system

Abstract

The system according to this embodiment aims to analyze match data in real time and immediately provide highlights and statistical data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an output unit. The collection unit collects match data related to matches currently being held. The analysis unit analyzes the match data collected by the collection unit. The generation unit generates real-time information, including match highlights, playback, or statistical data, based on the analysis results by the analysis unit. The output unit outputs the real-time information generated by the generation unit as video.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, data during a game has not been sufficiently analyzed in real time to immediately provide highlights and statistical data, and there is room for improvement. <000​​​​​​​​The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an output unit. The collection unit collects match data related to ongoing matches. The analysis unit analyzes the match data collected by the collection unit. The generation unit generates real-time information, including match highlights, playback, or statistical data, based on the analysis results from the analysis unit. The output unit outputs the real-time information generated by the generation unit as video. [Effects of the Invention]

[0007] The system according to this embodiment can analyze match data in real time and immediately provide highlights and statistical data. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​assistant for sports commentary according to an embodiment of the present invention is a system that has the functions of analyzing match data in real time and extracting highlights, supporting commentary based on the past performance and statistical data of athletes, and providing detailed information and past playbacks requested by the user. The AI ​​assistant for sports commentary has the functions of analyzing match data in real time and extracting highlights, supporting commentary based on the past performance and statistical data of athletes, and providing detailed information and past playbacks requested by the user. For example, the AI ​​assistant for sports commentary collects match data related to a match currently being held. The collection unit is equipped with sensors that collect player position data and ball movement data, thereby enabling the acquisition of detailed match data in real time. For example, in a soccer match, the sensors track the positions of players and the movement of the ball and collect data. Next, the AI ​​assistant for sports commentary analyzes the collected match data. The analysis unit analyzes the collected data and generates real-time information including match highlights, playbacks, or statistical data. For example, it analyzes important goal scenes and player performance data and extracts them as highlights. The generation unit generates real-time information based on the analysis results. The generated information is output as commentary text or audio of the match. For example, it can generate commentary text for a match based on the performance and statistical data of the athletes in the match, and output it as audio. Furthermore, the AI ​​assistant for sports commentary support is equipped with a reception unit that receives requests from users and can provide the detailed information and past playbacks that the user requests. The reception unit receives requests via voice input or text input, and the output unit outputs video corresponding to the request. For example, if a user requests past plays of a specific player, the playback video can be output. In addition, the generation unit can estimate the emotions of the athletes in a match and generate real-time information based on the estimated emotions. For example, it can estimate the emotion of joy when a player scores a goal and provide commentary based on that emotion. The collection unit can analyze the patterns of past movements of players and select the optimal collection method.For example, it can analyze a player's past movements and collect data focusing on specific plays. Furthermore, the data collection unit can filter the data to focus on specific players or plays when collecting match data. For example, it can collect data focusing on the movements of specific players or important plays. The data collection unit can estimate the emotions of the user, who is watching the video, and determine the priority of data to collect based on the estimated user emotions. For example, it can prioritize collecting scenes where the viewer is excited. Finally, when collecting match data, the data collection unit can prioritize collecting highly relevant data based on audience reaction data. For example, it can prioritize collecting scenes where the audience is excited. As a result, the AI ​​assistant supporting sports commentary can consistently handle everything from collecting, analyzing, generating, and outputting match data.

[0029] The AI ​​assistant for providing commentary on sports competitions according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and an output unit. The data collection unit collects match data related to a match in progress. The data collection unit includes, for example, sensors that collect player position data and ball movement data. The data collection unit can, for example, collect player position data using a GPS sensor. The data collection unit can also collect ball movement data using an acceleration sensor. The data collection unit can, for example, collect player position data in real time and understand the progress of the match. The data collection unit can, for example, track the movement of the ball and identify important plays. The analysis unit analyzes the match data collected by the data collection unit. The analysis unit can, for example, perform statistical analysis of the data. The analysis unit can, for example, analyze player performance data and extract match highlights. The analysis unit can, for example, use pattern recognition technology to identify important scenes in the match. The analysis unit can, for example, analyze player movement patterns and perform tactical analysis. The generation unit generates real-time information, including match highlights, playback, or statistical data, based on the analysis results from the analysis unit. The generation unit can, for example, generate commentary text for the match. The generation unit can, for example, generate commentary audio for the match. The generation unit can, for example, generate highlight videos for the match. The generation unit can, for example, generate graphs based on statistical data for the match. The generation unit can, for example, generate text summarizing important scenes of the match. The output unit outputs the real-time information generated by the generation unit as video. The output unit can, for example, output commentary text for the match as audio. The output unit can, for example, display highlight videos for the match. The output unit can, for example, display statistical data for the match as a graph. The output unit can, for example, output commentary audio for the match from a speaker. The output unit can, for example, display text summarizing important scenes of the match. As a result, the sports competition commentary support AI assistant according to the embodiment can consistently perform everything from collecting and analyzing match data to generating and outputting it.

[0030] The data collection unit collects match data related to ongoing matches. The unit is equipped with sensors to collect, for example, player position data and ball movement. Specifically, the unit can collect player position data using GPS sensors. This makes it possible to understand players' movement paths and positioning in real time. The unit can also collect ball movement using acceleration sensors. Acceleration sensors detect detailed movements such as ball speed, direction, and rotation, accurately capturing the dynamics of the match. For example, the unit can collect player position data in real time to understand the progress of the match. This allows for immediate tracking of player movements and tactical changes, and detailed recording of the match flow. Furthermore, the unit can track ball movement and identify important plays. For example, it can detect goal scenes, decisive passes, and shots in real time, which can be used for subsequent analysis and generation. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the match data collected by the data collection unit. For example, the analysis unit can perform statistical analysis of the data. Specifically, it can analyze player performance data and extract match highlights. For instance, it can analyze data such as player distance covered, number of sprints, and pass success rate to evaluate player performance. The analysis unit can use pattern recognition technology to identify important scenes in a match. For example, it can identify goal scenes, decisive passes, and shots to understand the flow of the game. Furthermore, the analysis unit can analyze player movement patterns and perform tactical analysis. For example, it can analyze player positioning and movement patterns to evaluate team tactics and strategies. Based on these analysis results, the analysis unit can extract match highlights and important scenes and provide them to the subsequent generation unit. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term performance evaluations and trend analyses. For example, based on past match data, it can evaluate player growth and performance fluctuations to plan future tactics and training. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term performance evaluation and tactical analysis, thereby improving the reliability and usefulness of the entire system.

[0032] The generation unit generates real-time information, including match highlights, playbacks, or statistical data, based on the analysis results from the analysis unit. Specifically, it can generate commentary text for matches. For example, it can automatically generate text explaining player performance and the flow of the game and provide it to viewers. The generation unit can also generate commentary audio for matches. For example, it can use AI to provide natural-sounding commentary on the match and provide it to viewers in real time. Furthermore, the generation unit can generate match highlight videos. For example, it can automatically edit important scenes and plays and provide them to viewers. The generation unit can also generate graphs based on match statistical data. For example, it can visually display player performance data and match statistics and provide them to viewers. The generation unit can also generate text summarizing important scenes from the match. For example, it can generate text that concisely summarizes the flow of the game and important plays and provide it to viewers. In this way, the generation unit can generate and provide viewers with information in various formats based on the analysis results. Furthermore, the generation unit can update the generated information in real time to respond to the latest match situation. For example, it can update commentary text and highlight videos according to the progress of the match to provide viewers with the latest information. This allows the generation unit to always provide viewers with the latest information and deepen their understanding of the match.

[0033] The output unit outputs real-time information generated by the generation unit as video. Specifically, it can output commentary text of the match as audio. For example, commentary text generated by AI can be converted into natural-sounding audio using speech synthesis technology and provided to viewers. The output unit can also display highlight videos of the match. For example, it can display edited highlight videos of important scenes and plays in real time and provide them to viewers. The output unit can display statistical data of the match as graphs. For example, it can visually display player performance data and match statistics and provide them to viewers. The output unit can also output commentary audio of the match from speakers. For example, it can play commentary audio generated by AI from speakers and provide it to viewers. The output unit can display text summarizing important scenes of the match. For example, it can display text in real time that concisely summarizes the flow of the match and important plays and provide it to viewers. In this way, the output unit can provide generated information to viewers in various formats, deepening their understanding of the match. Furthermore, the output unit can collect viewer feedback and continuously improve the accuracy and effectiveness of the output content. For example, based on viewer feedback, the commentary content and display format can be reviewed to provide clearer and more engaging information. This allows the output unit to deliver information quickly and reliably to viewers, deepening their understanding of the match.

[0034] The data collection unit includes sensors that collect position data of players in a match or the movement of the ball. The data collection unit can, for example, collect player position data using a GPS sensor. The data collection unit can also, for example, collect the movement of the ball using an accelerometer. The data collection unit can, for example, collect player position data in real time and understand the progress of the match. The data collection unit can, for example, track the movement of the ball and identify important plays. This allows for the collection of detailed match data in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input player position data into an AI, which can analyze the data to identify important plays.

[0035] The generation unit generates commentary text for the match based on the performance or statistical data of the players in the match, and the output unit outputs the text as audio. The generation unit can generate commentary text based on performance data such as players' goals and assists, for example. The generation unit can also generate commentary text based on players' success rates and performance data, for example. The generation unit can generate statistical data based on past match data and generate commentary text based on that. The output unit can output the generated commentary text as audio, for example. The output unit can also output the generated commentary text as audio from a speaker, for example. The output unit can also display the generated commentary text together with video, for example. This makes it possible to provide real-time audio commentary on the match. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input player performance data into a generation AI, and the generation AI can generate commentary text.

[0036] The reception unit receives requests from users, and the output unit outputs video corresponding to the requests received by the reception unit. The reception unit can receive requests, for example, via voice input. The reception unit can also receive requests, for example, via text input. The reception unit can output playback video, for example, if a user requests past plays of a specific player. The output unit can display video corresponding to the request. The output unit can also output the video corresponding to the request as audio from a speaker. The output unit can also display the video corresponding to the request along with the video itself. This allows the system to provide video corresponding to the user's request. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input voice input to the AI, which can analyze the request and select appropriate video.

[0037] The reception desk accepts requests via voice input and text input. The reception desk can accept voice input using, for example, speech recognition technology. The reception desk can also accept voice input using, for example, a microphone. The reception desk can accept text input using, for example, keyboard input. The reception desk can also accept text input using, for example, a touchscreen. This allows users to make requests by voice or text. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input voice input into AI, which can analyze the voice to identify the request.

[0038] The data collection unit analyzes the player's past movement patterns and selects an appropriate data collection method. For example, the data collection unit can analyze past match data to identify the player's movement patterns. For example, the data collection unit can analyze training data to identify the player's movement patterns. For example, the data collection unit can determine the optimal sensor placement based on the player's movement patterns. For example, the data collection unit can adjust the timing of data collection based on the player's movement patterns. This enables optimal data collection based on the player's past movements. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past match data into AI, which can analyze movement patterns and select the optimal data collection method.

[0039] The data collection unit filters the collected match data, focusing on specific players or plays. For example, the data collection unit can prioritize the collection of data on the movements of specific players. For example, the data collection unit can prioritize the collection of data on important plays. For example, the data collection unit can track the movements of specific players in real time and collect data. For example, the data collection unit can prioritize the collection of data on important plays. This makes it possible to collect data that focuses on specific players or plays. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the movements of specific players into the AI, and the AI ​​can analyze the data to identify important plays.

[0040] The data collection unit prioritizes collecting highly relevant data based on audience reaction data when collecting match data. For example, the data collection unit can analyze audience applause and cheers to identify highly relevant scenes. For example, the data collection unit can analyze audience facial expressions to identify exciting scenes. For example, the data collection unit can prioritize collecting important scenes based on audience reaction data. For example, the data collection unit can analyze audience reaction data in real time to collect highly relevant data. This allows for the priority collection of highly relevant data based on audience reactions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input audience reaction data into AI, which can analyze the data to identify highly relevant scenes.

[0041] The analysis unit improves the accuracy of the analysis based on the player's past performance and statistical data. For example, the analysis unit can improve the accuracy of the analysis based on performance data such as the player's goals and assists. For example, the analysis unit can also improve the accuracy of the analysis based on the player's success rate and performance data. For example, the analysis unit can generate statistical data based on past match data and use that to improve the accuracy of the analysis. This allows the accuracy of the analysis to be improved based on the player's past performance and statistical data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the player's performance data into AI, and the AI ​​can analyze the data to improve the accuracy of the analysis.

[0042] The analysis unit applies an appropriate analysis algorithm according to the situation of the match. The analysis unit can, for example, analyze the situation of the match using a machine learning algorithm. The analysis unit can, for example, analyze the situation of the match using a statistical analysis algorithm. The analysis unit can, for example, apply different analysis algorithms depending on the situation of the match. The analysis unit can, for example, select the optimal analysis algorithm according to the situation of the match. This allows the optimal analysis algorithm to be applied according to the situation of the match. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input match situation data into AI, and the AI ​​can select the optimal analysis algorithm.

[0043] The analysis unit determines the priority of analysis based on the position data of the players in the match and the movement of the ball. The analysis unit can, for example, collect player position data using GPS data and determine the priority of analysis. The analysis unit can, for example, analyze the movement of the ball using tracking data and determine the priority of analysis. The analysis unit can, for example, prioritize the analysis of important scenes based on the player position data. The analysis unit can, for example, identify important scenes of the match based on the movement of the ball and prioritize their analysis. This allows the analysis priority to be determined based on the position data of the players and the movement of the ball. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input player position data into AI, and the AI ​​can analyze the data and determine the priority of analysis.

[0044] The analysis unit identifies important scenes in a match and performs a detailed analysis. For example, the analysis unit can identify scoring opportunities and perform a detailed analysis. For example, the analysis unit can identify decisive plays and perform a detailed analysis. For example, the analysis unit can identify important scenes in a match in real time and perform a detailed analysis. For example, the analysis unit can perform a detailed analysis based on data from important scenes. This allows for the identification and detailed analysis of important scenes in a match. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match data into AI, which can then identify important scenes and perform a detailed analysis.

[0045] The generation unit generates explanatory text based on the performance and statistical data of the players in a match. For example, the generation unit can generate explanatory text based on performance data such as goals scored and assists by players. For example, the generation unit can also generate explanatory text based on players' success rates and performance data. For example, the generation unit can generate statistical data based on past match data and generate explanatory text based on that. This makes it possible to generate explanatory text based on the performance and statistical data of the players. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input player performance data into a generation AI, and the generation AI can generate explanatory text.

[0046] The generation unit applies an appropriate generation algorithm according to the match situation. For example, the generation unit can analyze the match situation using a machine learning algorithm and apply an appropriate generation algorithm. For example, the generation unit can analyze the match situation using a statistical analysis algorithm and apply an appropriate generation algorithm. For example, the generation unit can apply different generation algorithms depending on the match situation. For example, the generation unit can select the optimal generation algorithm according to the match situation. This allows the optimal generation algorithm to be applied according to the match situation. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input match situation data into a generation AI, and the generation AI can select the optimal generation algorithm.

[0047] The generation unit adjusts the generation method based on the position data of the players in the match and the movement of the ball. The generation unit can, for example, collect player position data using GPS data and adjust the generation method. The generation unit can, for example, analyze the movement of the ball using tracking data and adjust the generation method. The generation unit can, for example, prioritize the generation of important scenes based on the player position data. The generation unit can, for example, identify important scenes of the match based on the movement of the ball and prioritize their generation. This allows the generation method to be adjusted based on the player position data and the movement of the ball. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input player position data into a generation AI, and the generation AI can analyze the data and adjust the generation method.

[0048] The generation unit identifies important scenes in a match and generates detailed commentary. For example, the generation unit can identify scoring scenes and generate detailed commentary. For example, the generation unit can identify decisive plays and generate detailed commentary. For example, the generation unit can identify important scenes in a match in real time and generate detailed commentary. For example, the generation unit can generate detailed commentary based on data of important scenes. This makes it possible to identify important scenes in a match and generate detailed commentary. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input match data into a generation AI, which can then identify important scenes and generate detailed commentary.

[0049] The output unit adjusts the video content based on the performance and statistical data of the players in the match. For example, the output unit can adjust the video content based on performance data such as players' scores and assists. For example, the output unit can adjust the video content based on players' success rates and performance data. For example, the output unit can generate statistical data based on past match data and adjust the video content based on that. This allows the video content to be adjusted based on the players' performance and statistical data. Some or all of the above processing in the output unit may be performed using a generation AI, for example, or without a generation AI. For example, the output unit can input player performance data into a generation AI, and the generation AI can analyze the data and adjust the video content.

[0050] The output unit applies an appropriate output algorithm according to the match situation. For example, the output unit can analyze the match situation using a video processing algorithm and apply an appropriate output algorithm. For example, the output unit can analyze the match situation using an audio processing algorithm and apply an appropriate output algorithm. For example, the output unit can apply different output algorithms depending on the match situation. For example, the output unit can select the optimal output algorithm according to the match situation. This allows the optimal output algorithm to be applied according to the match situation. Some or all of the above processing in the output unit may be performed using a generative AI, or without a generative AI. For example, the output unit can input match situation data into a generative AI, which can then select the optimal output algorithm.

[0051] The output unit adjusts the video content based on the position data of the players in the match and the movement of the ball. The output unit can, for example, collect player position data using GPS data and adjust the video content. The output unit can, for example, analyze the movement of the ball using tracking data and adjust the video content. The output unit can, for example, prioritize the display of important scenes based on the player position data. The output unit can, for example, identify important scenes of the match based on the movement of the ball and prioritize their display. This allows the video content to be adjusted based on the player position data and the movement of the ball. Some or all of the above processing in the output unit may be performed using, for example, a generating AI, or without a generating AI. For example, the output unit can input player position data into a generating AI, and the generating AI can analyze the data and adjust the video content.

[0052] The output unit identifies important scenes in a match and outputs detailed video. For example, the output unit can identify scoring scenes and output detailed video. For example, the output unit can identify decisive plays and output detailed video. For example, the output unit can identify important scenes in a match in real time and output detailed video. For example, the output unit can output detailed video based on data of important scenes. This makes it possible to identify important scenes in a match and output detailed video. Some or all of the above processing in the output unit may be performed using a generative AI, or without a generative AI. For example, the output unit can input match data into a generative AI, which can then identify important scenes and output detailed video.

[0053] The reception desk accepts requests via voice input or text input at the time of reception. The reception desk can accept voice input using, for example, speech recognition technology. The reception desk can also accept voice input using, for example, a microphone. The reception desk can accept text input using, for example, keyboard input. The reception desk can also accept text input using, for example, a touchscreen. This allows requests to be accepted via voice input or text input. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input voice input into a generative AI, which can analyze the voice to identify the request content.

[0054] The reception unit selects an appropriate request method based on the viewer's past request history when receiving a request. The reception unit can, for example, analyze the content of past requests and select the optimal request method. The reception unit can, for example, analyze the frequency of requests and select the optimal request method. The reception unit can, for example, determine the priority of requests based on the viewer's past request history. This allows the reception unit to select the optimal request method based on the viewer's past request history. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception unit can input past request history into a generative AI, and the generative AI can analyze the data and select the optimal request method.

[0055] The reception unit selects the appropriate request method based on the viewer's device information upon receiving a request. For example, the reception unit can analyze the type of device and select the optimal request method. For example, the reception unit can analyze the OS version and select the optimal request method. For example, the reception unit can determine the priority of requests based on the viewer's device information. This allows the reception unit to select the optimal request method based on the viewer's device information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input device information into a generative AI, which can then analyze the data and select the optimal request method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] AI assistants that support commentary on sports competitions can also be equipped with functions to monitor the health of athletes. For example, they can monitor athletes' heart rate and body temperature in real time and issue alerts if abnormalities are detected. They can also estimate the athlete's fatigue level and suggest appropriate rest times. Furthermore, based on the athlete's past health data, they can predict the risk of injury and suggest preventive measures. This can support the athlete's health management and contribute to improving their performance.

[0058] AI assistants for sports commentary can further analyze players' tactical movements and evaluate the effectiveness of those tactics. For example, they can analyze players' positioning and pass success rates to quantify the effectiveness of tactics. They can also simulate different tactics and suggest the optimal one. Furthermore, they can analyze the situations in which specific tactics were effective based on past match data and incorporate this into the commentary. This allows for commentary from a tactical perspective, deepening viewers' understanding.

[0059] AI assistants for sports commentary can also be equipped with the ability to perform tactical analysis of matches and incorporate it into their commentary. For example, they can analyze players' positioning and pass success rates, quantifying the effectiveness of tactics. They can also simulate different tactics and suggest the optimal strategy. Furthermore, they can analyze the situations in which specific tactics were effective based on past match data and incorporate this into their commentary. This allows them to provide commentary from a tactical perspective, deepening viewers' understanding.

[0060] AI assistants for sports commentary can also be equipped with the ability to perform tactical analysis of matches and incorporate it into their commentary. For example, they can analyze players' positioning and pass success rates, quantifying the effectiveness of tactics. They can also simulate different tactics and suggest the optimal strategy. Furthermore, they can analyze the situations in which specific tactics were effective based on past match data and incorporate this into their commentary. This allows them to provide commentary from a tactical perspective, deepening viewers' understanding.

[0061] AI assistants for sports commentary can also be equipped with the ability to perform tactical analysis of matches and incorporate it into their commentary. For example, they can analyze players' positioning and pass success rates, quantifying the effectiveness of tactics. They can also simulate different tactics and suggest the optimal strategy. Furthermore, they can analyze the situations in which specific tactics were effective based on past match data and incorporate this into their commentary. This allows them to provide commentary from a tactical perspective, deepening viewers' understanding.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects match data related to the ongoing match. The data collection unit is equipped with sensors that collect, for example, player position data and ball movement data. The data collection unit can collect player position data using, for example, a GPS sensor. The data collection unit can also collect ball movement data using an accelerometer. The data collection unit can, for example, collect player position data in real time and understand the progress of the match. The data collection unit can, for example, track ball movement and identify important plays. Step 2: The analysis unit analyzes the match data collected by the collection unit. The analysis unit can, for example, perform statistical analysis of the data. The analysis unit can, for example, analyze player performance data and extract match highlights. The analysis unit can, for example, use pattern recognition technology to identify important scenes in the match. The analysis unit can, for example, analyze player movement patterns and perform tactical analysis. Step 3: The generation unit generates real-time information, including match highlights, playback, or statistical data, based on the analysis results from the analysis unit. The generation unit can, for example, generate commentary text for the match. The generation unit can, for example, generate commentary audio for the match. The generation unit can, for example, generate highlight videos of the match. The generation unit can, for example, generate graphs based on statistical data of the match. The generation unit can, for example, generate text summarizing important scenes from the match. Step 4: The output unit outputs the real-time information generated by the generation unit as video. The output unit can output, for example, commentary text of the match as audio. The output unit can also display, for example, highlight videos of the match. The output unit can display, for example, statistical data of the match as a graph. The output unit can also output commentary audio of the match from a speaker. The output unit can also display, for example, text summarizing important scenes of the match.

[0064] (Example of form 2) The AI ​​assistant for sports commentary according to an embodiment of the present invention is a system that has the functions of analyzing match data in real time and extracting highlights, supporting commentary based on the past performance and statistical data of athletes, and providing detailed information and past playbacks requested by the user. The AI ​​assistant for sports commentary has the functions of analyzing match data in real time and extracting highlights, supporting commentary based on the past performance and statistical data of athletes, and providing detailed information and past playbacks requested by the user. For example, the AI ​​assistant for sports commentary collects match data related to a match currently being held. The collection unit is equipped with sensors that collect player position data and ball movement data, thereby enabling the acquisition of detailed match data in real time. For example, in a soccer match, the sensors track the positions of players and the movement of the ball and collect data. Next, the AI ​​assistant for sports commentary analyzes the collected match data. The analysis unit analyzes the collected data and generates real-time information including match highlights, playbacks, or statistical data. For example, it analyzes important goal scenes and player performance data and extracts them as highlights. The generation unit generates real-time information based on the analysis results. The generated information is output as commentary text or audio of the match. For example, it can generate commentary text for a match based on the performance and statistical data of the athletes in the match, and output it as audio. Furthermore, the AI ​​assistant for sports commentary support is equipped with a reception unit that receives requests from users and can provide the detailed information and past playbacks that the user requests. The reception unit receives requests via voice input or text input, and the output unit outputs video corresponding to the request. For example, if a user requests past plays of a specific player, the playback video can be output. In addition, the generation unit can estimate the emotions of the athletes in a match and generate real-time information based on the estimated emotions. For example, it can estimate the emotion of joy when a player scores a goal and provide commentary based on that emotion. The collection unit can analyze the patterns of past movements of players and select the optimal collection method.For example, it can analyze a player's past movements and collect data focusing on specific plays. Furthermore, the data collection unit can filter the data to focus on specific players or plays when collecting match data. For example, it can collect data focusing on the movements of specific players or important plays. The data collection unit can estimate the emotions of the user, who is watching the video, and determine the priority of data to collect based on the estimated user emotions. For example, it can prioritize collecting scenes where the viewer is excited. Finally, when collecting match data, the data collection unit can prioritize collecting highly relevant data based on audience reaction data. For example, it can prioritize collecting scenes where the audience is excited. As a result, the AI ​​assistant supporting sports commentary can consistently handle everything from collecting, analyzing, generating, and outputting match data.

[0065] The AI ​​assistant for providing commentary on sports competitions according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and an output unit. The data collection unit collects match data related to a match in progress. The data collection unit includes, for example, sensors that collect player position data and ball movement data. The data collection unit can, for example, collect player position data using a GPS sensor. The data collection unit can also collect ball movement data using an acceleration sensor. The data collection unit can, for example, collect player position data in real time and understand the progress of the match. The data collection unit can, for example, track the movement of the ball and identify important plays. The analysis unit analyzes the match data collected by the data collection unit. The analysis unit can, for example, perform statistical analysis of the data. The analysis unit can, for example, analyze player performance data and extract match highlights. The analysis unit can, for example, use pattern recognition technology to identify important scenes in the match. The analysis unit can, for example, analyze player movement patterns and perform tactical analysis. The generation unit generates real-time information, including match highlights, playback, or statistical data, based on the analysis results from the analysis unit. The generation unit can, for example, generate commentary text for the match. The generation unit can, for example, generate commentary audio for the match. The generation unit can, for example, generate highlight videos for the match. The generation unit can, for example, generate graphs based on statistical data for the match. The generation unit can, for example, generate text summarizing important scenes of the match. The output unit outputs the real-time information generated by the generation unit as video. The output unit can, for example, output commentary text for the match as audio. The output unit can, for example, display highlight videos for the match. The output unit can, for example, display statistical data for the match as a graph. The output unit can, for example, output commentary audio for the match from a speaker. The output unit can, for example, display text summarizing important scenes of the match. As a result, the sports competition commentary support AI assistant according to the embodiment can consistently perform everything from collecting and analyzing match data to generating and outputting it.

[0066] The data collection unit collects match data related to ongoing matches. The unit is equipped with sensors to collect, for example, player position data and ball movement. Specifically, the unit can collect player position data using GPS sensors. This makes it possible to understand players' movement paths and positioning in real time. The unit can also collect ball movement using acceleration sensors. Acceleration sensors detect detailed movements such as ball speed, direction, and rotation, accurately capturing the dynamics of the match. For example, the unit can collect player position data in real time to understand the progress of the match. This allows for immediate tracking of player movements and tactical changes, and detailed recording of the match flow. Furthermore, the unit can track ball movement and identify important plays. For example, it can detect goal scenes, decisive passes, and shots in real time, which can be used for subsequent analysis and generation. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes the match data collected by the data collection unit. For example, the analysis unit can perform statistical analysis of the data. Specifically, it can analyze player performance data and extract match highlights. For instance, it can analyze data such as player distance covered, number of sprints, and pass success rate to evaluate player performance. The analysis unit can use pattern recognition technology to identify important scenes in a match. For example, it can identify goal scenes, decisive passes, and shots to understand the flow of the game. Furthermore, the analysis unit can analyze player movement patterns and perform tactical analysis. For example, it can analyze player positioning and movement patterns to evaluate team tactics and strategies. Based on these analysis results, the analysis unit can extract match highlights and important scenes and provide them to the subsequent generation unit. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term performance evaluations and trend analyses. For example, based on past match data, it can evaluate player growth and performance fluctuations to plan future tactics and training. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term performance evaluation and tactical analysis, thereby improving the reliability and usefulness of the entire system.

[0068] The generation unit generates real-time information, including match highlights, playbacks, or statistical data, based on the analysis results from the analysis unit. Specifically, it can generate commentary text for matches. For example, it can automatically generate text explaining player performance and the flow of the game and provide it to viewers. The generation unit can also generate commentary audio for matches. For example, it can use AI to provide natural-sounding commentary on the match and provide it to viewers in real time. Furthermore, the generation unit can generate match highlight videos. For example, it can automatically edit important scenes and plays and provide them to viewers. The generation unit can also generate graphs based on match statistical data. For example, it can visually display player performance data and match statistics and provide them to viewers. The generation unit can also generate text summarizing important scenes from the match. For example, it can generate text that concisely summarizes the flow of the game and important plays and provide it to viewers. In this way, the generation unit can generate and provide viewers with information in various formats based on the analysis results. Furthermore, the generation unit can update the generated information in real time to respond to the latest match situation. For example, it can update commentary text and highlight videos according to the progress of the match to provide viewers with the latest information. This allows the generation unit to always provide viewers with the latest information and deepen their understanding of the match.

[0069] The output unit outputs real-time information generated by the generation unit as video. Specifically, it can output commentary text of the match as audio. For example, commentary text generated by AI can be converted into natural-sounding audio using speech synthesis technology and provided to viewers. The output unit can also display highlight videos of the match. For example, it can display edited highlight videos of important scenes and plays in real time and provide them to viewers. The output unit can display statistical data of the match as graphs. For example, it can visually display player performance data and match statistics and provide them to viewers. The output unit can also output commentary audio of the match from speakers. For example, it can play commentary audio generated by AI from speakers and provide it to viewers. The output unit can display text summarizing important scenes of the match. For example, it can display text in real time that concisely summarizes the flow of the match and important plays and provide it to viewers. In this way, the output unit can provide generated information to viewers in various formats, deepening their understanding of the match. Furthermore, the output unit can collect viewer feedback and continuously improve the accuracy and effectiveness of the output content. For example, based on viewer feedback, the commentary content and display format can be reviewed to provide clearer and more engaging information. This allows the output unit to deliver information quickly and reliably to viewers, deepening their understanding of the match.

[0070] The data collection unit includes sensors that collect position data of players in a match or the movement of the ball. The data collection unit can, for example, collect player position data using a GPS sensor. The data collection unit can also, for example, collect the movement of the ball using an accelerometer. The data collection unit can, for example, collect player position data in real time and understand the progress of the match. The data collection unit can, for example, track the movement of the ball and identify important plays. This allows for the collection of detailed match data in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input player position data into an AI, which can analyze the data to identify important plays.

[0071] The generation unit generates commentary text for the match based on the performance or statistical data of the players in the match, and the output unit outputs the text as audio. The generation unit can generate commentary text based on performance data such as players' goals and assists, for example. The generation unit can also generate commentary text based on players' success rates and performance data, for example. The generation unit can generate statistical data based on past match data and generate commentary text based on that. The output unit can output the generated commentary text as audio, for example. The output unit can also output the generated commentary text as audio from a speaker, for example. The output unit can also display the generated commentary text together with video, for example. This makes it possible to provide real-time audio commentary on the match. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input player performance data into a generation AI, and the generation AI can generate commentary text.

[0072] The reception unit receives requests from users, and the output unit outputs video corresponding to the requests received by the reception unit. The reception unit can receive requests, for example, via voice input. The reception unit can also receive requests, for example, via text input. The reception unit can output playback video, for example, if a user requests past plays of a specific player. The output unit can display video corresponding to the request. The output unit can also output the video corresponding to the request as audio from a speaker. The output unit can also display the video corresponding to the request along with the video itself. This allows the system to provide video corresponding to the user's request. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input voice input to the AI, which can analyze the request and select appropriate video.

[0073] The reception desk accepts requests via voice input and text input. The reception desk can accept voice input using, for example, speech recognition technology. The reception desk can also accept voice input using, for example, a microphone. The reception desk can accept text input using, for example, keyboard input. The reception desk can also accept text input using, for example, a touchscreen. This allows users to make requests by voice or text. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input voice input into AI, which can analyze the voice to identify the request.

[0074] The generation unit estimates the emotions of the athletes in a match and generates real-time information based on the estimated emotions. The generation unit can estimate the emotions of athletes using, for example, facial recognition technology. The generation unit can also estimate the emotions of athletes using, for example, voice analysis technology. The generation unit can generate real-time information based on the emotions of athletes. The generation unit can estimate the emotions of joy when an athlete scores a goal and provide commentary based on those emotions. This makes it possible to provide real-time information based on the emotions of athletes. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the athletes' facial expression data into the generation AI, and the generation AI can estimate emotions and generate real-time information.

[0075] The data collection unit analyzes the player's past movement patterns and selects an appropriate data collection method. For example, the data collection unit can analyze past match data to identify the player's movement patterns. For example, the data collection unit can analyze training data to identify the player's movement patterns. For example, the data collection unit can determine the optimal sensor placement based on the player's movement patterns. For example, the data collection unit can adjust the timing of data collection based on the player's movement patterns. This enables optimal data collection based on the player's past movements. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past match data into AI, which can analyze movement patterns and select the optimal data collection method.

[0076] The data collection unit filters the collected match data, focusing on specific players or plays. For example, the data collection unit can prioritize the collection of data on the movements of specific players. For example, the data collection unit can prioritize the collection of data on important plays. For example, the data collection unit can track the movements of specific players in real time and collect data. For example, the data collection unit can prioritize the collection of data on important plays. This makes it possible to collect data that focuses on specific players or plays. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the movements of specific players into the AI, and the AI ​​can analyze the data to identify important plays.

[0077] The data collection unit estimates the emotions of the user, who is a viewer of the video, and determines the priority of data to collect based on the estimated user emotions. The data collection unit can estimate the viewer's emotions using, for example, facial recognition technology. The data collection unit can also estimate the viewer's emotions using, for example, voice analysis technology. The data collection unit can determine the priority of data to collect based on the viewer's emotion data. The data collection unit can, for example, prioritize the collection of scenes in which the viewer is excited. This allows the data collection priority to be determined based on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the viewer's facial expression data into a generative AI, which can then estimate emotions and determine the priority of the data.

[0078] The data collection unit prioritizes collecting highly relevant data based on audience reaction data when collecting match data. For example, the data collection unit can analyze audience applause and cheers to identify highly relevant scenes. For example, the data collection unit can analyze audience facial expressions to identify exciting scenes. For example, the data collection unit can prioritize collecting important scenes based on audience reaction data. For example, the data collection unit can analyze audience reaction data in real time to collect highly relevant data. This allows for the priority collection of highly relevant data based on audience reactions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input audience reaction data into AI, which can analyze the data to identify highly relevant scenes.

[0079] The analysis unit estimates the player's emotions and adjusts the analysis criteria based on the estimated emotions. The analysis unit can estimate the player's emotions using, for example, facial recognition technology. The analysis unit can also estimate the player's emotions using, for example, voice analysis technology. The analysis unit can adjust the analysis criteria based on, for example, the player's emotional data. The analysis unit can relax the analysis criteria if, for example, the player is nervous. This allows the analysis criteria to be adjusted based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the player's facial data into a generative AI, which can then estimate emotions and adjust the analysis criteria.

[0080] The analysis unit improves the accuracy of the analysis based on the player's past performance and statistical data. For example, the analysis unit can improve the accuracy of the analysis based on performance data such as the player's goals and assists. For example, the analysis unit can also improve the accuracy of the analysis based on the player's success rate and performance data. For example, the analysis unit can generate statistical data based on past match data and use that to improve the accuracy of the analysis. This allows the accuracy of the analysis to be improved based on the player's past performance and statistical data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the player's performance data into AI, and the AI ​​can analyze the data to improve the accuracy of the analysis.

[0081] The analysis unit applies an appropriate analysis algorithm according to the situation of the match. The analysis unit can, for example, analyze the situation of the match using a machine learning algorithm. The analysis unit can, for example, analyze the situation of the match using a statistical analysis algorithm. The analysis unit can, for example, apply different analysis algorithms depending on the situation of the match. The analysis unit can, for example, select the optimal analysis algorithm according to the situation of the match. This allows the optimal analysis algorithm to be applied according to the situation of the match. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input match situation data into AI, and the AI ​​can select the optimal analysis algorithm.

[0082] The analysis unit estimates the viewer's emotions and adjusts the display method of the analysis results based on the estimated emotions. The analysis unit can estimate the viewer's emotions using, for example, facial recognition technology. The analysis unit can also estimate the viewer's emotions using, for example, voice analysis technology. The analysis unit can adjust the display method of the analysis results based on, for example, the viewer's emotion data. The analysis unit can highlight the analysis results if, for example, the viewer is excited. This allows the display method of the analysis results to be adjusted based on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the viewer's facial expression data into a generative AI, which can then estimate emotions and adjust the display method of the analysis results.

[0083] The analysis unit determines the priority of analysis based on the position data of the players in the match and the movement of the ball. The analysis unit can, for example, collect player position data using GPS data and determine the priority of analysis. The analysis unit can, for example, analyze the movement of the ball using tracking data and determine the priority of analysis. The analysis unit can, for example, prioritize the analysis of important scenes based on the player position data. The analysis unit can, for example, identify important scenes of the match based on the movement of the ball and prioritize their analysis. This allows the analysis priority to be determined based on the position data of the players and the movement of the ball. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input player position data into AI, and the AI ​​can analyze the data and determine the priority of analysis.

[0084] The analysis unit identifies important scenes in a match and performs a detailed analysis. For example, the analysis unit can identify scoring opportunities and perform a detailed analysis. For example, the analysis unit can identify decisive plays and perform a detailed analysis. For example, the analysis unit can identify important scenes in a match in real time and perform a detailed analysis. For example, the analysis unit can perform a detailed analysis based on data from important scenes. This allows for the identification and detailed analysis of important scenes in a match. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match data into AI, which can then identify important scenes and perform a detailed analysis.

[0085] The generation unit estimates the emotions of the players and generates real-time information based on the estimated emotions. The generation unit can estimate the emotions of the players using, for example, facial recognition technology. The generation unit can also estimate the emotions of the players using, for example, voice analysis technology. The generation unit can generate real-time information based on, for example, the players' emotional data. The generation unit can estimate the emotions of joy when a player scores a goal and provide commentary based on those emotions. This makes it possible to provide real-time information based on the players' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the players' facial expression data into the generation AI, and the generation AI can estimate emotions and generate real-time information.

[0086] The generation unit generates explanatory text based on the performance and statistical data of the players in a match. For example, the generation unit can generate explanatory text based on performance data such as goals scored and assists by players. For example, the generation unit can also generate explanatory text based on players' success rates and performance data. For example, the generation unit can generate statistical data based on past match data and generate explanatory text based on that. This makes it possible to generate explanatory text based on the performance and statistical data of the players. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input player performance data into a generation AI, and the generation AI can generate explanatory text.

[0087] The generation unit applies an appropriate generation algorithm according to the match situation. For example, the generation unit can analyze the match situation using a machine learning algorithm and apply an appropriate generation algorithm. For example, the generation unit can analyze the match situation using a statistical analysis algorithm and apply an appropriate generation algorithm. For example, the generation unit can apply different generation algorithms depending on the match situation. For example, the generation unit can select the optimal generation algorithm according to the match situation. This allows the optimal generation algorithm to be applied according to the match situation. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input match situation data into a generation AI, and the generation AI can select the optimal generation algorithm.

[0088] The generation unit estimates the viewer's emotions and determines the priority of information to generate based on the estimated emotions. The generation unit can estimate the viewer's emotions using, for example, facial recognition technology. The generation unit can also estimate the viewer's emotions using, for example, voice analysis technology. The generation unit can determine the priority of information to generate based on, for example, viewer emotion data. The generation unit can, for example, prioritize the generation of important information if the viewer is excited. This allows the information to be prioritized based on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input viewer facial data into the generation AI, which can estimate emotions and determine the priority of information.

[0089] The generation unit adjusts the generation method based on the position data of the players in the match and the movement of the ball. The generation unit can, for example, collect player position data using GPS data and adjust the generation method. The generation unit can, for example, analyze the movement of the ball using tracking data and adjust the generation method. The generation unit can, for example, prioritize the generation of important scenes based on the player position data. The generation unit can, for example, identify important scenes of the match based on the movement of the ball and prioritize their generation. This allows the generation method to be adjusted based on the player position data and the movement of the ball. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input player position data into a generation AI, and the generation AI can analyze the data and adjust the generation method.

[0090] The generation unit identifies important scenes in a match and generates detailed commentary. For example, the generation unit can identify scoring scenes and generate detailed commentary. For example, the generation unit can identify decisive plays and generate detailed commentary. For example, the generation unit can identify important scenes in a match in real time and generate detailed commentary. For example, the generation unit can generate detailed commentary based on data of important scenes. This makes it possible to identify important scenes in a match and generate detailed commentary. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input match data into a generation AI, which can then identify important scenes and generate detailed commentary.

[0091] The output unit estimates the viewer's emotions and adjusts the video display method based on the estimated emotions. The output unit can estimate the viewer's emotions using, for example, facial recognition technology. The output unit can also estimate the viewer's emotions using, for example, voice analysis technology. The output unit can adjust the video display method based on, for example, the viewer's emotion data. The output unit can highlight the video if, for example, the viewer is excited. This allows the video display method to be adjusted based on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using, for example, AI, or not using AI. For example, the output unit can input the viewer's facial expression data to the generative AI, which can then estimate the emotions and adjust the video display method.

[0092] The output unit adjusts the video content based on the performance and statistical data of the players in the match. For example, the output unit can adjust the video content based on performance data such as players' scores and assists. For example, the output unit can adjust the video content based on players' success rates and performance data. For example, the output unit can generate statistical data based on past match data and adjust the video content based on that. This allows the video content to be adjusted based on the players' performance and statistical data. Some or all of the above processing in the output unit may be performed using a generation AI, for example, or without a generation AI. For example, the output unit can input player performance data into a generation AI, and the generation AI can analyze the data and adjust the video content.

[0093] The output unit applies an appropriate output algorithm according to the match situation. For example, the output unit can analyze the match situation using a video processing algorithm and apply an appropriate output algorithm. For example, the output unit can analyze the match situation using an audio processing algorithm and apply an appropriate output algorithm. For example, the output unit can apply different output algorithms depending on the match situation. For example, the output unit can select the optimal output algorithm according to the match situation. This allows the optimal output algorithm to be applied according to the match situation. Some or all of the above processing in the output unit may be performed using a generative AI, or without a generative AI. For example, the output unit can input match situation data into a generative AI, which can then select the optimal output algorithm.

[0094] The output unit estimates the viewer's emotions and determines the priority of the video based on the estimated emotions. The output unit can estimate the viewer's emotions using, for example, facial recognition technology. The output unit can also estimate the viewer's emotions using, for example, voice analysis technology. The output unit can determine the priority of the video based on, for example, viewer emotion data. The output unit can, for example, prioritize displaying important video if the viewer is excited. This allows the priority of the video to be determined based on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using, for example, AI, or not using AI. For example, the output unit can input viewer facial data to a generative AI, which can then estimate emotions and determine the priority of the video.

[0095] The output unit adjusts the video content based on the position data of the players in the match and the movement of the ball. The output unit can, for example, collect player position data using GPS data and adjust the video content. The output unit can, for example, analyze the movement of the ball using tracking data and adjust the video content. The output unit can, for example, prioritize the display of important scenes based on the player position data. The output unit can, for example, identify important scenes of the match based on the movement of the ball and prioritize their display. This allows the video content to be adjusted based on the player position data and the movement of the ball. Some or all of the above processing in the output unit may be performed using, for example, a generating AI, or without a generating AI. For example, the output unit can input player position data into a generating AI, and the generating AI can analyze the data and adjust the video content.

[0096] The output unit identifies important scenes in a match and outputs detailed video. For example, the output unit can identify scoring scenes and output detailed video. For example, the output unit can identify decisive plays and output detailed video. For example, the output unit can identify important scenes in a match in real time and output detailed video. For example, the output unit can output detailed video based on data of important scenes. This makes it possible to identify important scenes in a match and output detailed video. Some or all of the above processing in the output unit may be performed using a generative AI, or without a generative AI. For example, the output unit can input match data into a generative AI, which can then identify important scenes and output detailed video.

[0097] The reception unit estimates the viewer's emotions and prioritizes requests based on the estimated emotions. The reception unit can estimate the viewer's emotions using, for example, facial recognition technology. The reception unit can also estimate the viewer's emotions using, for example, voice analysis technology. The reception unit can prioritize requests based on, for example, viewer emotion data. The reception unit can prioritize important requests if, for example, the viewer is excited. This allows the reception unit to prioritize requests based on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input viewer facial data into a generative AI, which can estimate emotions and determine the priority of requests.

[0098] The reception desk accepts requests via voice input or text input at the time of reception. The reception desk can accept voice input using, for example, speech recognition technology. The reception desk can also accept voice input using, for example, a microphone. The reception desk can accept text input using, for example, keyboard input. The reception desk can also accept text input using, for example, a touchscreen. This allows requests to be accepted via voice input or text input. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input voice input into a generative AI, which can analyze the voice to identify the request content.

[0099] The reception unit selects an appropriate request method based on the viewer's past request history when receiving a request. The reception unit can, for example, analyze the content of past requests and select the optimal request method. The reception unit can, for example, analyze the frequency of requests and select the optimal request method. The reception unit can, for example, determine the priority of requests based on the viewer's past request history. This allows the reception unit to select the optimal request method based on the viewer's past request history. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception unit can input past request history into a generative AI, and the generative AI can analyze the data and select the optimal request method.

[0100] The reception unit estimates the viewer's emotions and adjusts the display method of requests based on the estimated emotions. The reception unit can estimate the viewer's emotions using, for example, facial recognition technology. The reception unit can also estimate the viewer's emotions using, for example, voice analysis technology. The reception unit can adjust the display method of requests based on the viewer's emotion data. The reception unit can highlight requests if, for example, the viewer is excited. This allows the display method of requests to be adjusted based on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the viewer's facial expression data into a generative AI, which can estimate emotions and adjust the display method of requests.

[0101] The reception unit selects the appropriate request method based on the viewer's device information upon receiving a request. For example, the reception unit can analyze the type of device and select the optimal request method. For example, the reception unit can analyze the OS version and select the optimal request method. For example, the reception unit can determine the priority of requests based on the viewer's device information. This allows the reception unit to select the optimal request method based on the viewer's device information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input device information into a generative AI, which can then analyze the data and select the optimal request method.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] AI assistants that support commentary on sports competitions can also be equipped with functions to monitor the health of athletes. For example, they can monitor athletes' heart rate and body temperature in real time and issue alerts if abnormalities are detected. They can also estimate the athlete's fatigue level and suggest appropriate rest times. Furthermore, based on the athlete's past health data, they can predict the risk of injury and suggest preventive measures. This can support the athlete's health management and contribute to improving their performance.

[0104] AI assistants for sports commentary can also be equipped with features to analyze audience reactions in real time and visualize the excitement of the match. For example, they can analyze the volume of cheers and applause from the audience and display the level of excitement in a graph. They can also analyze the facial expressions of the audience and estimate emotions such as joy and surprise. Furthermore, they can automatically select and provide highlight scenes from the match based on audience reaction data. This allows for more immersive commentary by utilizing audience reactions.

[0105] AI assistants for sports commentary can further analyze players' tactical movements and evaluate the effectiveness of those tactics. For example, they can analyze players' positioning and pass success rates to quantify the effectiveness of tactics. They can also simulate different tactics and suggest the optimal one. Furthermore, they can analyze the situations in which specific tactics were effective based on past match data and incorporate this into the commentary. This allows for commentary from a tactical perspective, deepening viewers' understanding.

[0106] AI assistants for sports commentary can also be equipped with the ability to estimate the psychological state of athletes and reflect it in their commentary. For example, they can analyze an athlete's facial expressions and movements to estimate their psychological state, such as tension or concentration. They can also predict an athlete's psychological state in a specific situation based on their past performance data. Furthermore, they can explain the impact of the estimated psychological state on the athlete's performance. This allows for commentary that takes the athlete's psychological state into account, thereby attracting the interest of viewers.

[0107] AI assistants for sports commentary can also be equipped with the ability to perform tactical analysis of matches and incorporate it into their commentary. For example, they can analyze players' positioning and pass success rates, quantifying the effectiveness of tactics. They can also simulate different tactics and suggest the optimal strategy. Furthermore, they can analyze the situations in which specific tactics were effective based on past match data and incorporate this into their commentary. This allows them to provide commentary from a tactical perspective, deepening viewers' understanding.

[0108] AI assistants for sports commentary can also be equipped with the ability to estimate athletes' emotions and provide commentary based on those estimated emotions. For example, they can analyze athletes' facial expressions and movements to estimate emotions such as joy or tension. They can also predict emotions in specific situations based on athletes' past performance data. Furthermore, they can explain the impact of the estimated emotions on the athletes' performance. This allows for commentary that takes athletes' emotions into account, thereby attracting the interest of viewers.

[0109] AI assistants for sports commentary can also be equipped with the ability to perform tactical analysis of matches and incorporate it into their commentary. For example, they can analyze players' positioning and pass success rates, quantifying the effectiveness of tactics. They can also simulate different tactics and suggest the optimal strategy. Furthermore, they can analyze the situations in which specific tactics were effective based on past match data and incorporate this into their commentary. This allows them to provide commentary from a tactical perspective, deepening viewers' understanding.

[0110] AI assistants for sports commentary can also be equipped with the ability to estimate the psychological state of athletes and reflect it in their commentary. For example, they can analyze an athlete's facial expressions and movements to estimate their psychological state, such as tension or concentration. They can also predict an athlete's psychological state in a specific situation based on their past performance data. Furthermore, they can explain the impact of the estimated psychological state on the athlete's performance. This allows for commentary that takes the athlete's psychological state into account, thereby attracting the interest of viewers.

[0111] AI assistants for sports commentary can also be equipped with the ability to perform tactical analysis of matches and incorporate it into their commentary. For example, they can analyze players' positioning and pass success rates, quantifying the effectiveness of tactics. They can also simulate different tactics and suggest the optimal strategy. Furthermore, they can analyze the situations in which specific tactics were effective based on past match data and incorporate this into their commentary. This allows them to provide commentary from a tactical perspective, deepening viewers' understanding.

[0112] AI assistants for sports commentary can also be equipped with the ability to estimate athletes' emotions and provide commentary based on those estimated emotions. For example, they can analyze athletes' facial expressions and movements to estimate emotions such as joy or tension. They can also predict emotions in specific situations based on athletes' past performance data. Furthermore, they can explain the impact of the estimated emotions on the athletes' performance. This allows for commentary that takes athletes' emotions into account, thereby attracting the interest of viewers.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects match data related to the ongoing match. The data collection unit is equipped with sensors that collect, for example, player position data and ball movement data. The data collection unit can collect player position data using, for example, a GPS sensor. The data collection unit can also collect ball movement data using an accelerometer. The data collection unit can, for example, collect player position data in real time and understand the progress of the match. The data collection unit can, for example, track ball movement and identify important plays. Step 2: The analysis unit analyzes the match data collected by the collection unit. The analysis unit can, for example, perform statistical analysis of the data. The analysis unit can, for example, analyze player performance data and extract match highlights. The analysis unit can, for example, use pattern recognition technology to identify important scenes in the match. The analysis unit can, for example, analyze player movement patterns and perform tactical analysis. Step 3: The generation unit generates real-time information, including match highlights, playback, or statistical data, based on the analysis results from the analysis unit. The generation unit can, for example, generate commentary text for the match. The generation unit can, for example, generate commentary audio for the match. The generation unit can, for example, generate highlight videos of the match. The generation unit can, for example, generate graphs based on statistical data of the match. The generation unit can, for example, generate text summarizing important scenes from the match. Step 4: The output unit outputs the real-time information generated by the generation unit as video. The output unit can output, for example, commentary text of the match as audio. The output unit can also display, for example, highlight videos of the match. The output unit can display, for example, statistical data of the match as a graph. The output unit can also output commentary audio of the match from a speaker. The output unit can also display, for example, text summarizing important scenes of the match.

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] For example, the data collection unit is implemented by either the data processing unit 12 or the smart device 14. For example, the data collection unit can collect player location data using the camera 42 or GPS sensor of the smart device 14. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and generates match highlights and statistical data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates real-time information based on the analysis results. The output unit is implemented by, for example, the output device 40 of the smart device 14, which outputs the generated information as video or audio. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] For example, the data collection unit can be implemented by either the data processing unit 12 or the smart glasses 214. For example, the data collection unit can collect player location data using the camera 42 or GPS sensor of the smart glasses 214. The analysis unit can be implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and generates match highlights and statistical data. The generation unit can be implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates real-time information based on the analysis results. The output unit can be implemented by, for example, the speaker 240 of the smart glasses 214, which outputs the generated information as audio. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] For example, the data collection unit is implemented by either the data processing unit 12 or the headset terminal 314. For example, the data collection unit can collect player location data using the camera 42 or GPS sensor of the headset terminal 314. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and generates match highlights and statistical data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates real-time information based on the analysis results. The output unit is implemented by, for example, the display 343 of the headset terminal 314, which outputs the generated information as video. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] For example, the data collection unit can be implemented by either the data processing unit 12 or the robot 414. For example, the data collection unit can collect player position data using the camera 42 or GPS sensor of the robot 414. The analysis unit can be implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and generates match highlights and statistical data. The generation unit can be implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates real-time information based on the analysis results. The output unit can be implemented by, for example, the speaker 240 of the robot 414, which outputs the generated information as sound. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0168] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) The data collection department collects match data related to ongoing matches, An analysis unit analyzes the match data collected by the aforementioned collection unit, Based on the analysis results from the aforementioned analysis unit, a generation unit generates real-time information including match highlights, playback, or statistical data. The system includes an output unit that outputs the real-time information generated by the generation unit as video. A system characterized by the following features. (Note 2) The aforementioned collection unit is It is equipped with sensors that collect position data of players in a match or the movement of the ball. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the performance or statistical data of the players in the match, generate text to explain the match. The output unit is, Output the aforementioned text as audio. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a reception desk that accepts requests from users, The output unit is, The reception unit outputs video corresponding to the request received. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Requests are accepted via voice input and text input. The system described in Appendix 4, characterized by the features described herein. (Note 6) The generating unit is To estimate the emotions of the athletes in the match, Generate real-time information based on estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the player's past movement patterns and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting match data, Filter by focusing on a specific player or play. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is By estimating the emotions of the users who are viewers of the video, Prioritize the data to collect based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting match data, Prioritize collecting highly relevant data based on audience reaction data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, To estimate the players' emotions, Adjust the analysis criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Improve the accuracy of the analysis based on the players' past performance and statistical data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Apply the appropriate analysis algorithm according to the match situation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, To estimate the emotions of the viewers, Adjust the display method of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The analysis priorities are determined based on the positional data of the players in the match and the movement of the ball. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Identify the key moments of the match, Perform a detailed analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is To estimate the players' emotions, Generate real-time information based on estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is Generate explanatory text based on the performance and statistical data of the players in the match. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Apply the appropriate generation algorithm depending on the match situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is To estimate the emotions of the viewers, Prioritize the information generated based on estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is The generation method is adjusted based on the position data of the players in the match and the movement of the ball. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is Identify the key moments of the match, Generate a detailed explanation The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, To estimate the emotions of the viewers, Adjust the way the video is displayed based on estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The output unit is, The video content is adjusted based on the performance and statistical data of the athletes in the match. The system described in Appendix 1, characterized by the features described herein. (Note 25) The output unit is, Apply the appropriate output algorithm depending on the match situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The output unit is, To estimate the emotions of the viewers, Prioritize videos based on estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The output unit is, The video content is adjusted based on the positional data of the players in the match and the movement of the ball. The system described in Appendix 1, characterized by the features described herein. (Note 28) The output unit is, Identify the key moments of the match, Output detailed video The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reception unit is To estimate the emotions of the viewers, Prioritize requests based on estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reception unit is Requests are accepted via voice input or text input at the time of registration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reception unit is At the time of reception, the appropriate request method will be selected based on the viewer's past request history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reception unit is To estimate the emotions of the viewers, Adjust how requests are displayed based on estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned reception unit is At the time of registration, the appropriate request method is selected based on the viewer's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects match data related to ongoing matches, An analysis unit analyzes the match data collected by the aforementioned collection unit, Based on the analysis results from the aforementioned analysis unit, a generation unit generates real-time information including match highlights, playback, or statistical data. The system includes an output unit that outputs the real-time information generated by the generation unit as video. A system characterized by the following features.

2. The aforementioned collection unit is It is equipped with sensors that collect position data of players in a match or the movement of the ball. The system according to feature 1.

3. The generating unit is Based on the performance or statistical data of the players in the match, generate text to explain the match. The output unit is, Output the aforementioned text as audio. The system according to feature 1.

4. It has a reception desk that accepts requests from users, The output unit is, The reception unit outputs video corresponding to the request received. The system according to feature 1.

5. The aforementioned reception unit is Requests are accepted via voice input and text input. The system according to feature 4.

6. The generating unit is To estimate the emotions of the athletes in the match, Generate real-time information based on estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the player's past movement patterns and select the appropriate data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting match data, Filter by focusing on a specific player or play. The system according to feature 1.

9. The aforementioned collection unit is By estimating the emotions of the users who are viewers of the video, Prioritize the data to collect based on estimated user sentiment. The system according to feature 1.

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