer system can also automatically generate training plans based on match data to help improve player performance. For example, the generating AI can provide training menus to strengthen a player's weaknesses. The generating AI can also analyze a player's physical data and match data to provide an optimal training plan. Furthermore, the generating AI can monitor a player's progress and update the training plan. This allows the system to automatically generate training plans based on match data and help improve a player's performance.

[0086] The AI ​​announcer system can also automatically generate interviews with players and coaches before and after a match to provide background information about the match. For example, the generation AI can provide pre-match enthusiasm and post-match impressions in interview format. The generation AI can also generate background information about the match based on players' past performances and team tactics. Furthermore, the generation AI can provide comments about pre-match strategies and post-match points for improvement. This allows the system to automatically generate interviews with players and coaches before and after a match to provide background information.

[0087] The AI ​​announcer system can also display performance evaluations based on match data in real time. For example, the generating AI can score a player's running distance and shooting success rate. The generating AI can also evaluate a team's passing success rate and ball possession rate. Furthermore, the generating AI can analyze players' personal data and team match data to calculate scores. This allows the performance of players and teams to be evaluated in real time and scores to be displayed.

[0088] The AI ​​announcer system can also automatically highlight important moments in a match and notify spectators. For example, the generation AI can automatically extract scoring scenes and fine plays and notify spectators. The generation AI can also analyze the progress of a match and select important scenes. Furthermore, the generation AI can compile highlight scenes of the match and notify spectators. This allows the system to automatically highlight important moments in a match and notify spectators.

[0089] The AI ​​announcer system can also be equipped with multilingual capabilities to support different sports, making it suitable for use at international sporting events. For example, the generating AI can provide commentary in multiple languages ​​for soccer, basketball, baseball, etc. The generating AI can also provide commentary in multiple languages, such as English, Spanish, and French. Furthermore, the generating AI can learn the terminology and rules of each sport to provide appropriate commentary. This multilingual capability makes it suitable for use at international sporting events.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The video analysis unit analyzes sports video footage. For example, the video analysis unit uses image recognition technology to analyze the movements of players. The video analysis unit can also analyze the flow of the game using motion analysis algorithms. Furthermore, the video analysis unit can analyze the positional information of players to understand the progress of the game. Step 2: The commentary generation unit generates commentary content based on the video data of the sports video analyzed by the video analysis unit. For example, the commentary generation unit generates commentary content using a text generation algorithm. The commentary generation unit can also generate commentary content as audio using voice synthesis technology. Furthermore, the commentary generation unit can also generate appropriate commentary content according to the movements of players and the situation of the game. Step 3: The output unit outputs the commentary content generated by the commentary generation unit. For example, the output unit outputs the commentary content as audio using an audio output device. The output unit can also display the commentary content as text using a text display device. Furthermore, the output unit can display the commentary content in synchronization with the video.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] 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.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 section that analyzes sports video footage, a commentary generation unit that generates commentary content based on the video data of the sports video analyzed by the video analysis unit; an output unit that outputs the commentary content generated by the commentary generation unit; A system characterized by:

2. The commentary generation unit Predict the flow of the game and provide real-time predictions or strategic commentary on the next play 2. The system of claim 1.

3. The generated AI is It will have multilingual capabilities, support different sports, and be available for international sporting events.

2. The system of claim 1.

4. The generated AI is Automatically highlight key moments in the match and notify spectators 2. The system of claim 1.

5. The generated AI is Evaluate new announcers' play-by-plays in real time and provide feedback 2. The system of claim 1.

6. The commentary generation unit Analyzing spectators' emotions in real time and generating commentary content according to those emotions 2. The system of claim 1.

7. The emotion estimation function is Commentary that focuses on specific players or plays based on audience sentiment 2. The system of claim 1.

8. The emotion estimation function is Conducting marketing analysis based on spectator sentiment data to optimize merchandising strategies 2. The system of claim 1.

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