system

Abstract

The system according to this embodiment aims to provide high-quality sports commentary tailored to the user's needs. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user information. The analysis unit analyzes the video based on the information collected by the collection unit. The generation unit generates commentary audio based on the video analyzed by the analysis unit. The provision unit provides the audio generated by the generation unit.

Description

Technical Field

[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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the quality of sports broadcasts depends on the knowledge and expression of the announcer, and there is a problem that the user satisfaction varies.

[0005] The system according to the embodiment aims to provide a high-quality sports broadcast that meets the user's requirements.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user information. The analysis unit analyzes video based on the information collected by the collection unit. The generation unit generates commentary audio based on the video analyzed by the analysis unit. The provision unit provides the audio generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide high-quality sports commentary tailored to the user's needs. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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 sports commentary system according to an embodiment of the present invention is a system that performs sports commentary using generative AI. The sports commentary system can improve viewer satisfaction, which has been dependent on the announcer's knowledge and expressive abilities in conventional sports commentary. In particular, the viewing experience was sometimes detrimental due to the announcer's lack of knowledge or extreme favoritism towards one team, but by using generative AI, real-time video analysis can be performed, and commentary can be provided that is tailored to the needs of each individual viewer. First, the user inputs information about their level of understanding of the sport and the team and players they support. For example, if the user is knowledgeable about baseball, detailed explanations such as how the catcher attacks will be provided, and if the user is not knowledgeable about baseball, an explanation including the rules will be provided. It is also possible to provide commentary that is specialized for the team or players the user supports. For example, when a player who is not playing in the game appears on camera, the system can provide commentary on that player's actions. Next, the generative AI analyzes the game video in real time and generates commentary audio that is tailored to the user's requests. The generated audio can be adjusted in terms of the level of enthusiasm and the amount of trivia to suit the user's preferences. For example, a user who is a fan of a particular team and prefers high-energy commentary can be provided with such commentary. Furthermore, because the generating AI can analyze multiple camera feeds simultaneously, it can pick up on subtle movements during a match, as well as seemingly unrelated actions. This allows viewers to enjoy the match from a more multifaceted perspective. This system elevates the sports viewing experience to the next level, providing rich and personalized sports commentary for each viewer. As a result, the sports commentary system can improve viewer satisfaction and provide a more fulfilling sports viewing experience.

[0029] The sports commentary system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user information. For example, the collection unit can collect information such as the user's age, gender, and interests. The collection unit can also collect information about the user's understanding of sports and the teams and players they support. For example, the collection unit can evaluate the user's understanding of sports based on survey results and past viewing history. The analysis unit analyzes the video based on the information collected by the collection unit. For example, the analysis unit can analyze the video using image recognition technology or motion analysis technology. The generation unit generates commentary audio based on the video analyzed by the analysis unit. For example, the generation unit can generate commentary audio using speech synthesis technology or template-based generation technology. The provision unit provides the audio generated by the generation unit. For example, the provision unit can provide the generated audio using streaming technology or download technology. As a result, the sports commentary system according to this embodiment can provide sports commentary in real time based on user information. The elements of the collection unit, analysis unit, generation unit, and provision unit are sequentially related. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user information into an AI model, and the AI ​​model can evaluate the user's understanding of sports, the team they support, and information about the players. Some or all of the processing described above in the analysis unit may be performed using a generation AI. For example, the analysis unit can input the information collected by the data collection unit into a generation AI, and have the generation AI perform video analysis. Some or all of the processing described above in the generation unit may be performed using a generation AI. For example, the generation unit can input the video analyzed by the analysis unit into a generation AI, and have the generation AI perform commentary audio generation. Some or all of the processing described above in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can input the audio generated by the generation unit into an AI model, and the AI ​​model can determine how to provide the audio.

[0030] The data collection unit collects user information. For example, the data collection unit can collect information such as the user's age, gender, and interests. Specifically, it collects basic information entered by the user when registering with the system, as well as detailed interest data obtained through questionnaires. The data collection unit can also collect information about the user's understanding of sports and the teams and players they support. For example, the data collection unit can evaluate the user's understanding of sports based on questionnaire results and past viewing history. Furthermore, the data collection unit can analyze the user's social media posts and online activity history to collect information about the user's interests and the teams and players they support. This allows the data collection unit to create detailed user profiles and collect basic data to provide optimal sports commentary to individual users. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input user information into an AI model and have the AI ​​model evaluate the user's understanding of sports and the teams and players they support. The AI ​​model can learn from past data and predict user behavior patterns and interests with high accuracy. This allows the data collection unit to efficiently and accurately collect user information, thereby improving the overall system performance.

[0031] The analysis unit analyzes video based on information collected by the data collection unit. The analysis unit can analyze video using, for example, image recognition technology or motion analysis technology. Specifically, it can analyze sports match footage in real time to understand player movements, ball position, and the progress of the match. Based on collected user information, the analysis unit can focus on specific players or plays that the user is interested in. For example, it can prioritize the analysis of the team or player the user supports and provide commentary based on the results. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input information collected by the data collection unit into the generative AI and have the generative AI perform the video analysis. The generative AI can extract features from the video using a deep learning model and analyze player movements and the progress of the match with high accuracy. This allows the analysis unit to quickly and accurately analyze the collected data and generate basic information to provide users with optimal sports commentary. Furthermore, the analysis unit can utilize past match data and statistical information to evaluate match trends and player performance, and predict future match developments. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term match analysis and predictions, thereby improving the reliability and accuracy of the entire system.

[0032] The generation unit generates commentary audio based on the video analyzed by the analysis unit. The generation unit can generate commentary audio using, for example, speech synthesis technology or template-based generation technology. Specifically, it automatically generates appropriate commentary based on data on the progress of the match and the movements of the players provided by the analysis unit. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the video analyzed by the analysis unit into the generation AI and have the generation AI perform the generation of commentary audio. The generation AI uses natural language processing technology to generate appropriate commentary according to the situation of the match and provides it to the user in real time. The generation AI can adjust the content and tone of the commentary according to the user's interests and level of understanding. For example, it can provide detailed explanations of basic rules and player introductions for beginners, and detailed explanations of tactics and player techniques for advanced users. In this way, the generation unit can provide customized commentary that meets the user's needs. Furthermore, the generation unit can also utilize past match data and statistical information to evaluate match trends and player performance and predict future match developments. This allows the generation unit to handle not only real-time commentary but also long-term match analysis and predictions, improving the overall reliability and accuracy of the system.

[0033] The service provider provides the audio generated by the generation unit. The service provider can provide audio generated using, for example, streaming or downloading technologies. Specifically, if a user is watching a match in real time, the service provider can deliver commentary audio in real time using streaming technology. If a user watches the match later, the service provider can provide commentary audio using downloading technology. Some or all of the above processing in the service provider may be performed using, for example, AI, or not. For example, the service provider can input the audio generated by the generation unit into an AI model, and the AI ​​model can determine the method of audio delivery. The AI ​​model can select the optimal delivery method considering the user's viewing environment and network conditions. This allows the service provider to provide commentary audio to the user in the most optimal form. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the delivery method. For example, it can review and improve the delivery method based on the user's viewing history and feedback. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only streaming but also voice calls, SMS, email, etc. This allows the service provider to deliver commentary audio to users quickly and reliably, improving the overall reliability of the system and user satisfaction.

[0034] The analysis unit can analyze multiple camera feeds simultaneously. For example, it can simultaneously analyze footage from different angles or at different resolutions. For example, the analysis unit can analyze multiple camera feeds in real time to provide detailed information about the match. The analysis unit can also integrate and analyze multiple camera feeds. For example, it can integrate different camera feeds to grasp the overall picture of the match. This allows for the provision of detailed information about the match by simultaneously analyzing multiple camera feeds. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input multiple camera feeds into the generation AI and have the generation AI perform the video analysis.

[0035] The data collection unit can collect information about the user's understanding of sports and the teams and players they support. For example, the data collection unit can evaluate the user's understanding of sports based on survey results and past viewing history. The data collection unit can also collect information about the teams and players the user supports based on social media following information and past viewing history. For example, the data collection unit can collect information about the teams and players the user follows and identify the teams and players the user supports. This allows for more personalized commentary by collecting information about the user's understanding of sports and the teams and players they support. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input user information into an AI model and have the AI ​​model evaluate the user's understanding of sports and the teams and players they support.

[0036] The generation unit can adjust the level of enthusiasm and the amount of trivia to suit the user's preferences. For example, the generation unit can adjust the level of enthusiasm by adjusting the intonation and speaking speed of the voice. It can also adjust the amount of trivia by adjusting the frequency and type of information. For example, the generation unit can provide commentary with high enthusiasm or commentary with lots of trivia to suit the user's preferences. By adjusting the level of enthusiasm and the amount of trivia in the commentary to suit the user's preferences, a more satisfying commentary can be provided. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input information about the user's preferences into the generation AI and have the generation AI generate the commentary audio.

[0037] The service provider can provide the generated audio in real time. For example, the service provider can provide the generated audio in real time using streaming technology. The service provider can also set an acceptable latency range and provide audio in real time. For example, the service provider can provide viewers with delay-free commentary using low-latency streaming technology. This allows for delay-free commentary to be provided to viewers by providing the generated audio in real time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated audio into an AI model and the AI ​​model can determine how to provide the audio.

[0038] The data collection unit can analyze the user's past viewing history and select the optimal information collection method. For example, the data collection unit can collect information about matches and players that the user might be interested in, based on data from matches the user has watched in the past. The data collection unit can also analyze trends in matches the user has watched in the past and prioritize the collection of information about similar matches and situations. Furthermore, the data collection unit can collect information about highly-rated matches and players based on the user's ratings of matches the user has watched in the past. In this way, by analyzing the user's past viewing history, more information that is likely to be of interest can be collected. 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 the user's viewing history data into an AI model and have the AI ​​model select the optimal information collection method.

[0039] The data collection unit can filter information based on the user's current areas of interest and sports trends. For example, the data collection unit can prioritize collecting information on sports and athletes that the user is currently interested in. It can also prioritize collecting information on current sports trends and trending matches. Furthermore, the data collection unit can collect information on specific playing styles and tactics that the user is interested in. This allows for the provision of more relevant information by filtering information based on the user's current areas of interest and sports trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input user areas of interest and sports trend data into an AI model and have the AI ​​model perform the information filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user lives in a specific region, the data collection unit can prioritize the collection of information about teams and players in that region. Furthermore, if the user is traveling, the data collection unit can collect information about matches and events taking place in the region they are visiting. Additionally, if the user is interested in sports in a specific region, the data collection unit can collect information about sports in that region. This allows for the provision of more relevant information by considering the user's geographical location. 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 the user's geographical location into an AI model, and the AI ​​model can select highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can prioritize collecting information about teams and players that the user follows on social media. It can also collect information about matches and events that the user has shared on social media. Furthermore, the data collection unit can collect information related to topics that the user has shown interest in on social media. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's social media data into an AI model and collect relevant information using the AI ​​model.

[0042] The analysis unit can improve the accuracy of its analysis by considering the progress of the match and important moments during the analysis. For example, the analysis unit can adjust the accuracy of the analysis in real time according to the progress of the match. The analysis unit can also perform detailed analysis on important moments and scoring scenes. Furthermore, the analysis unit can improve the accuracy of the analysis based on the flow of the match and tactics. As a result, by considering the progress of the match and important moments, it can provide more accurate analysis results. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input match progress data into the generative AI and have the generative AI perform the adjustment of the analysis accuracy.

[0043] The analysis unit can apply different analysis algorithms based on the rules and tactics of the match during analysis. For example, the analysis unit can select an appropriate analysis algorithm based on the rules of the match. It can also apply different analysis algorithms based on the tactics of the match. Furthermore, the analysis unit can select the optimal analysis algorithm according to the characteristics of the match. This allows for the provision of more appropriate analysis results by applying analysis algorithms based on the rules and tactics of the match. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input match rules and tactical data into the generative AI and have the generative AI select the analysis algorithm.

[0044] The analysis unit can perform analysis while considering the geographical background and cultural elements of the match. For example, the analysis unit can perform an appropriate analysis based on the geographical background of the match. Furthermore, the analysis unit can improve the accuracy of the analysis by considering the cultural elements of the match. In addition, the analysis unit can select the optimal analysis method according to the characteristics of the match. This allows for the provision of more appropriate analysis results by considering the geographical background and cultural elements of the match. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input data on the geographical background and cultural elements of the match into the generative AI and have the generative AI perform improvements to the accuracy of the analysis.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data on the match during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the match. Furthermore, the analysis unit can perform a detailed analysis based on the match data. In addition, the analysis unit can select the optimal analysis method according to the characteristics of the match. This allows for the provision of more accurate analysis results by referring to relevant literature and data on the match. Some or all of the above-mentioned processes in the analysis unit are performed using a generating AI. For example, the analysis unit can input relevant literature and data on the match into the generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0046] The generation unit can adjust the level of detail in the audio based on the importance of the match and the user's level of interest during generation. For example, if the match is of high importance, the generation unit can generate audio that includes detailed commentary. Similarly, if the user's level of interest is high, the generation unit can generate audio that includes detailed information. Furthermore, the generation unit can select the optimal audio generation method according to the characteristics of the match. This allows for the provision of more appropriate audio by adjusting the level of detail based on the importance of the match and the user's level of interest. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the importance of the match and the user's level of interest into the generation AI, and have the generation AI perform the adjustment of the level of detail in the audio.

[0047] The generation unit can apply different voice generation algorithms during generation depending on the category and characteristics of the match. For example, the generation unit can select an appropriate voice generation algorithm based on the category of the match. It can also apply different voice generation algorithms depending on the characteristics of the match. Furthermore, the generation unit can select the optimal voice generation method depending on the characteristics of the match. This allows for the provision of more appropriate voices by applying voice generation algorithms according to the category and characteristics of the match. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input match category and characteristic data into the generation AI and have the generation AI select a voice generation algorithm.

[0048] The generation unit can determine audio priorities based on the progress of the match during generation. For example, the generation unit can adjust audio priorities in real time according to the progress of the match. The generation unit can also generate audio that includes detailed commentary for important moments and scoring scenes. Furthermore, the generation unit can determine audio priorities based on the flow of the match and tactics. This allows for the provision of more appropriate audio by determining audio priorities based on the progress of the match. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input match progress data into the generation AI and have the generation AI perform the determination of audio priorities.

[0049] The generation unit can adjust the order of audio based on the relevance of the matches during generation. For example, the generation unit can select an appropriate order of audio based on the relevance of the matches. The generation unit can also adjust the order of audio according to the characteristics of the matches. Furthermore, the generation unit can select the optimal audio generation method according to the characteristics of the matches. This allows for the provision of more appropriate audio by adjusting the order of audio based on the relevance of the matches. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input match relevance data into the generation AI and have the generation AI perform the adjustment of the order of audio.

[0050] The service provider can select the optimal delivery method by referring to the user's past viewing history at the time of delivery. For example, the service provider can provide audio about matches or players that the user might be interested in, based on data from matches the user has watched in the past. The service provider can also analyze trends in matches the user has watched in the past and prioritize providing audio about similar matches or situations. Furthermore, the service provider can provide audio about matches or players with high ratings, based on the user's ratings of matches the user has watched in the past. This allows for the provision of more appropriate audio by referring to the user's past viewing history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's viewing history data into an AI model and have the AI ​​model select the optimal delivery method.

[0051] The delivery unit can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the delivery unit can provide an audio delivery method that is adapted to the screen size. If the user is using a tablet, the delivery unit can also provide an audio delivery method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the delivery unit can provide a concise and highly visible audio delivery method. This allows for the provision of more appropriate audio by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into an AI model and have the AI ​​model select the optimal delivery method.

[0052] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user lives in a specific region, the service provider can prioritize providing audio related to teams and players in that region. Furthermore, if the user is traveling, the service provider can provide audio related to matches and events taking place in the region they are visiting. Additionally, if the user is interested in sports in a specific region, the service provider can provide audio related to sports in that region. This allows for the provision of more appropriate audio by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into an AI model and have the AI ​​model select the optimal delivery method.

[0053] The delivery unit can analyze the user's social media activity at the time of delivery to select the optimal delivery method. For example, the delivery unit can prioritize providing audio related to teams or players that the user follows on social media. It can also provide audio related to matches or events that the user has shared on social media. Furthermore, the delivery unit can provide audio related to topics that the user has shown interest in on social media. This allows for the provision of more appropriate audio by analyzing the user's social media activity. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's social media data into an AI model and have the AI ​​model select the optimal delivery method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] Sports commentary systems can be equipped with features that automatically adjust the volume and sound quality of the audio according to the user's viewing environment. For example, if the user is using headphones, the system can emphasize high and low frequencies to enhance sound quality. If the user is using speakers, the system can automatically adjust the volume and minimize echo. Furthermore, if the user is in a noisy environment, noise-canceling technology can be used to suppress background noise and provide clear commentary audio. This allows for a more comfortable viewing experience by providing audio optimized for the user's viewing environment.

[0056] A sports commentary system can be equipped with a function to automatically generate highlight scenes from matches based on the user's past viewing history. For example, it can analyze data from matches the user has watched in the past and extract plays and scoring scenes that are likely to be of particular interest. Furthermore, if the user is interested in a particular player or team, the system can prioritize generating highlight scenes related to that player or team. It can also generate highlights that include similar situations based on match scenes that the user has previously rated highly. In this way, the system can improve the viewing experience by providing highlight scenes tailored to the user's interests.

[0057] A sports commentary system can provide information about region-specific sports events and athletes based on the user's geographical location. For example, if a user lives in a specific region, it can prioritize providing information about matches and events taking place in that region. If the user is traveling, it can also provide information about sports events and local athletes in their destination region. Furthermore, if the user is interested in sports in a particular region, it can provide the latest information and trends related to those sports. This allows for the provision of more relevant content by basing information on the user's geographical location.

[0058] A sports commentary system can be equipped with the ability to analyze users' social media activity and provide relevant information in real time. For example, it can provide the latest information on teams and players that users follow on social media. It can also provide real-time information on matches and events that users have shared on social media. Furthermore, it can provide information related to topics that users have shown interest in on social media. This allows for the provision of more relevant content by providing information based on users' social media activity.

[0059] Sports commentary systems can be equipped with a function that automatically adjusts the optimal video quality and resolution based on the user's device information. For example, if the user is using a smartphone, the video resolution can be automatically adjusted to conserve data usage. If the user is using a tablet, the system can provide high-resolution video optimized for the large screen. Furthermore, if the user is using a smart TV, it is possible to provide the highest quality video to create an immersive viewing experience. In this way, adjusting the video quality based on the user's device information can provide a more comfortable viewing experience.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects user information. For example, the data collection unit can collect information such as the user's age, gender, interests, level of understanding of sports, and the teams or players they support. This information is evaluated based on survey results and past viewing history. Step 2: The analysis unit analyzes the video based on the information collected by the collection unit. The analysis unit can analyze the video using image recognition technology and motion analysis technology. Furthermore, it can input the collected information using a generation AI and perform video analysis. Step 3: The generation unit generates commentary audio based on the video analyzed by the analysis unit. The generation unit can generate commentary audio using speech synthesis technology or template-based generation technology. Furthermore, it can take the video analyzed using the generation AI as input and perform commentary audio generation. Step 4: The providing unit provides the audio generated by the generating unit. The providing unit can provide the generated audio using streaming or downloading technologies. Furthermore, it can input the generated audio into an AI model to determine how to provide the audio.

[0062] (Example of form 2) The sports commentary system according to an embodiment of the present invention is a system that performs sports commentary using generative AI. The sports commentary system can improve viewer satisfaction, which has been dependent on the announcer's knowledge and expressive abilities in conventional sports commentary. In particular, the viewing experience was sometimes detrimental due to the announcer's lack of knowledge or extreme favoritism towards one team, but by using generative AI, real-time video analysis can be performed, and commentary can be provided that is tailored to the needs of each individual viewer. First, the user inputs information about their level of understanding of the sport and the team and players they support. For example, if the user is knowledgeable about baseball, detailed explanations such as how the catcher attacks will be provided, and if the user is not knowledgeable about baseball, an explanation including the rules will be provided. It is also possible to provide commentary that is specialized for the team or players the user supports. For example, when a player who is not playing in the game appears on camera, the system can provide commentary on that player's actions. Next, the generative AI analyzes the game video in real time and generates commentary audio that is tailored to the user's requests. The generated audio can be adjusted in terms of the level of enthusiasm and the amount of trivia to suit the user's preferences. For example, a user who is a fan of a particular team and prefers high-energy commentary can be provided with such commentary. Furthermore, because the generating AI can analyze multiple camera feeds simultaneously, it can pick up on subtle movements during a match, as well as seemingly unrelated actions. This allows viewers to enjoy the match from a more multifaceted perspective. This system elevates the sports viewing experience to the next level, providing rich and personalized sports commentary for each viewer. As a result, the sports commentary system can improve viewer satisfaction and provide a more fulfilling sports viewing experience.

[0063] The sports commentary system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user information. For example, the collection unit can collect information such as the user's age, gender, and interests. The collection unit can also collect information about the user's understanding of sports and the teams and players they support. For example, the collection unit can evaluate the user's understanding of sports based on survey results and past viewing history. The analysis unit analyzes the video based on the information collected by the collection unit. For example, the analysis unit can analyze the video using image recognition technology or motion analysis technology. The generation unit generates commentary audio based on the video analyzed by the analysis unit. For example, the generation unit can generate commentary audio using speech synthesis technology or template-based generation technology. The provision unit provides the audio generated by the generation unit. For example, the provision unit can provide the generated audio using streaming technology or download technology. As a result, the sports commentary system according to this embodiment can provide sports commentary in real time based on user information. The elements of the collection unit, analysis unit, generation unit, and provision unit are sequentially related. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user information into an AI model, and the AI ​​model can evaluate the user's understanding of sports, the team they support, and information about the players. Some or all of the processing described above in the analysis unit may be performed using a generation AI. For example, the analysis unit can input the information collected by the data collection unit into a generation AI, and have the generation AI perform video analysis. Some or all of the processing described above in the generation unit may be performed using a generation AI. For example, the generation unit can input the video analyzed by the analysis unit into a generation AI, and have the generation AI perform commentary audio generation. Some or all of the processing described above in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can input the audio generated by the generation unit into an AI model, and the AI ​​model can determine how to provide the audio.

[0064] The data collection unit collects user information. For example, the data collection unit can collect information such as the user's age, gender, and interests. Specifically, it collects basic information entered by the user when registering with the system, as well as detailed interest data obtained through questionnaires. The data collection unit can also collect information about the user's understanding of sports and the teams and players they support. For example, the data collection unit can evaluate the user's understanding of sports based on questionnaire results and past viewing history. Furthermore, the data collection unit can analyze the user's social media posts and online activity history to collect information about the user's interests and the teams and players they support. This allows the data collection unit to create detailed user profiles and collect basic data to provide optimal sports commentary to individual users. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input user information into an AI model and have the AI ​​model evaluate the user's understanding of sports and the teams and players they support. The AI ​​model can learn from past data and predict user behavior patterns and interests with high accuracy. This allows the data collection unit to efficiently and accurately collect user information, thereby improving the overall system performance.

[0065] The analysis unit analyzes video based on information collected by the data collection unit. The analysis unit can analyze video using, for example, image recognition technology or motion analysis technology. Specifically, it can analyze sports match footage in real time to understand player movements, ball position, and the progress of the match. Based on collected user information, the analysis unit can focus on specific players or plays that the user is interested in. For example, it can prioritize the analysis of the team or player the user supports and provide commentary based on the results. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input information collected by the data collection unit into the generative AI and have the generative AI perform the video analysis. The generative AI can extract features from the video using a deep learning model and analyze player movements and the progress of the match with high accuracy. This allows the analysis unit to quickly and accurately analyze the collected data and generate basic information to provide users with optimal sports commentary. Furthermore, the analysis unit can utilize past match data and statistical information to evaluate match trends and player performance, and predict future match developments. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term match analysis and predictions, thereby improving the reliability and accuracy of the entire system.

[0066] The generation unit generates commentary audio based on the video analyzed by the analysis unit. The generation unit can generate commentary audio using, for example, speech synthesis technology or template-based generation technology. Specifically, it automatically generates appropriate commentary based on data on the progress of the match and the movements of the players provided by the analysis unit. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the video analyzed by the analysis unit into the generation AI and have the generation AI perform the generation of commentary audio. The generation AI uses natural language processing technology to generate appropriate commentary according to the situation of the match and provides it to the user in real time. The generation AI can adjust the content and tone of the commentary according to the user's interests and level of understanding. For example, it can provide detailed explanations of basic rules and player introductions for beginners, and detailed explanations of tactics and player techniques for advanced users. In this way, the generation unit can provide customized commentary that meets the user's needs. Furthermore, the generation unit can also utilize past match data and statistical information to evaluate match trends and player performance and predict future match developments. This allows the generation unit to handle not only real-time commentary but also long-term match analysis and predictions, improving the overall reliability and accuracy of the system.

[0067] The service provider provides the audio generated by the generation unit. The service provider can provide audio generated using, for example, streaming or downloading technologies. Specifically, if a user is watching a match in real time, the service provider can deliver commentary audio in real time using streaming technology. If a user watches the match later, the service provider can provide commentary audio using downloading technology. Some or all of the above processing in the service provider may be performed using, for example, AI, or not. For example, the service provider can input the audio generated by the generation unit into an AI model, and the AI ​​model can determine the method of audio delivery. The AI ​​model can select the optimal delivery method considering the user's viewing environment and network conditions. This allows the service provider to provide commentary audio to the user in the most optimal form. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the delivery method. For example, it can review and improve the delivery method based on the user's viewing history and feedback. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only streaming but also voice calls, SMS, email, etc. This allows the service provider to deliver commentary audio to users quickly and reliably, improving the overall reliability of the system and user satisfaction.

[0068] The analysis unit can analyze multiple camera feeds simultaneously. For example, it can simultaneously analyze footage from different angles or at different resolutions. For example, the analysis unit can analyze multiple camera feeds in real time to provide detailed information about the match. The analysis unit can also integrate and analyze multiple camera feeds. For example, it can integrate different camera feeds to grasp the overall picture of the match. This allows for the provision of detailed information about the match by simultaneously analyzing multiple camera feeds. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input multiple camera feeds into the generation AI and have the generation AI perform the video analysis.

[0069] The data collection unit can collect information about the user's understanding of sports and the teams and players they support. For example, the data collection unit can evaluate the user's understanding of sports based on survey results and past viewing history. The data collection unit can also collect information about the teams and players the user supports based on social media following information and past viewing history. For example, the data collection unit can collect information about the teams and players the user follows and identify the teams and players the user supports. This allows for more personalized commentary by collecting information about the user's understanding of sports and the teams and players they support. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input user information into an AI model and have the AI ​​model evaluate the user's understanding of sports and the teams and players they support.

[0070] The generation unit can adjust the level of enthusiasm and the amount of trivia to suit the user's preferences. For example, the generation unit can adjust the level of enthusiasm by adjusting the intonation and speaking speed of the voice. It can also adjust the amount of trivia by adjusting the frequency and type of information. For example, the generation unit can provide commentary with high enthusiasm or commentary with lots of trivia to suit the user's preferences. By adjusting the level of enthusiasm and the amount of trivia in the commentary to suit the user's preferences, a more satisfying commentary can be provided. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input information about the user's preferences into the generation AI and have the generation AI generate the commentary audio.

[0071] The service provider can provide the generated audio in real time. For example, the service provider can provide the generated audio in real time using streaming technology. The service provider can also set an acceptable latency range and provide audio in real time. For example, the service provider can provide viewers with delay-free commentary using low-latency streaming technology. This allows for delay-free commentary to be provided to viewers by providing the generated audio in real time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated audio into an AI model and the AI ​​model can determine how to provide the audio.

[0072] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize collecting information about the team or player they are supporting. If the user is relaxed, the data collection unit can prioritize collecting information about the overall flow and tactics of the match. Furthermore, if the user is nervous, the data collection unit can prioritize collecting information about important moments and scoring scenes of the match. This allows for the collection of more relevant information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The data collection unit can analyze the user's past viewing history and select the optimal information collection method. For example, the data collection unit can collect information about matches and players that the user might be interested in, based on data from matches the user has watched in the past. The data collection unit can also analyze trends in matches the user has watched in the past and prioritize the collection of information about similar matches and situations. Furthermore, the data collection unit can collect information about highly-rated matches and players based on the user's ratings of matches the user has watched in the past. In this way, by analyzing the user's past viewing history, more information that is likely to be of interest can be collected. 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 the user's viewing history data into an AI model and have the AI ​​model select the optimal information collection method.

[0074] The data collection unit can filter information based on the user's current areas of interest and sports trends. For example, the data collection unit can prioritize collecting information on sports and athletes that the user is currently interested in. It can also prioritize collecting information on current sports trends and trending matches. Furthermore, the data collection unit can collect information on specific playing styles and tactics that the user is interested in. This allows for the provision of more relevant information by filtering information based on the user's current areas of interest and sports trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input user areas of interest and sports trend data into an AI model and have the AI ​​model perform the information filtering.

[0075] The data collection unit can estimate the user's emotions and adjust the level of detail of the information it collects based on the estimated emotions. For example, if the user is excited, the data collection unit can collect detailed play commentary and background information on players. If the user is relaxed, the data collection unit can also collect information about the overall flow of the match and tactics. Furthermore, if the user is tense, the data collection unit can collect information about important moments and scoring scenes in the match. By adjusting the level of detail of the information based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user lives in a specific region, the data collection unit can prioritize the collection of information about teams and players in that region. Furthermore, if the user is traveling, the data collection unit can collect information about matches and events taking place in the region they are visiting. Additionally, if the user is interested in sports in a specific region, the data collection unit can collect information about sports in that region. This allows for the provision of more relevant information by considering the user's geographical location. 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 the user's geographical location into an AI model, and the AI ​​model can select highly relevant information.

[0077] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can prioritize collecting information about teams and players that the user follows on social media. It can also collect information about matches and events that the user has shared on social media. Furthermore, the data collection unit can collect information related to topics that the user has shown interest in on social media. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's social media data into an AI model and collect relevant information using the AI ​​model.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is excited, the analysis unit can highlight and analyze the highlight scenes and important plays of the match. If the user is relaxed, the analysis unit can analyze the overall flow of the match and tactics. Furthermore, if the user is tense, the analysis unit can analyze important moments and scoring scenes of the match. By adjusting the analysis criteria based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis criteria.

[0079] The analysis unit can improve the accuracy of its analysis by considering the progress of the match and important moments during the analysis. For example, the analysis unit can adjust the accuracy of the analysis in real time according to the progress of the match. The analysis unit can also perform detailed analysis on important moments and scoring scenes. Furthermore, the analysis unit can improve the accuracy of the analysis based on the flow of the match and tactics. As a result, by considering the progress of the match and important moments, it can provide more accurate analysis results. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input match progress data into the generative AI and have the generative AI perform the adjustment of the analysis accuracy.

[0080] The analysis unit can apply different analysis algorithms based on the rules and tactics of the match during analysis. For example, the analysis unit can select an appropriate analysis algorithm based on the rules of the match. It can also apply different analysis algorithms based on the tactics of the match. Furthermore, the analysis unit can select the optimal analysis algorithm according to the characteristics of the match. This allows for the provision of more appropriate analysis results by applying analysis algorithms based on the rules and tactics of the match. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input match rules and tactical data into the generative AI and have the generative AI select the analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can provide a visually stimulating display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is tense, the analysis unit can provide a simple and highly visible display method. By adjusting the display method of the analysis results based on the user's emotions, a more visually appealing display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0082] The analysis unit can perform analysis while considering the geographical background and cultural elements of the match. For example, the analysis unit can perform an appropriate analysis based on the geographical background of the match. Furthermore, the analysis unit can improve the accuracy of the analysis by considering the cultural elements of the match. In addition, the analysis unit can select the optimal analysis method according to the characteristics of the match. This allows for the provision of more appropriate analysis results by considering the geographical background and cultural elements of the match. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input data on the geographical background and cultural elements of the match into the generative AI and have the generative AI perform improvements to the accuracy of the analysis.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data on the match during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the match. Furthermore, the analysis unit can perform a detailed analysis based on the match data. In addition, the analysis unit can select the optimal analysis method according to the characteristics of the match. This allows for the provision of more accurate analysis results by referring to relevant literature and data on the match. Some or all of the above-mentioned processes in the analysis unit are performed using a generating AI. For example, the analysis unit can input relevant literature and data on the match into the generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0084] The generation unit can estimate the user's emotions and adjust the tone and tension of the generated voice based on the estimated emotions. For example, if the user is excited, the generation unit can generate a high-tension voice. If the user is relaxed, the generation unit can generate a calm voice. Furthermore, if the user is tense, the generation unit can generate a reassuring voice. By adjusting the tone and tension of the voice based on the user's emotions, a more satisfying voice can be provided. 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 is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the tone and tension of the voice.

[0085] The generation unit can adjust the level of detail in the audio based on the importance of the match and the user's level of interest during generation. For example, if the match is of high importance, the generation unit can generate audio that includes detailed commentary. Similarly, if the user's level of interest is high, the generation unit can generate audio that includes detailed information. Furthermore, the generation unit can select the optimal audio generation method according to the characteristics of the match. This allows for the provision of more appropriate audio by adjusting the level of detail based on the importance of the match and the user's level of interest. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the importance of the match and the user's level of interest into the generation AI, and have the generation AI perform the adjustment of the level of detail in the audio.

[0086] The generation unit can apply different voice generation algorithms during generation depending on the category and characteristics of the match. For example, the generation unit can select an appropriate voice generation algorithm based on the category of the match. It can also apply different voice generation algorithms depending on the characteristics of the match. Furthermore, the generation unit can select the optimal voice generation method depending on the characteristics of the match. This allows for the provision of more appropriate voices by applying voice generation algorithms according to the category and characteristics of the match. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input match category and characteristic data into the generation AI and have the generation AI select a voice generation algorithm.

[0087] The generation unit can estimate the user's emotions and adjust the length of the generated audio based on the estimated emotions. For example, if the user is excited, the generation unit can generate a longer audio with detailed explanations. Similarly, if the user is relaxed, it can generate an audio with detailed information. Furthermore, if the user is tense, it can generate a shorter, more concise audio. This allows for the provision of more appropriate audio by adjusting the length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, 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 generation unit are performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the audio.

[0088] The generation unit can determine audio priorities based on the progress of the match during generation. For example, the generation unit can adjust audio priorities in real time according to the progress of the match. The generation unit can also generate audio that includes detailed commentary for important moments and scoring scenes. Furthermore, the generation unit can determine audio priorities based on the flow of the match and tactics. This allows for the provision of more appropriate audio by determining audio priorities based on the progress of the match. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input match progress data into the generation AI and have the generation AI perform the determination of audio priorities.

[0089] The generation unit can adjust the order of audio based on the relevance of the matches during generation. For example, the generation unit can select an appropriate order of audio based on the relevance of the matches. The generation unit can also adjust the order of audio according to the characteristics of the matches. Furthermore, the generation unit can select the optimal audio generation method according to the characteristics of the matches. This allows for the provision of more appropriate audio by adjusting the order of audio based on the relevance of the matches. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input match relevance data into the generation AI and have the generation AI perform the adjustment of the order of audio.

[0090] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is excited, the delivery unit can provide a high-energy voice. If the user is relaxed, the delivery unit can provide a calm tone of voice. Furthermore, if the user is tense, the delivery unit can provide a reassuring tone of voice. In this way, by adjusting the delivery method based on the user's emotions, a more appropriate voice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the delivery method of the voice.

[0091] The service provider can select the optimal delivery method by referring to the user's past viewing history at the time of delivery. For example, the service provider can provide audio about matches or players that the user might be interested in, based on data from matches the user has watched in the past. The service provider can also analyze trends in matches the user has watched in the past and prioritize providing audio about similar matches or situations. Furthermore, the service provider can provide audio about matches or players with high ratings, based on the user's ratings of matches the user has watched in the past. This allows for the provision of more appropriate audio by referring to the user's past viewing history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's viewing history data into an AI model and have the AI ​​model select the optimal delivery method.

[0092] The delivery unit can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the delivery unit can provide an audio delivery method that is adapted to the screen size. If the user is using a tablet, the delivery unit can also provide an audio delivery method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the delivery unit can provide a concise and highly visible audio delivery method. This allows for the provision of more appropriate audio by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into an AI model and have the AI ​​model select the optimal delivery method.

[0093] The delivery unit can estimate the user's emotions and adjust the timing of audio delivery based on the estimated emotions. For example, if the user is excited, the delivery unit can provide audio in real time. If the user is relaxed, the delivery unit can also provide audio in accordance with the flow of the game. Furthermore, if the user is nervous, the delivery unit can provide audio at important moments or scoring scenes. In this way, by adjusting the timing of audio delivery based on the user's emotions, more appropriate audio can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the timing of audio delivery.

[0094] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user lives in a specific region, the service provider can prioritize providing audio related to teams and players in that region. Furthermore, if the user is traveling, the service provider can provide audio related to matches and events taking place in the region they are visiting. Additionally, if the user is interested in sports in a specific region, the service provider can provide audio related to sports in that region. This allows for the provision of more appropriate audio by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into an AI model and have the AI ​​model select the optimal delivery method.

[0095] The delivery unit can analyze the user's social media activity at the time of delivery to select the optimal delivery method. For example, the delivery unit can prioritize providing audio related to teams or players that the user follows on social media. It can also provide audio related to matches or events that the user has shared on social media. Furthermore, the delivery unit can provide audio related to topics that the user has shown interest in on social media. This allows for the provision of more appropriate audio by analyzing the user's social media activity. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's social media data into an AI model and have the AI ​​model select the optimal delivery method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] Sports commentary systems can be equipped with features that automatically adjust the volume and sound quality of the audio according to the user's viewing environment. For example, if the user is using headphones, the system can emphasize high and low frequencies to enhance sound quality. If the user is using speakers, the system can automatically adjust the volume and minimize echo. Furthermore, if the user is in a noisy environment, noise-canceling technology can be used to suppress background noise and provide clear commentary audio. This allows for a more comfortable viewing experience by providing audio optimized for the user's viewing environment.

[0098] Sports commentary systems can estimate the user's emotions and adjust the timing of video transitions based on those estimates. For example, if the user is excited, the system can frequently switch camera angles to highlight important plays or scoring moments. Conversely, if the user is relaxed, a wide-angle camera angle can be maintained for extended periods to help them grasp the overall flow of the game. Furthermore, if the user is tense, the system can provide camera angles focused on specific players or plays to aid visual concentration. By switching video in accordance with the user's emotions, a greater sense of immersion can be achieved.

[0099] A sports commentary system can be equipped with a function to automatically generate highlight scenes from matches based on the user's past viewing history. For example, it can analyze data from matches the user has watched in the past and extract plays and scoring scenes that are likely to be of particular interest. Furthermore, if the user is interested in a particular player or team, the system can prioritize generating highlight scenes related to that player or team. It can also generate highlights that include similar situations based on match scenes that the user has previously rated highly. In this way, the system can improve the viewing experience by providing highlight scenes tailored to the user's interests.

[0100] Sports commentary systems can estimate the user's emotions and adjust the commentary's tension and tone in real time based on those estimates. For example, if the user is excited, the commentator can increase the tension and provide commentary in an energetic tone. If the user is relaxed, the commentator can provide commentary in a calm tone, explaining the flow of the game. Furthermore, if the user is nervous, the commentator can provide reassuring and calming commentary. This allows for a more personalized viewing experience by providing commentary that responds to the user's emotions.

[0101] A sports commentary system can provide information about region-specific sports events and athletes based on the user's geographical location. For example, if a user lives in a specific region, it can prioritize providing information about matches and events taking place in that region. If the user is traveling, it can also provide information about sports events and local athletes in their destination region. Furthermore, if the user is interested in sports in a particular region, it can provide the latest information and trends related to those sports. This allows for the provision of more relevant content by basing information on the user's geographical location.

[0102] Sports commentary systems can be equipped with features that estimate the user's emotions and highlight important moments of the game based on those emotions. For example, if the user is excited, scoring scenes and highlight scenes can be highlighted. If the user is relaxed, important moments can be moderately highlighted to help them grasp the overall flow of the game. Furthermore, if the user is tense, the system can help them concentrate visually by highlighting the climax and decisive moments of the game. In this way, the viewing experience can be improved by highlighting important moments of the game according to the user's emotions.

[0103] A sports commentary system can be equipped with the ability to analyze users' social media activity and provide relevant information in real time. For example, it can provide the latest information on teams and players that users follow on social media. It can also provide real-time information on matches and events that users have shared on social media. Furthermore, it can provide information related to topics that users have shown interest in on social media. This allows for the provision of more relevant content by providing information based on users' social media activity.

[0104] A sports commentary system can estimate the user's emotions and adjust the commentary based on those emotions. For example, if the user is excited, it can provide detailed play analysis and background information on the players. If the user is relaxed, it can provide commentary on the overall flow of the game and tactics. Furthermore, if the user is tense, it can provide commentary on important moments and scoring scenes of the game. By providing commentary tailored to the user's emotions, it can deliver more relevant information.

[0105] Sports commentary systems can be equipped with a function that automatically adjusts the optimal video quality and resolution based on the user's device information. For example, if the user is using a smartphone, the video resolution can be automatically adjusted to conserve data usage. If the user is using a tablet, the system can provide high-resolution video optimized for the large screen. Furthermore, if the user is using a smart TV, it is possible to provide the highest quality video to create an immersive viewing experience. In this way, adjusting the video quality based on the user's device information can provide a more comfortable viewing experience.

[0106] A sports commentary system can have the ability to estimate the user's emotions and provide replays of the game based on those emotions. For example, if the user is excited, it can frequently provide replays of scoring plays and highlights. If the user is relaxed, it can provide replays of important moments at appropriate intervals to help them grasp the overall flow of the game. Furthermore, if the user is tense, it can help them concentrate visually by providing replays of the game's climax and decisive moments. In this way, the viewing experience can be improved by providing replays that match the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects user information. For example, the data collection unit can collect information such as the user's age, gender, interests, level of understanding of sports, and the teams or players they support. This information is evaluated based on survey results and past viewing history. Step 2: The analysis unit analyzes the video based on the information collected by the collection unit. The analysis unit can analyze the video using image recognition technology and motion analysis technology. Furthermore, it can input the collected information using a generation AI and perform video analysis. Step 3: The generation unit generates commentary audio based on the video analyzed by the analysis unit. The generation unit can generate commentary audio using speech synthesis technology or template-based generation technology. Furthermore, it can take the video analyzed using the generation AI as input and perform commentary audio generation. Step 4: The providing unit provides the audio generated by the generating unit. The providing unit can provide the generated audio using streaming or downloading technologies. Furthermore, it can input the generated audio into an AI model to determine how to provide the audio.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and processes it with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing device 12 and analyzes the video based on the collected information. The generation unit is implemented in the specific processing unit 290 of the data processing device 12 and generates commentary audio based on the analyzed video. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the generated audio to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and processes it with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the video based on the collected information. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates commentary audio based on the analyzed video. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the generated audio to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314 and processes it with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the video based on the collected information. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates commentary audio based on the analyzed video. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the generated audio to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and processes it with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the video based on the collected information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates commentary audio based on the analyzed video. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated audio to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A collection unit that collects user information, An analysis unit analyzes the video based on the information collected by the aforementioned collection unit, A generation unit generates commentary audio based on the video analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the sound generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyzes images from multiple cameras simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect information about users' understanding of sports, the teams they support, and the players they like. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The level of tension and the amount of trivia can be adjusted to suit the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides generated audio in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past viewing history and select the optimal method for gathering information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current areas of interest and sports trends. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the level of detail of the information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by considering the progress of the match and important moments. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied based on the rules and tactics of the match. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the geographical background and cultural elements of the match will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, we refer to relevant literature and data on the match to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the tone and tension of the generated voice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the level of detail in the audio is adjusted based on the importance of the match and the user's level of interest. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, different voice generation algorithms are applied depending on the category and characteristics of the match. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the generated audio based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the priority of audio is determined based on the progress of the match. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the order of audio is adjusted based on the relevance of the matches. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way it delivers audio based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing content, the system will refer to the user's past viewing history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of voice delivery based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, At the time of delivery, we analyze the user's social media activity to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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. A collection unit that collects user information, An analysis unit analyzes the video based on the information collected by the aforementioned collection unit, A generation unit generates commentary audio based on the video analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the sound generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyzes images from multiple cameras simultaneously. The system according to feature 1.

3. The aforementioned collection unit is Collect information about users' understanding of sports, the teams they support, and the players they like. The system according to feature 1.

4. The generating unit is The level of tension and the amount of trivia can be adjusted to suit the user's preferences. The system according to feature 1.

5. The aforementioned supply unit is, Provides generated audio in real time. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past viewing history and select the optimal method for gathering information. The system according to feature 1.

8. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current areas of interest and sports trends. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and adjusts the level of detail of the information collected based on those estimated emotions. The system according to feature 1.

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