System

Abstract

An object of a system according to an embodiment is to provide an explanation according to a user's preference.SOLUTION: A system according to an embodiment includes a video analysis unit, an explanation generation unit, a mode selection unit, and a voice selection unit. The video analysis unit analyzes a video of a sports broadcast. The explanation generation unit generates an explanation based on the video data analyzed by the video analysis unit. The mode selection unit adjusts the content of the explanation according to the mode selected by the user. The voice selection part converts the explanation into voice according to the voice selected by the user.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] In conventional technology, the commentary for live sports broadcasts is fixed, making it difficult to provide commentary that meets the user's preferences.

[0005] The system according to the embodiment aims to provide explanations according to the preferences of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a commentary generation unit, a mode selection unit, and a voice selection unit. The video analysis unit analyzes video of a live sports broadcast. The commentary generation unit generates commentary based on the video data analyzed by the video analysis unit. The mode selection unit adjusts the content of the commentary depending on the mode selected by the user. The voice selection unit converts the commentary into audio depending on the voice selected by the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide explanations according to the preferences of the user. [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 sports commentary system according to the embodiment of the present invention is a system in which a generation AI provides commentary in a mode and voice that matches the user's preferences in accordance with the video of a live sports broadcast. This allows the sports commentary system to provide commentary that matches the user's preferences.

[0029] A sports commentary system according to an embodiment includes a video analysis unit, a commentary generation unit, a mode selection unit, and a voice selection unit. The video analysis unit analyzes video of a live sports broadcast. For example, in a soccer game, the video analysis unit analyzes the position of the ball and the movements of the players to understand the progress of the game. In a baseball game, the video analysis unit analyzes the pitcher's pitch and the batter's swing to understand the development of the game. The commentary generation unit generates commentary based on the video data analyzed by the video analysis unit. For example, when a goal is scored in a soccer game, the commentary generation unit generates commentary such as "What a great shot!" In addition, when a home run is hit in a baseball game, the commentary generation unit generates commentary such as "What a great home run!" The mode selection unit adjusts the content of the commentary depending on the mode selected by the user. For example, there are an expert commentary mode, a beginner commentary mode, a humorous commentary mode, and the like. In the expert commentary mode, the mode selection unit provides detailed commentary on tactics and techniques, and in the beginner commentary mode, the mode selection unit provides explanations of basic rules and plays. The voice selection unit converts the commentary into audio depending on the voice selected by the user. For example, there are male voices, female voices, youthful voices, calm voices, etc. The voice selection unit converts the commentary into audio according to the voice selected by the user and provides it to the user. This allows the sports commentary system according to the embodiment to provide commentary according to the user's preferences. For example, the content and tone of the commentary can be adjusted according to the mode and voice selected by the user to improve the viewing experience.

[0030] The video analysis unit can identify abnormal plays and tactics by referring to past match data and comparing it with the progress of the current match. For example, the video analysis unit analyzes past match data and compares it with the progress of the current match. For example, if a specific player is moving in an unusual manner, the abnormality is detected. The video analysis unit also analyzes the tactics of the current match based on the past match data. For example, if a team is using an unusual formation, the tactic is identified. The video analysis unit also combines past match data with current match data to comprehensively identify abnormal plays and tactics. For example, if a player's movement or a team's tactics differ significantly from past matches, the abnormality is identified. This makes it possible to identify abnormal plays and tactics.

[0031] The video analysis unit can be expanded to analyze live broadcast footage of different sports. For example, the video analysis unit analyzes video of a tennis match to understand the position of the ball and the movements of the players. For example, it analyzes the speed and course of the serve to understand the progress of the match. The video analysis unit also analyzes video of a basketball match to understand the movements of the players and the position of the ball. For example, it analyzes the success rate of shots and the movements of the defense to understand the development of the match. The video analysis unit also analyzes video of a rugby match to understand the movements of the players and the position of the ball. For example, it analyzes the success rate of tries and the movements of tackles to understand the progress of the match. This allows the system to be expanded to analyze live broadcast footage of different sports.

[0032] The video analysis unit analyzes not only sports broadcast footage but also player interview footage and practice footage, allowing for comprehensive match analysis. The video analysis unit, for example, analyzes player interview footage to extract information about the player's psychological state and tactics. For example, it analyzes comments made in pre-match interviews to predict how the match will play out. The video analysis unit also analyzes practice footage to evaluate the player's performance and skills. For example, it analyzes movements during practice to evaluate the player's condition and improvement in skills. The video analysis unit also combines sports broadcast footage, interview footage, and practice footage to perform comprehensive match analysis. For example, it comprehensively analyzes comments made in pre-match interviews, performance during practice, and movements during the match to predict how the match will play out. This allows for comprehensive match analysis.

[0033] The commentary generation unit dynamically changes the tone and content of the commentary according to the progress of the match, thereby reflecting the tension and excitement of the match. The commentary generation unit, for example, analyzes the progress of the match in real time and dynamically changes the tone of the commentary. For example, when the match is getting heated, the commentary is given in an excited tone. The commentary generation unit also dynamically changes the content of the commentary according to the progress of the match. For example, when the match is getting tense, detailed technical commentary is given. The commentary generation unit also comprehensively changes the tone and content of the commentary according to the progress of the match. For example, when the match is getting exciting, detailed technical commentary is given in an excited tone. This makes it possible to provide commentary that reflects the tension and excitement of the match.

[0034] The commentary generation unit can provide commentary by referring to a player's past performance data and comparing it with a current play. The commentary generation unit, for example, analyzes a player's past performance data and compares it with a current play. For example, it compares a player's successful plays in the past with his current play and explains the differences. The commentary generation unit also evaluates a current play based on a player's past performance data. For example, it compares a player's successful plays in the past with his current play and evaluates the success rate of the current play. The commentary generation unit also comprehensively analyzes a player's past performance data and his current play and provides commentary. For example, it compares a player's successful plays in the past with his current play and explains the differences in detail. This makes it possible to provide commentary by comparing past performance data with current play.

[0035] The commentary generation unit can automatically select highlight scenes of a match and generate special commentary for those scenes. For example, the generation AI automatically selects highlight scenes of a match and generates special commentary for those scenes. For example, detailed commentary is provided for goal scenes and important plays. The commentary generation unit also develops an algorithm for selecting highlight scenes of a match. For example, the generation AI analyzes the progress of the match and the movements of players to identify important scenes. The commentary generation unit also builds a database for generating special commentary for highlight scenes of a match. For example, detailed technical explanations for each scene and background information on players are collected and reflected in the commentary. This makes it possible to provide special commentary for highlight scenes of a match.

[0036] The mode selection unit can learn the user's past viewing history and preferences and automatically suggest the optimal commentary mode. For example, the mode selection unit uses a generation AI to analyze the user's past viewing history and automatically suggest the optimal commentary mode. For example, a specialized commentary mode is suggested for a user who has previously preferred to watch specialized commentaries. The mode selection unit also develops an algorithm to learn the user's preferences. For example, the generation AI analyzes viewing history and survey results to identify the user's preferences. The mode selection unit also builds a database to suggest the optimal commentary mode. For example, the features and content of each mode are collected and reflected in the suggestions. This makes it possible to suggest the optimal commentary mode based on the user's past viewing history and preferences.

[0037] The mode selection unit dynamically changes the mode selected by the user depending on the progress of the match, allowing it to provide optimal commentary. For example, the generation AI in the mode selection unit analyzes the progress of the match in real time and dynamically changes the mode selected by the user. For example, when the match is getting heated, the mode selection unit changes from a professional commentary mode to a more exciting mode. The mode selection unit also develops an algorithm for changing the mode depending on the progress of the match. For example, the generation AI analyzes the progress of the match and the user's reactions to select the optimal mode. The mode selection unit also builds a database for dynamically changing the mode. For example, the characteristics and content of each mode are collected and reflected in the changes. This allows it to provide optimal commentary depending on the progress of the match.

[0038] The mode selection unit can also provide commentary modes for different sports and events. For example, the generation AI in the mode selection unit provides commentary modes for different sports. For example, commentary modes corresponding to sports such as soccer, baseball, and tennis are prepared. The mode selection unit also provides commentary modes for different events. For example, commentary modes for concerts and plays are prepared. The mode selection unit also builds a database for providing commentary modes for different sports and events. For example, the characteristics and content of each sport or event are collected and reflected in the commentary. This makes it possible to provide commentary modes for different sports and events.

[0039] The mode selection unit can change not only the content of the commentary but also the way the video is displayed depending on the mode selected by the user. For example, the generation AI changes the way the video is displayed depending on the commentary mode selected by the user. For example, in a professional commentary mode, the display emphasizes tactical diagrams and player movements. The mode selection unit also develops algorithms for changing the way the video is displayed. For example, the generation AI analyzes the progress of the game and the user's reactions and selects the optimal display method. The mode selection unit also builds a database for changing the way the video is displayed. For example, the features and content of each mode are collected and reflected in the display. This makes it possible to change not only the content of the commentary but also the way the video is displayed.

[0040] The voice selection unit can learn the user's voice preferences and automatically suggest the most suitable voice. For example, the generation AI analyzes the user's past voice selection history and automatically suggests the most suitable voice. For example, a male voice is suggested for a user who has previously preferred male voices. The voice selection unit also develops an algorithm to learn the user's voice preferences. For example, the generation AI analyzes survey results and selection history to identify the user's preferences. The voice selection unit also builds a database to suggest the most suitable voice. For example, it collects the characteristics and content of each voice and reflects them in the suggestions. This makes it possible to suggest the most suitable voice based on the user's voice preferences.

[0041] The voice selection unit can provide different voice tones and accents, allowing the user to customize the voice in more detail. For example, the voice selection unit allows the generation AI to provide different voice tones and accents, allowing the user to customize the voice in more detail. For example, the voice selection unit provides a function to adjust the pitch and speed of the voice. The voice selection unit also develops algorithms for providing different voice tones and accents. For example, the generation AI generates different tones and accents using voice synthesis technology. The voice selection unit also builds a database for providing different voice tones and accents. For example, the characteristics and details of each tone and accent are collected and reflected in the customization. This allows the user to customize the voice in more detail.

[0042] The voice selection unit can also change the content and tone of the commentary depending on the voice selected by the user. For example, the generation AI changes the content and tone of the commentary depending on the voice selected by the user. For example, if a youthful voice is selected, the commentary will be in a lively and cheerful tone. The voice selection unit also develops an algorithm for changing the content and tone of the commentary. For example, the generation AI uses voice synthesis technology to generate different tones and content. The voice selection unit also builds a database for changing the content and tone of the commentary. For example, the characteristics and content of each voice are collected and reflected in the commentary. This makes it possible to change the content and tone of the commentary depending on the voice selected by the user.

[0043] The explanation generation unit can optimize the method of providing explanations according to the user's device and environment. For example, the generation AI in the explanation generation unit optimizes the method of providing explanations according to the user's device. For example, it provides a short explanation on a smartphone and a detailed explanation on a television. The explanation generation unit also optimizes the method of providing explanations according to the user's environment. For example, it provides a detailed explanation indoors and a concise explanation outdoors. The explanation generation unit also develops an algorithm for optimizing the method of providing explanations. For example, the generation AI analyzes information about the device and environment and selects the optimal method of providing explanations. The explanation generation unit also builds a database for optimizing the method of providing explanations. For example, it collects the characteristics and content of each device and environment and reflects them in the method of providing explanations. This makes it possible to optimize the method of providing explanations according to the user's device and environment.

[0044] The commentary generation unit can continuously improve the content of the commentary based on user feedback. For example, the generation AI of the commentary generation unit collects user feedback and continuously improves the content of the commentary based on that feedback. For example, if a user requests a detailed commentary, the commentary generation unit provides more detailed information in the next commentary. The commentary generation unit also develops an algorithm for analyzing user feedback. For example, the generation AI analyzes survey results and viewing history to identify the user's wishes. The commentary generation unit also builds a database for improving the content of the commentary. For example, the content of each feedback and points for improvement are collected and reflected in the commentary. This allows the content of the commentary to be continuously improved based on user feedback.

[0045] The commentary generation unit can provide commentary for different media. For example, the generation AI in the commentary generation unit provides commentary for radio. For example, it provides audio-only commentary on the progress of a match, informing radio listeners of the game's situation. The commentary generation unit also provides commentary for podcasts. For example, it provides audio of match highlights and player interviews. The commentary generation unit also develops algorithms for providing commentary for different media. For example, the generation AI analyzes the characteristics of the media and selects the optimal commentary method. The commentary generation unit also builds a database for providing commentary for different media. For example, it collects the characteristics and content of each media and reflects them in the commentary. This makes it possible to provide commentary for different media.

[0046] The commentary generation unit customizes the content of the commentary according to the user's preferences, thereby providing a personalized viewing experience. In the commentary generation unit, for example, a generation AI customizes the content of the commentary according to the user's preferences. For example, the commentary may focus on information about a specific player or team. The commentary generation unit also develops an algorithm for analyzing the user's preferences. For example, the generation AI analyzes viewing history and survey results to identify the user's preferences. The commentary generation unit also builds a database for customizing the content of the commentary. For example, the AI ​​collects each user's preferences and wishes and reflects them in the commentary. This allows the commentary content to be customized according to the user's preferences, thereby providing a personalized viewing experience.

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

[0048] The sports commentary system may further include a health monitoring unit that acquires the user's health data and adjusts the commentary content. For example, the system may monitor the user's heart rate and stress level and adjust the tone of the commentary to avoid overexcitement. The health monitoring unit may also analyze the user's exercise volume and provide commentary encouraging appropriate rest. Furthermore, the health monitoring unit may refer to the user's sleep data and provide commentary that takes fatigue into account. This allows the system to provide commentary that takes the user's health condition into consideration.

[0049] The sports commentary system may further include an advertisement serving unit that serves advertisements according to the user's preferences. For example, if a user is interested in sports goods, related advertisements may be displayed during the game. The advertisement serving unit may also analyze the user's past purchase history and select advertisements that will attract the user's interest. Furthermore, the advertisement serving unit may serve advertisements for related events or products based on the user's viewing history. This may provide advertisements according to the user's preferences, improving the viewing experience.

[0050] The sports commentary system may further include an advertisement serving unit that serves advertisements according to the user's preferences. For example, if a user is interested in sports goods, related advertisements may be displayed during the game. The advertisement serving unit may also analyze the user's past purchase history and select advertisements that will attract the user's interest. Furthermore, the advertisement serving unit may serve advertisements for related events or products based on the user's viewing history. This may provide advertisements according to the user's preferences, improving the viewing experience.

[0051] The sports commentary system may further include a health monitoring unit that acquires the user's health data and adjusts the commentary content. For example, the system may monitor the user's heart rate and stress level and adjust the tone of the commentary to avoid overexcitement. The health monitoring unit may also analyze the user's exercise volume and provide commentary encouraging appropriate rest. Furthermore, the health monitoring unit may refer to the user's sleep data and provide commentary that takes fatigue into account. This allows the system to provide commentary that takes the user's health condition into consideration.

[0052] The sports commentary system may further include an advertisement serving unit that serves advertisements according to the user's preferences. For example, if a user is interested in sports goods, related advertisements may be displayed during the game. The advertisement serving unit may also analyze the user's past purchase history and select advertisements that will attract the user's interest. Furthermore, the advertisement serving unit may serve advertisements for related events or products based on the user's viewing history. This may provide advertisements according to the user's preferences, improving the viewing experience.

[0053] The sports commentary system may further include a health monitoring unit that acquires the user's health data and adjusts the commentary content. For example, the system may monitor the user's heart rate and stress level and adjust the tone of the commentary to avoid overexcitement. The health monitoring unit may also analyze the user's exercise volume and provide commentary encouraging appropriate rest. Furthermore, the health monitoring unit may refer to the user's sleep data and provide commentary that takes fatigue into account. This allows the system to provide commentary that takes the user's health condition into consideration.

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

[0055] Step 1: The video analysis unit analyzes the footage of live sports broadcasts. For example, in a soccer game, the position of the ball and the movements of the players are analyzed to understand the progress of the game. In a baseball game, the unit analyzes the pitcher's throws and the batter's swing to understand the development of the game. Step 2: The commentary generation unit generates commentary based on the video data analyzed by the video analysis unit. For example, if a goal is scored in a soccer game, the commentary generation unit generates a commentary such as "What a great shot!". Similarly, if a home run is hit in a baseball game, the commentary generation unit generates a commentary such as "What a great home run!". Step 3: The mode selection unit adjusts the content of the commentary depending on the mode selected by the user. For example, there are a professional commentary mode, a commentary mode for beginners, a commentary mode with humor, etc. The professional commentary mode provides detailed commentary on tactics and techniques, while the commentary mode for beginners provides explanations of basic rules and plays. Step 4: The voice selection unit converts the commentary into audio according to the voice selected by the user. For example, there are male voices, female voices, youthful voices, calm voices, etc. The commentary is converted into audio according to the voice selected by the user and provided to the user.

[0056] (Example 2) The sports commentary system according to the embodiment of the present invention is a system in which a generation AI provides commentary in a mode and voice that matches the user's preferences in accordance with the video of a live sports broadcast. This allows the sports commentary system to provide commentary that matches the user's preferences.

[0057] A sports commentary system according to an embodiment includes a video analysis unit, a commentary generation unit, a mode selection unit, and a voice selection unit. The video analysis unit analyzes video of a live sports broadcast. For example, in a soccer game, the video analysis unit analyzes the position of the ball and the movements of the players to understand the progress of the game. In a baseball game, the video analysis unit analyzes the pitcher's pitch and the batter's swing to understand the development of the game. The commentary generation unit generates commentary based on the video data analyzed by the video analysis unit. For example, when a goal is scored in a soccer game, the commentary generation unit generates commentary such as "What a great shot!" In addition, when a home run is hit in a baseball game, the commentary generation unit generates commentary such as "What a great home run!" The mode selection unit adjusts the content of the commentary depending on the mode selected by the user. For example, there are an expert commentary mode, a beginner commentary mode, a humorous commentary mode, and the like. In the expert commentary mode, the mode selection unit provides detailed commentary on tactics and techniques, and in the beginner commentary mode, the mode selection unit provides explanations of basic rules and plays. The voice selection unit converts the commentary into audio depending on the voice selected by the user. For example, there are male voices, female voices, youthful voices, calm voices, etc. The voice selection unit converts the commentary into audio according to the voice selected by the user and provides it to the user. This allows the sports commentary system according to the embodiment to provide commentary according to the user's preferences. For example, the content and tone of the commentary can be adjusted according to the mode and voice selected by the user to improve the viewing experience.

[0058] The video analysis unit can infer emotions from players' facial expressions and body movements, and analyze the tension and excitement of a match based on those emotions. For example, the video analysis unit analyzes players' facial expressions in real time to infer emotions such as joy, anger, and tension. For example, the video analysis unit can detect the emotion of joy from the facial expression of a player who has scored a goal, and reflect that emotion in the analysis of the match. The video analysis unit also analyzes players' body movements to infer emotions. For example, if a player is nervous, it can detect that their movements become stiff and analyze the tension. The video analysis unit can also combine players' facial expressions and body movements to comprehensively analyze their emotions. For example, if a player has a happy expression and moves lightly, that emotion can be reflected in the analysis. This makes it possible to analyze the tension and excitement of a match.

[0059] The video analysis unit can identify abnormal plays and tactics by referring to past match data and comparing it with the progress of the current match. For example, the video analysis unit analyzes past match data and compares it with the progress of the current match. For example, if a specific player is moving in an unusual manner, the abnormality is detected. The video analysis unit also analyzes the tactics of the current match based on the past match data. For example, if a team is using an unusual formation, the tactic is identified. The video analysis unit also combines past match data with current match data to comprehensively identify abnormal plays and tactics. For example, if a player's movement or a team's tactics differ significantly from past matches, the abnormality is identified. This makes it possible to identify abnormal plays and tactics.

[0060] The video analysis unit analyzes the reactions of the spectators and can reflect the level of excitement and the strength of their cheering in the analysis of the match. The video analysis unit, for example, analyzes the facial expressions and movements of the spectators to estimate the level of excitement and the strength of their cheering. For example, in a scene where spectators are standing and cheering, it is determined that the level of excitement is high. The video analysis unit also analyzes the tone and volume of the spectators' voices to estimate the strength of their cheering. For example, if the spectators' voices are loud and have an excited tone, it is determined that the strength of their cheering is high. The video analysis unit also comprehensively analyzes the reactions of the spectators and reflects this in the analysis of the match. For example, it combines the facial expressions, movements, and tone of the spectators' voices to analyze the level of excitement and the strength of their cheering. This allows the reactions of the spectators to be reflected in the analysis of the match.

[0061] The video analysis unit can be expanded to analyze live broadcast footage of different sports. For example, the video analysis unit analyzes video of a tennis match to understand the position of the ball and the movements of the players. For example, it analyzes the speed and course of the serve to understand the progress of the match. The video analysis unit also analyzes video of a basketball match to understand the movements of the players and the position of the ball. For example, it analyzes the success rate of shots and the movements of the defense to understand the development of the match. The video analysis unit also analyzes video of a rugby match to understand the movements of the players and the position of the ball. For example, it analyzes the success rate of tries and the movements of tackles to understand the progress of the match. This allows the system to be expanded to analyze live broadcast footage of different sports.

[0062] The video analysis unit analyzes not only sports broadcast footage but also player interview footage and practice footage, allowing for comprehensive match analysis. The video analysis unit, for example, analyzes player interview footage to extract information about the player's psychological state and tactics. For example, it analyzes comments made in pre-match interviews to predict how the match will play out. The video analysis unit also analyzes practice footage to evaluate the player's performance and skills. For example, it analyzes movements during practice to evaluate the player's condition and improvement in skills. The video analysis unit also combines sports broadcast footage, interview footage, and practice footage to perform comprehensive match analysis. For example, it comprehensively analyzes comments made in pre-match interviews, performance during practice, and movements during the match to predict how the match will play out. This allows for comprehensive match analysis.

[0063] The video analysis unit uses the emotion estimation function to analyze the emotions of players in real time and provide commentary based on those emotions. For example, the video analysis unit analyzes the facial expressions of players in real time to estimate their emotions. For example, it analyzes the facial expression of joy of a player who has scored a goal and provides commentary based on that emotion. The video analysis unit also analyzes the tone and volume of a player's voice to estimate their emotions. For example, if a player speaks in an excited voice, it provides commentary based on that emotion. The video analysis unit also analyzes the player's biometric data (heart rate and electrodermal activity) to estimate their emotions. For example, if a player's heart rate is elevated, it provides commentary based on that emotion. This makes it possible to provide commentary based on the player's emotions.

[0064] The commentary generation unit dynamically changes the tone and content of the commentary according to the progress of the match, thereby reflecting the tension and excitement of the match. The commentary generation unit, for example, analyzes the progress of the match in real time and dynamically changes the tone of the commentary. For example, when the match is getting heated, the commentary is given in an excited tone. The commentary generation unit also dynamically changes the content of the commentary according to the progress of the match. For example, when the match is getting tense, detailed technical commentary is given. The commentary generation unit also comprehensively changes the tone and content of the commentary according to the progress of the match. For example, when the match is getting exciting, detailed technical commentary is given in an excited tone. This makes it possible to provide commentary that reflects the tension and excitement of the match.

[0065] The commentary generation unit can provide commentary by referring to a player's past performance data and comparing it with a current play. The commentary generation unit, for example, analyzes a player's past performance data and compares it with a current play. For example, it compares a player's successful plays in the past with his current play and explains the differences. The commentary generation unit also evaluates a current play based on a player's past performance data. For example, it compares a player's successful plays in the past with his current play and evaluates the success rate of the current play. The commentary generation unit also comprehensively analyzes a player's past performance data and his current play and provides commentary. For example, it compares a player's successful plays in the past with his current play and explains the differences in detail. This makes it possible to provide commentary by comparing past performance data with current play.

[0066] The commentary generation unit uses the emotion estimation function to generate commentary according to the user's emotions, thereby maintaining the user's interest. The commentary generation unit, for example, analyzes the user's emotions in real time and generates commentary according to the emotions. For example, if the user is excited, the commentary generation unit provides commentary that increases the user's excitement. The commentary generation unit also adjusts the content of the commentary based on the user's emotions. For example, if the user is excited, the commentary generation unit provides detailed technical commentary. The commentary generation unit also adjusts the tone of the commentary based on the user's emotions. For example, if the user is excited, the commentary is provided in an excited tone. This allows the user to generate commentary according to the user's emotions and maintain interest.

[0067] The commentary generation unit can automatically select highlight scenes of a match and generate special commentary for those scenes. For example, the generation AI automatically selects highlight scenes of a match and generates special commentary for those scenes. For example, detailed commentary is provided for goal scenes and important plays. The commentary generation unit also develops an algorithm for selecting highlight scenes of a match. For example, the generation AI analyzes the progress of the match and the movements of players to identify important scenes. The commentary generation unit also builds a database for generating special commentary for highlight scenes of a match. For example, detailed technical explanations for each scene and background information on players are collected and reflected in the commentary. This makes it possible to provide special commentary for highlight scenes of a match.

[0068] The commentary generation unit can use the emotion estimation function to provide commentary that will heighten the user's emotions at important moments in a match. For example, the generation AI uses the emotion estimation function to provide commentary that will heighten the user's emotions at important moments in a match. For example, commentary in an excited tone is provided for goal scenes and important plays. The commentary generation unit also develops an algorithm for identifying important moments in a match. For example, the generation AI analyzes the progress of the match and the movements of players to identify important scenes. The commentary generation unit also builds a database for generating commentary that will heighten the user's emotions. For example, detailed technical explanations for each scene and background information on players are collected and reflected in the commentary. This makes it possible to provide commentary that will heighten the user's emotions at important moments in a match.

[0069] The mode selection unit can learn the user's past viewing history and preferences and automatically suggest the optimal commentary mode. For example, the mode selection unit uses a generation AI to analyze the user's past viewing history and automatically suggest the optimal commentary mode. For example, a specialized commentary mode is suggested for a user who has previously preferred to watch specialized commentaries. The mode selection unit also develops an algorithm to learn the user's preferences. For example, the generation AI analyzes viewing history and survey results to identify the user's preferences. The mode selection unit also builds a database to suggest the optimal commentary mode. For example, the features and content of each mode are collected and reflected in the suggestions. This makes it possible to suggest the optimal commentary mode based on the user's past viewing history and preferences.

[0070] The mode selection unit dynamically changes the mode selected by the user depending on the progress of the match, allowing it to provide optimal commentary. For example, the generation AI in the mode selection unit analyzes the progress of the match in real time and dynamically changes the mode selected by the user. For example, when the match is getting heated, the mode selection unit changes from a professional commentary mode to a more exciting mode. The mode selection unit also develops an algorithm for changing the mode depending on the progress of the match. For example, the generation AI analyzes the progress of the match and the user's reactions to select the optimal mode. The mode selection unit also builds a database for dynamically changing the mode. For example, the characteristics and content of each mode are collected and reflected in the changes. This allows it to provide optimal commentary depending on the progress of the match.

[0071] The mode selection unit can use the emotion estimation function to suggest a commentary mode that corresponds to the user's emotion, improving the viewing experience. For example, the mode selection unit uses a generation AI to analyze the user's emotion in real time and suggest a commentary mode that corresponds to that emotion. For example, if the user is excited, the mode selection unit suggests a commentary mode that increases the excitement. The mode selection unit also develops an algorithm for analyzing the user's emotion. For example, the generation AI estimates the user's emotion using face recognition technology or voice analysis technology. The mode selection unit also builds a database for suggesting commentary modes that correspond to the emotion. For example, the mode selection unit collects the features and content of each mode and reflects them in the suggestions. This makes it possible to suggest commentary modes that correspond to the user's emotion, improving the viewing experience.

[0072] The mode selection unit can also provide commentary modes for different sports and events. For example, the generation AI in the mode selection unit provides commentary modes for different sports. For example, commentary modes corresponding to sports such as soccer, baseball, and tennis are prepared. The mode selection unit also provides commentary modes for different events. For example, commentary modes for concerts and plays are prepared. The mode selection unit also builds a database for providing commentary modes for different sports and events. For example, the characteristics and content of each sport or event are collected and reflected in the commentary. This makes it possible to provide commentary modes for different sports and events.

[0073] The mode selection unit can change not only the content of the commentary but also the way the video is displayed depending on the mode selected by the user. For example, the generation AI changes the way the video is displayed depending on the commentary mode selected by the user. For example, in a professional commentary mode, the display emphasizes tactical diagrams and player movements. The mode selection unit also develops algorithms for changing the way the video is displayed. For example, the generation AI analyzes the progress of the game and the user's reactions and selects the optimal display method. The mode selection unit also builds a database for changing the way the video is displayed. For example, the features and content of each mode are collected and reflected in the display. This makes it possible to change not only the content of the commentary but also the way the video is displayed.

[0074] The mode selection unit uses the emotion estimation function to change the commentary mode in real time according to the user's emotions, thereby providing an optimal viewing experience. For example, the mode selection unit uses a generation AI to analyze the user's emotions in real time and dynamically change the commentary mode according to those emotions. For example, if the user is excited, the commentary mode is changed to one that increases excitement. The mode selection unit also develops an algorithm for analyzing the user's emotions. For example, the generation AI estimates the user's emotions using facial recognition technology or voice analysis technology. The mode selection unit also builds a database for changing the commentary mode in real time according to emotions. For example, the features and content of each mode are collected and reflected in the changes. This allows the commentary mode to be changed in real time according to the user's emotions, thereby providing an optimal viewing experience.

[0075] The voice selection unit can learn the user's voice preferences and automatically suggest the most suitable voice. For example, the generation AI analyzes the user's past voice selection history and automatically suggests the most suitable voice. For example, a male voice is suggested for a user who has previously preferred male voices. The voice selection unit also develops an algorithm to learn the user's voice preferences. For example, the generation AI analyzes survey results and selection history to identify the user's preferences. The voice selection unit also builds a database to suggest the most suitable voice. For example, it collects the characteristics and content of each voice and reflects them in the suggestions. This makes it possible to suggest the most suitable voice based on the user's voice preferences.

[0076] The voice selection unit can provide different voice tones and accents, allowing the user to customize the voice in more detail. For example, the voice selection unit allows the generation AI to provide different voice tones and accents, allowing the user to customize the voice in more detail. For example, the voice selection unit provides a function to adjust the pitch and speed of the voice. The voice selection unit also develops algorithms for providing different voice tones and accents. For example, the generation AI generates different tones and accents using voice synthesis technology. The voice selection unit also builds a database for providing different voice tones and accents. For example, the characteristics and details of each tone and accent are collected and reflected in the customization. This allows the user to customize the voice in more detail.

[0077] The voice selection unit uses an emotion estimation function to suggest voices that correspond to the user's emotions, improving the viewing experience. For example, the voice selection unit uses a generation AI to analyze the user's emotions in real time and suggest voices that correspond to those emotions. For example, if the user is excited, the voice selection unit suggests voices that increase the excitement. The voice selection unit also develops algorithms to analyze the user's emotions. For example, the generation AI uses facial recognition technology and voice analysis technology to estimate the user's emotions. The voice selection unit also builds a database to suggest voices that correspond to emotions. For example, it collects the characteristics and content of each voice and reflects them in the suggestions. This makes it possible to suggest voices that correspond to the user's emotions, improving the viewing experience.

[0078] The voice selection unit can also change the content and tone of the commentary depending on the voice selected by the user. For example, the generation AI changes the content and tone of the commentary depending on the voice selected by the user. For example, if a youthful voice is selected, the commentary will be in a lively and cheerful tone. The voice selection unit also develops an algorithm for changing the content and tone of the commentary. For example, the generation AI uses voice synthesis technology to generate different tones and content. The voice selection unit also builds a database for changing the content and tone of the commentary. For example, the characteristics and content of each voice are collected and reflected in the commentary. This makes it possible to change the content and tone of the commentary depending on the voice selected by the user.

[0079] The voice selection unit uses the emotion estimation function to change the voice in real time according to the user's emotions, thereby providing an optimal viewing experience. For example, the voice selection unit uses a generation AI to analyze the user's emotions in real time and dynamically change the voice according to those emotions. For example, if the user is excited, the voice selection unit changes the voice to one that increases excitement. The voice selection unit also develops an algorithm to analyze the user's emotions. For example, the generation AI uses facial recognition technology and voice analysis technology to estimate the user's emotions. The voice selection unit also builds a database to change the voice according to emotions in real time. For example, it collects the characteristics and content of each voice and reflects them in the changes. This allows the voice to be changed in real time according to the user's emotions, thereby providing an optimal viewing experience.

[0080] The explanation generation unit can optimize the method of providing explanations according to the user's device and environment. For example, the generation AI in the explanation generation unit optimizes the method of providing explanations according to the user's device. For example, it provides a short explanation on a smartphone and a detailed explanation on a television. The explanation generation unit also optimizes the method of providing explanations according to the user's environment. For example, it provides a detailed explanation indoors and a concise explanation outdoors. The explanation generation unit also develops an algorithm for optimizing the method of providing explanations. For example, the generation AI analyzes information about the device and environment and selects the optimal method of providing explanations. The explanation generation unit also builds a database for optimizing the method of providing explanations. For example, it collects the characteristics and content of each device and environment and reflects them in the method of providing explanations. This makes it possible to optimize the method of providing explanations according to the user's device and environment.

[0081] The commentary generation unit can continuously improve the content of the commentary based on user feedback. For example, the generation AI of the commentary generation unit collects user feedback and continuously improves the content of the commentary based on that feedback. For example, if a user requests a detailed commentary, the commentary generation unit provides more detailed information in the next commentary. The commentary generation unit also develops an algorithm for analyzing user feedback. For example, the generation AI analyzes survey results and viewing history to identify the user's wishes. The commentary generation unit also builds a database for improving the content of the commentary. For example, the content of each feedback and points for improvement are collected and reflected in the commentary. This allows the content of the commentary to be continuously improved based on user feedback.

[0082] The commentary generation unit uses the emotion estimation function to provide commentary that corresponds to the user's emotions, thereby improving the viewing experience. For example, the commentary generation unit uses a generation AI to analyze the user's emotions in real time and provide commentary that corresponds to those emotions. For example, if the user is excited, the commentary generation unit provides commentary that increases the user's excitement. The commentary generation unit also develops an algorithm for analyzing the user's emotions. For example, the generation AI estimates the user's emotions using facial recognition technology or voice analysis technology. The commentary generation unit also builds a database for providing commentary that corresponds to emotions. For example, the content and tone of the commentary that corresponds to each emotion are collected and reflected in the commentary. This makes it possible to provide commentary that corresponds to the user's emotions, thereby improving the viewing experience.

[0083] The commentary generation unit can provide commentary for different media. For example, the generation AI in the commentary generation unit provides commentary for radio. For example, it provides audio-only commentary on the progress of a match, informing radio listeners of the game's situation. The commentary generation unit also provides commentary for podcasts. For example, it provides audio of match highlights and player interviews. The commentary generation unit also develops algorithms for providing commentary for different media. For example, the generation AI analyzes the characteristics of the media and selects the optimal commentary method. The commentary generation unit also builds a database for providing commentary for different media. For example, it collects the characteristics and content of each media and reflects them in the commentary. This makes it possible to provide commentary for different media.

[0084] The commentary generation unit customizes the content of the commentary according to the user's preferences, thereby providing a personalized viewing experience. In the commentary generation unit, for example, a generation AI customizes the content of the commentary according to the user's preferences. For example, the commentary may focus on information about a specific player or team. The commentary generation unit also develops an algorithm for analyzing the user's preferences. For example, the generation AI analyzes viewing history and survey results to identify the user's preferences. The commentary generation unit also builds a database for customizing the content of the commentary. For example, the AI ​​collects each user's preferences and wishes and reflects them in the commentary. This allows the commentary content to be customized according to the user's preferences, thereby providing a personalized viewing experience.

[0085] The commentary generation unit uses the emotion estimation function to change the commentary in real time according to the user's emotions, thereby providing an optimal viewing experience. For example, the commentary generation unit uses a generation AI to analyze the user's emotions in real time and dynamically change the commentary according to those emotions. For example, if the user is excited, the commentary is changed to one that increases the user's excitement. The commentary generation unit also develops an algorithm for analyzing the user's emotions. For example, the generation AI estimates the user's emotions using facial recognition technology or voice analysis technology. The commentary generation unit also builds a database for changing the commentary in real time according to emotions. For example, the content and tone of the commentary corresponding to each emotion are collected and reflected in the commentary. This allows the commentary to be changed in real time according to the user's emotions, thereby providing an optimal viewing experience.

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

[0087] The sports commentary system may further include a health monitoring unit that acquires the user's health data and adjusts the commentary content. For example, the system may monitor the user's heart rate and stress level and adjust the tone of the commentary to avoid overexcitement. The health monitoring unit may also analyze the user's exercise volume and provide commentary encouraging appropriate rest. Furthermore, the health monitoring unit may refer to the user's sleep data and provide commentary that takes fatigue into account. This allows the system to provide commentary that takes the user's health condition into consideration.

[0088] The sports commentary system may further include an emotion feedback unit that estimates the user's emotion and adjusts the content of the commentary based on the estimated emotion. For example, if the user is excited, the tone of the commentary may be made more lively. If the user is bored, the commentary may be provided with a humorous touch. Furthermore, if the user is nervous, the emotion feedback unit may provide commentary that relaxes the user. This allows the commentary to be provided in accordance with the user's emotion, improving the viewing experience.

[0089] The sports commentary system may further include an advertisement serving unit that serves advertisements according to the user's preferences. For example, if a user is interested in sports goods, related advertisements may be displayed during the game. The advertisement serving unit may also analyze the user's past purchase history and select advertisements that will attract the user's interest. Furthermore, the advertisement serving unit may serve advertisements for related events or products based on the user's viewing history. This may provide advertisements according to the user's preferences, improving the viewing experience.

[0090] The sports commentary system may further include an emotion feedback unit that estimates the user's emotion and adjusts the content of the commentary based on the estimated emotion. For example, if the user is excited, the tone of the commentary may be made more lively. If the user is bored, the commentary may be provided with a humorous touch. Furthermore, if the user is nervous, the emotion feedback unit may provide commentary that relaxes the user. This allows the commentary to be provided in accordance with the user's emotion, improving the viewing experience.

[0091] The sports commentary system may further include an emotion feedback unit that estimates the user's emotion and adjusts the content of the commentary based on the estimated emotion. For example, if the user is excited, the tone of the commentary may be made more lively. If the user is bored, the commentary may be provided with a humorous touch. Furthermore, if the user is nervous, the emotion feedback unit may provide commentary that relaxes the user. This allows the commentary to be provided in accordance with the user's emotion, improving the viewing experience.

[0092] The sports commentary system may further include an emotion feedback unit that estimates the user's emotion and adjusts the content of the commentary based on the estimated emotion. For example, if the user is excited, the tone of the commentary may be made more lively. If the user is bored, the commentary may be provided with a humorous touch. Furthermore, if the user is nervous, the emotion feedback unit may provide commentary that relaxes the user. This allows the commentary to be provided in accordance with the user's emotion, improving the viewing experience.

[0093] The sports commentary system may further include an advertisement serving unit that serves advertisements according to the user's preferences. For example, if a user is interested in sports goods, related advertisements may be displayed during the game. The advertisement serving unit may also analyze the user's past purchase history and select advertisements that will attract the user's interest. Furthermore, the advertisement serving unit may serve advertisements for related events or products based on the user's viewing history. This may provide advertisements according to the user's preferences, improving the viewing experience.

[0094] The sports commentary system may further include a health monitoring unit that acquires the user's health data and adjusts the commentary content. For example, the system may monitor the user's heart rate and stress level and adjust the tone of the commentary to avoid overexcitement. The health monitoring unit may also analyze the user's exercise volume and provide commentary encouraging appropriate rest. Furthermore, the health monitoring unit may refer to the user's sleep data and provide commentary that takes fatigue into account. This allows the system to provide commentary that takes the user's health condition into consideration.

[0095] The sports commentary system may further include an advertisement serving unit that serves advertisements according to the user's preferences. For example, if a user is interested in sports goods, related advertisements may be displayed during the game. The advertisement serving unit may also analyze the user's past purchase history and select advertisements that will attract the user's interest. Furthermore, the advertisement serving unit may serve advertisements for related events or products based on the user's viewing history. This may provide advertisements according to the user's preferences, improving the viewing experience.

[0096] The sports commentary system may further include a health monitoring unit that acquires the user's health data and adjusts the commentary content. For example, the system may monitor the user's heart rate and stress level and adjust the tone of the commentary to avoid overexcitement. The health monitoring unit may also analyze the user's exercise volume and provide commentary encouraging appropriate rest. Furthermore, the health monitoring unit may refer to the user's sleep data and provide commentary that takes fatigue into account. This allows the system to provide commentary that takes the user's health condition into consideration.

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

[0098] Step 1: The video analysis unit analyzes the footage of live sports broadcasts. For example, in a soccer game, the position of the ball and the movements of the players are analyzed to understand the progress of the game. In a baseball game, the unit analyzes the pitcher's throws and the batter's swing to understand the development of the game. Step 2: The commentary generation unit generates commentary based on the video data analyzed by the video analysis unit. For example, if a goal is scored in a soccer game, the commentary generation unit generates a commentary such as "What a great shot!". Similarly, if a home run is hit in a baseball game, the commentary generation unit generates a commentary such as "What a great home run!". Step 3: The mode selection unit adjusts the content of the commentary depending on the mode selected by the user. For example, there are a professional commentary mode, a commentary mode for beginners, a commentary mode with humor, etc. The professional commentary mode provides detailed commentary on tactics and techniques, while the commentary mode for beginners provides explanations of basic rules and plays. Step 4: The voice selection unit converts the commentary into audio according to the voice selected by the user. For example, there are male voices, female voices, youthful voices, calm voices, etc. The commentary is converted into audio according to the voice selected by the user and provided to the user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A video analysis section that analyzes sports broadcast footage, a comment generation unit that generates a comment based on the video data analyzed by the video analysis unit; a mode selection unit that adjusts the content of the commentary according to a mode selected by a user; a voice selection unit that converts the commentary into voice in accordance with a voice selected by the user; A system characterized by:

2. The video analysis unit Estimating emotions from players' facial expressions and body movements, and analyzing the tension and excitement of the match based on those emotions 2. The system of claim 1.

3. The video analysis unit Expanding the system to analyze live footage of other sports 2. The system of claim 1.

4. The explanation generation unit Dynamically change the tone and content of the commentary as the match progresses to reflect the tension and excitement of the match.

2. The system of claim 1.

5. The mode selection unit Learns the user's past viewing history and preferences and automatically suggests the most suitable commentary mode 2. The system of claim 1.

6. The voice selection unit Learns the user's voice preferences and automatically suggests the best voice 2. The system of claim 1.

7. The video analysis unit Analyzing the reactions of the spectators and reflecting their excitement and the strength of their cheering in the analysis of the match 2. The system of claim 1.

8. The explanation generation unit A commentary is generated according to the user's emotions, and the user's interest is maintained.

2. The system of claim 1.

Images