system

Abstract

The system according to this embodiment aims to reduce man-hours and costs by automatically generating highlight videos of matches. [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 video data of the match. The analysis unit analyzes the video data collected by the collection unit and extracts important scenes. The generation unit combines the scenes extracted by the analysis unit to generate a highlight video. The provision unit provides the highlight video generated by the generation unit.

Description

Technical Field

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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, there was a problem that it took a lot of man-hours and costs to create a highlight video of a game. <00,00026>

[0005] The system according to the embodiment aims to automatically generate a highlight video of a game and reduce man-hours and costs.

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 video data of the match. The analysis unit analyzes the video data collected by the collection unit and extracts important scenes. The generation unit combines the scenes extracted by the analysis unit to generate a highlight video. The provision unit provides the highlight video generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate match highlight videos, reducing man-hours and costs. [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, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that will be incorporated into "Basketball LIVE," which provides B.LEAGUE game videos and player footage. This system will automatically generate game highlights and provide a function to create original highlight videos that focus on the player's favorite player. This will reduce human labor and costs and improve fan satisfaction. First, the system will analyze the game video data and extract important scenes. For example, scoring scenes and fine plays will be included. Next, the extracted scenes will be combined to automatically generate a highlight video. At this time, humans only need to make minor adjustments using prompts, so labor and costs can be significantly reduced. Furthermore, the system can analyze the unstructured data of the video and create original highlight videos that focus on specific players. For example, it can generate a video that collects scenes where a specific player scores or performs well. As a result, fans can enjoy highlight videos that feature their favorite player throughout, and it is expected that the number of Basketball LIVE users will increase. This AI agent system can also be applied to other sports, security cameras, and the security industry, aiming to realize a world where unstructured data can be easily handled. This allows the AI ​​agent system to efficiently collect, analyze, generate, and provide video data from matches.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video data of matches. The collection unit can collect video data of matches using, for example, cameras or sensors. The collection unit can also collect video data of matches in real time. The collection unit can also automatically collect scenes in which a particular player performs well by referring to past match data. The analysis unit analyzes the video data collected by the collection unit and extracts important scenes. The analysis unit can extract important scenes such as scoring scenes and fine plays. The analysis unit can also dynamically change the criteria for extracting important scenes, taking into account the situation and context of the match. The analysis unit can also estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. The generation unit combines the scenes extracted by the analysis unit to generate a highlight video. The generation unit can, for example, make fine adjustments using prompts. The generation unit can also estimate the user's emotions and adjust the content of the highlight video it generates based on the estimated user emotions. The generation unit can also perform editing to give a storyline to specific players or teams when generating highlight videos. The delivery unit provides the highlight videos generated by the generation unit. The delivery unit can, for example, create original highlight videos that focus on specific players. The delivery unit can also estimate the user's emotions and adjust how the provided highlight videos are displayed based on the estimated user emotions. The delivery unit can also be equipped with functions that allow for horizontal expansion to other sports, security cameras, and the security industry. As a result, the AI ​​agent system according to the embodiment can efficiently collect, analyze, generate, and provide video data of matches.

[0030] The data collection unit collects video data from matches. For example, the unit can collect match video data using cameras and sensors. Specifically, it uses high-resolution cameras installed in stadiums and arenas, and motion sensors to track player movements. These devices collect data in real time as the match progresses and transmit it to a central database. The data collection unit can also collect match video data in real time. This allows for immediate understanding of the match's progress and ensures that important scenes are recorded without being missed. Furthermore, the data collection unit can automatically collect scenes where specific players perform well by referencing past match data. For example, it can analyze past match data, extract scenes where a specific player scored a goal or made a great play, and save these scenes in the database. This allows the data collection unit to collect richer data by utilizing not only current match data but also past match data. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Additionally, adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes video data collected by the collection unit and extracts important scenes. For example, the analysis unit can extract important scenes such as scoring plays and spectacular plays. Specifically, it utilizes AI-based image recognition technology to analyze player movements and ball positions from video data. This allows for the automatic detection of important scenes such as scoring plays and spectacular plays. The analysis unit can also dynamically change the criteria for extracting important scenes, taking into account the match situation and context. For example, it adjusts the criteria for extracting important scenes in real time, taking into account the progress of the match, the score, and player performance. This allows the analysis unit to perform flexible analysis according to the match situation. Furthermore, the analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is excited, it will extract more scoring plays and spectacular plays; if the user is calm, it will extract scenes that emphasize the flow of the match. This allows the analysis unit to perform optimal analysis according to the user's emotions. The analysis unit can also utilize historical data and statistical information to perform long-term trend analysis and performance evaluation. For example, the analysis of fluctuations in the performance of specific players or teams can be used to predict future performance and formulate strategies. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term performance evaluation and strategy formulation, improving the reliability and usefulness of the entire system.

[0032] The generation unit generates a highlight video by combining scenes extracted by the analysis unit. The generation unit can, for example, make fine adjustments via prompts. Specifically, it can customize the content of the highlight video according to the scenes, players, and match situations desired by the user. The generation unit can also estimate the user's emotions and adjust the content of the generated highlight video based on the estimated emotions. For example, if the user is excited, it will generate a highlight video that includes more scoring scenes and great plays, while if the user is calm, it will generate a highlight video that emphasizes the flow of the match. In this way, the generation unit can provide the optimal highlight video according to the user's emotions. The generation unit can also edit the highlight video to give it a storyline for specific players or teams. For example, it can edit to focus on the highlight scenes of a particular player and emphasize that player's story. It can also add scenes that explain the strategies and tactics of a particular team to deepen the understanding of the match. In this way, the generation unit can provide not just a simple highlight video, but also story-driven content that matches the user's interests and concerns. Furthermore, the generation unit can evaluate the quality of the generated highlight video and regenerate or modify it as needed. This allows the generation unit to consistently provide high-quality highlight videos, thereby improving user satisfaction.

[0033] The service provider provides highlight videos generated by the generation unit. For example, the service provider can create original highlight videos focusing on specific players. Specifically, when a user selects a particular player, it provides a highlight video edited to highlight that player's performance. The service provider can also estimate the user's emotions and adjust the display method of the highlight video based on those emotions. For example, if the user is excited, it provides a highlight video with more dynamic effects and presentation; if the user is calm, it provides a simpler, more subdued highlight video. This allows the service provider to provide the optimal display method according to the user's emotions. The service provider can also incorporate features for application to other sports, security cameras, and the security industry. For example, adding functions to collect, analyze, generate, and provide match data from other sports can cater to a wider range of sports fans. Furthermore, in the security camera and security industry, it can support efficient monitoring and rapid response by extracting important scenes and generating and providing highlight videos. This allows the service provider to offer a flexible system applicable not only to the sports field but also to other fields. Additionally, the service provider can collect user feedback and continuously improve the quality and display method of the content it provides. This allows the service provider to consistently deliver optimal content tailored to user needs, thereby improving the overall reliability and satisfaction of the system.

[0034] The service provider can create original highlight videos that focus on specific players. For example, the service provider can generate videos that compile scenes of a specific player scoring goals or performing well. The service provider can also include interviews and training footage of the specific player. The service provider can also collect post-match comments and impressions from a specific player and incorporate them into the highlight video. This allows for increased fan satisfaction by providing original highlight videos that focus on specific players. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input data on a specific player into a generating AI and have the generating AI generate an original highlight video.

[0035] The data collection unit can collect information about players outside of matches. For example, it can collect information about players' training, interviews, and daily lives. The data collection unit can also collect information about players' pre-match warm-ups and post-match cool-downs. The data collection unit can also collect information about players' off-day activities and hobbies. By collecting information about players outside of matches, a variety of content can be provided to fans. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can take pictures of players with a camera, input that data into a generating AI, and have the generating AI perform the analysis.

[0036] The analysis unit can extract at least one important scene from among scoring scenes and fine plays. The analysis unit can extract based, for example, on the type of scoring scene and the importance of the goal. The analysis unit can also extract based on the type of fine play and the difficulty of the play. The analysis unit can also include players' emotional expressions and spectator reactions in its analysis. The analysis unit can also include players' tactical movements and team coordination in its analysis. This allows for the improvement of the quality of the highlight video by extracting important scenes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match video data into a generating AI and have the generating AI perform the extraction of important scenes.

[0037] The generation unit can perform fine-tuning using prompts. For example, the generation unit can use prompts to adjust the order and length of scenes in the highlight video. The generation unit can also use prompts to prioritize the inclusion of specific scenes or players. The generation unit can also use prompts to customize the content of the highlight video. This allows for improved accuracy of the highlight video through fine-tuning using prompts. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input prompts into a generation AI and have the generation AI perform the fine-tuning.

[0038] The service provider can be equipped with functions based on horizontal expansion to other sports or the security camera and security industry. For example, the service provider can add a function to provide highlight videos of other sports events. The service provider can also add a function to analyze security camera footage and provide important scenes. The service provider can also add a function to extract and provide important scenes for the security industry. This allows the service provider to accommodate a wide range of applications by incorporating functions that consider horizontal expansion to other sports, security cameras, and the security industry. 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 data from other sports events into a generating AI and have the generating AI perform the generation of highlight videos.

[0039] The data collection unit can prioritize the collection of important scenes in real time according to the progress of the match. For example, the data collection unit can collect the climax scenes of the match in real time. The data collection unit can also prioritize the collection of important plays immediately after the start of the match. The data collection unit can also collect decisive scenes near the end of the match in real time. This enables real-time content delivery by prioritizing the collection of important scenes according to the progress of the match. 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 match progress data into a generating AI and have the generating AI perform the collection of important scenes.

[0040] The data collection unit can automatically collect scenes where a specific player performs well by referring to past match data. For example, the data collection unit can automatically collect scoring scenes of players who were the top scorers in past matches. The data collection unit can also automatically collect scenes of players who made a series of spectacular plays in past matches. The data collection unit can also automatically collect highlights of players who were selected as MVP in past matches. This allows for the provision of engaging content for fans by collecting scenes where a specific player performs well by referring to past match data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past match data into a generating AI and have the generating AI collect scenes where a specific player performs well.

[0041] The data collection unit can include player interviews and practice footage when collecting information about players outside of matches. For example, the data collection unit can collect post-match interviews with players. The data collection unit can also collect footage of players practicing. The data collection unit can also collect information about players on their days off. By collecting information about players outside of matches, a variety of content can be provided to fans. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data from player interviews and practice footage into a generating AI and have the generating AI perform the analysis.

[0042] The data collection unit can also include video data from other sports and entertainment events in its collection targets. For example, the data collection unit can collect highlight scenes from other sports events. The data collection unit can also collect performance scenes from entertainment events. The data collection unit can also collect athlete interviews from other sports events. This allows for the provision of a wide range of content by including video data from other sports and entertainment events in the collection targets. 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 data from other sports and entertainment events into a generating AI and have the generating AI perform the collection.

[0043] The analysis unit can analyze not only scoring scenes and spectacular plays, but also players' tactical movements and teamwork. For example, the analysis unit can analyze players' tactical movements. The analysis unit can also analyze teamwork. The analysis unit can also analyze players' positioning. By including players' tactical movements and teamwork in the analysis, it is possible to provide more detailed analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match video data into a generating AI and have the generating AI perform the analysis of tactical movements and teamwork.

[0044] The analysis unit can dynamically change the criteria for extracting important scenes based on the match situation and context. For example, the analysis unit can extract the climax of the match as an important scene. It can also extract important plays immediately after the start of the match. It can also extract decisive scenes near the end of the match. By dynamically changing the criteria for extracting important scenes, taking into account the match situation and context, it is possible to generate more appropriate highlight videos. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match situation data into a generating AI and have the generating AI perform the changes to the criteria for extracting important scenes.

[0045] The analysis unit can include, in addition to analyzing scoring scenes and spectacular plays, the emotional expressions of players and the reactions of spectators in its analysis. For example, the analysis unit can analyze the emotional expressions of players after scoring a goal. The analysis unit can also analyze the reactions of spectators. The analysis unit can also analyze the emotional expressions of players during a match. By including the emotional expressions of players and the reactions of spectators in the analysis, more detailed analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match video data into a generating AI and have the generating AI perform the analysis of emotional expressions and spectator reactions.

[0046] The analysis unit can be equipped with the ability to analyze video data from other sports and entertainment events. For example, the analysis unit can analyze scoring scenes from other sports events. It can also analyze performance scenes from entertainment events. It can also analyze athlete interviews from other sports events. By adding the ability to analyze video data from other sports and entertainment events, it can be used for a wide range of applications. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from other sports or entertainment events into a generating AI and have the generating AI perform the analysis.

[0047] The generation unit can edit the highlight video to give it a narrative structure, focusing on specific players or teams. For example, it can edit the video to focus on a specific player's scoring plays. It can also edit the video to focus on a team's coordinated plays. It can also edit the flow of the match to give it a narrative structure. By editing the video to give it a narrative structure, it is possible to attract viewers' interest and provide a more engaging highlight video. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input data on specific players or teams into a generation AI and have the generation AI perform the editing to give it a narrative structure.

[0048] The generation unit can provide diverse visual experiences by combining different viewpoints and camera angles when generating highlight videos. For example, the generation unit can combine scoring scenes from different camera angles. It can also combine spectacular plays from different viewpoints. It can also combine emotional expressions of players from different camera angles. In this way, by combining different viewpoints and camera angles, it can provide viewers with diverse visual experiences. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on different viewpoints and camera angles into a generation AI and have the generation AI perform the generation of highlight videos.

[0049] The generation unit can prioritize including specific scenes or players specified by the user when making fine adjustments based on prompts. For example, the generation unit can prioritize including scoring scenes specified by the user. It can also prioritize including highlight scenes of players specified by the user. It can also prioritize including climactic scenes from matches specified by the user. This allows the generation unit to provide highlight videos that meet the user's needs by prioritizing the inclusion of specific scenes or players specified by the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input data on scenes or players specified by the user into a generation AI and have the generation AI perform the generation of the highlight video.

[0050] The generation unit can be equipped with the ability to generate highlight videos of other sports and entertainment events. For example, the generation unit can generate highlight videos of other sports events. The generation unit can also generate highlight videos of entertainment events. The generation unit can also generate highlight videos of other sports events that include athlete interviews. By adding the ability to generate highlight videos of other sports and entertainment events, the generation unit can be adapted to a wide range of applications. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data of other sports or entertainment events into a generation AI and have the generation AI perform the generation of highlight videos.

[0051] The service provider can display personalized recommended videos based on the user's viewing history and preferences when providing highlight videos. For example, the service provider can recommend highlight videos of players the user has watched in the past. The service provider can also recommend highlight videos of matches that match the user's preferences based on their viewing history. The service provider can also recommend highlight videos of related players based on the user's viewing history. This improves user satisfaction by displaying personalized recommended videos based on the user's viewing history and preferences. 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 viewing history data into a generating AI and have the generating AI generate recommended videos.

[0052] The content provider can collect real-time viewer reactions when providing highlight videos and reflect them in the content of future content. For example, the content provider can collect real-time viewer reactions and reflect them in the next highlight video. The content provider can also collect real-time viewer comments and reflect them in the next highlight video. The content provider can also collect real-time viewer ratings and reflect them in the next highlight video. This allows for the provision of more appropriate content by collecting real-time viewer reactions and reflecting them in the content of future content. Some or all of the above processing in the content provider may be performed using AI, for example, or not using AI. For example, the content provider can input real-time viewer reaction data into a generating AI and have the generating AI adjust the content of the next content.

[0053] The service provider can be equipped with features that allow for horizontal expansion to other sports, security cameras, and the security industry. For example, the service provider can add a function to provide highlight videos of other sports events. The service provider can also add a function to analyze security camera footage and provide important scenes. The service provider can also add a function to extract and provide important scenes for the security industry. This allows the service provider to accommodate a wide range of applications by incorporating features that allow for horizontal expansion to other sports, security cameras, and the security industry. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data from other sports events or security cameras into a generating AI and have the generating AI extract and provide important scenes.

[0054] The service provider can prioritize displaying videos that focus on specific players or scenes selected by the user when providing highlight videos. For example, the service provider can prioritize displaying scoring scenes of players selected by the user. The service provider can also prioritize displaying climactic scenes from matches selected by the user. The service provider can also prioritize displaying interviews with players selected by the user. This improves user satisfaction by prioritizing the display of videos that focus on specific players or scenes selected by the user. 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 data on players or scenes selected by the user into a generating AI and have the generating AI generate focused videos.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can collect not only video data of the match but also data on the audience's reactions. For example, it can collect audio data of the audience's cheers and applause to reflect the excitement of the match. It can also capture the facial expressions and movements of the audience with cameras and collect that data. This can enhance the sense of realism in the match and improve fan satisfaction. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input audience reaction data into a generating AI and have the generating AI perform the analysis.

[0057] The data collection unit can collect footage of players interacting with their families and friends outside of matches. For example, it can collect footage of players spending time with their families or relaxing with friends. It can also collect footage of players showcasing their hobbies and special skills. This allows fans to learn about the players' human side and feel a greater sense of connection with them. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input data of players interacting with their families and friends into a generating AI and have the generating AI perform the analysis.

[0058] The analysis unit can also consider the player's physical condition and fatigue level when extracting at least one important scene from scoring plays and outstanding plays. For example, it can extract outstanding plays when the player is tired, or scoring scenes when the player is close to their physical limit. It can also analyze vital data such as the player's heart rate and respiratory rate and reflect this in the extraction of important scenes. This makes it possible to provide highlight videos that more strongly emphasize the player's effort and hard work. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the player's vital data into a generating AI and have the generating AI perform the extraction of important scenes.

[0059] The generation unit can customize the highlight videos based on the user's viewing history and preferences when making fine adjustments based on prompts. For example, it can analyze the trends of highlight videos the user has watched in the past and prioritize including scenes that match their preferences. Also, if the user supports a particular player or team, it can include more scenes of that player or team. This allows for the provision of highlight videos tailored to the user's preferences, improving the viewing experience. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's viewing history data into a generation AI and have the generation AI perform the customization of the highlight videos.

[0060] The service provider can incorporate features that allow for horizontal expansion into other sports, security camera, or security industries. For example, when providing highlight videos of other sporting events, it can add features for tactical analysis of matches and evaluation of player performance. It can also add features to analyze security camera footage and detect abnormal behavior or suspicious individuals. Furthermore, for the security industry, it can add features to notify important scenes in real time. By incorporating features that allow for horizontal expansion into other sports, security camera, and security industries, it can accommodate a wide range of applications. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can input data from other sporting events or security cameras into a generating AI and have the generating AI perform the analysis and notification.

[0061] The collection unit can prioritize the collection of important scenes in real time according to the progress of the match. For example, it can collect the climax of the match in real time. It can also prioritize the collection of important plays immediately after the start of the match. It can also collect decisive scenes near the end of the match in real time. This makes it possible to provide content in real time by prioritizing the collection of important scenes according to the progress of the match. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input match progress data into a generating AI and have the generating AI perform the collection of important scenes.

[0062] The data collection unit can automatically collect scenes where a specific player performs well by referring to past match data. For example, it can automatically collect scoring scenes of players who were the top scorers in past matches. It can also automatically collect scenes of players who made a series of spectacular plays in past matches. It can also automatically collect highlights of players who were selected as MVP in past matches. This allows for the provision of attractive content for fans by collecting scenes where a specific player performs well by referring to past match data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past match data into a generating AI and have the generating AI perform well.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects video data of the match. The collection unit can collect video data of the match using cameras or sensors, for example. The collection unit can also collect video data of the match in real time. In addition, it can automatically collect scenes where a specific player performs well by referring to past match data. Step 2: The analysis unit analyzes the video data collected by the collection unit and extracts important scenes. The analysis unit can extract important scenes such as scoring scenes and spectacular plays. It can also dynamically change the criteria for extracting important scenes, taking into account the situation and context of the match. Furthermore, it can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. Step 3: The generation unit combines the scenes extracted by the analysis unit to generate a highlight video. The generation unit can, for example, make fine adjustments via prompts. It can also estimate the user's emotions and adjust the content of the generated highlight video based on those emotions. It can also edit the video to give a storyline to specific players or teams. Step 4: The provider unit provides the highlight video generated by the generator unit. The provider unit can, for example, create an original highlight video focusing on a specific player. It can also estimate the user's emotions and adjust how the highlight video is displayed based on those emotions. It can also include features that allow for expansion to other sports, security cameras, and the security industry.

[0065] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that will be incorporated into "Basketball LIVE," which provides B.LEAGUE game videos and player footage. This system will automatically generate game highlights and provide a function to create original highlight videos that focus on the player's favorite player. This will reduce human labor and costs and improve fan satisfaction. First, the system will analyze the game video data and extract important scenes. For example, scoring scenes and fine plays will be included. Next, the extracted scenes will be combined to automatically generate a highlight video. At this time, humans only need to make minor adjustments using prompts, so labor and costs can be significantly reduced. Furthermore, the system can analyze the unstructured data of the video and create original highlight videos that focus on specific players. For example, it can generate a video that collects scenes where a specific player scores or performs well. As a result, fans can enjoy highlight videos that feature their favorite player throughout, and it is expected that the number of Basketball LIVE users will increase. This AI agent system can also be applied to other sports, security cameras, and the security industry, aiming to realize a world where unstructured data can be easily handled. This allows the AI ​​agent system to efficiently collect, analyze, generate, and provide video data from matches.

[0066] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video data of matches. The collection unit can collect video data of matches using, for example, cameras or sensors. The collection unit can also collect video data of matches in real time. The collection unit can also automatically collect scenes in which a particular player performs well by referring to past match data. The analysis unit analyzes the video data collected by the collection unit and extracts important scenes. The analysis unit can extract important scenes such as scoring scenes and fine plays. The analysis unit can also dynamically change the criteria for extracting important scenes, taking into account the situation and context of the match. The analysis unit can also estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. The generation unit combines the scenes extracted by the analysis unit to generate a highlight video. The generation unit can, for example, make fine adjustments using prompts. The generation unit can also estimate the user's emotions and adjust the content of the highlight video it generates based on the estimated user emotions. The generation unit can also perform editing to give a storyline to specific players or teams when generating highlight videos. The delivery unit provides the highlight videos generated by the generation unit. The delivery unit can, for example, create original highlight videos that focus on specific players. The delivery unit can also estimate the user's emotions and adjust how the provided highlight videos are displayed based on the estimated user emotions. The delivery unit can also be equipped with functions that allow for horizontal expansion to other sports, security cameras, and the security industry. As a result, the AI ​​agent system according to the embodiment can efficiently collect, analyze, generate, and provide video data of matches.

[0067] The data collection unit collects video data from matches. For example, the unit can collect match video data using cameras and sensors. Specifically, it uses high-resolution cameras installed in stadiums and arenas, and motion sensors to track player movements. These devices collect data in real time as the match progresses and transmit it to a central database. The data collection unit can also collect match video data in real time. This allows for immediate understanding of the match's progress and ensures that important scenes are recorded without being missed. Furthermore, the data collection unit can automatically collect scenes where specific players perform well by referencing past match data. For example, it can analyze past match data, extract scenes where a specific player scored a goal or made a great play, and save these scenes in the database. This allows the data collection unit to collect richer data by utilizing not only current match data but also past match data. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Additionally, adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0068] The analysis unit analyzes video data collected by the collection unit and extracts important scenes. For example, the analysis unit can extract important scenes such as scoring plays and spectacular plays. Specifically, it utilizes AI-based image recognition technology to analyze player movements and ball positions from video data. This allows for the automatic detection of important scenes such as scoring plays and spectacular plays. The analysis unit can also dynamically change the criteria for extracting important scenes, taking into account the match situation and context. For example, it adjusts the criteria for extracting important scenes in real time, taking into account the progress of the match, the score, and player performance. This allows the analysis unit to perform flexible analysis according to the match situation. Furthermore, the analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is excited, it will extract more scoring plays and spectacular plays; if the user is calm, it will extract scenes that emphasize the flow of the match. This allows the analysis unit to perform optimal analysis according to the user's emotions. The analysis unit can also utilize historical data and statistical information to perform long-term trend analysis and performance evaluation. For example, the analysis of fluctuations in the performance of specific players or teams can be used to predict future performance and formulate strategies. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term performance evaluation and strategy formulation, improving the reliability and usefulness of the entire system.

[0069] The generation unit generates a highlight video by combining scenes extracted by the analysis unit. The generation unit can, for example, make fine adjustments via prompts. Specifically, it can customize the content of the highlight video according to the scenes, players, and match situations desired by the user. The generation unit can also estimate the user's emotions and adjust the content of the generated highlight video based on the estimated emotions. For example, if the user is excited, it will generate a highlight video that includes more scoring scenes and great plays, while if the user is calm, it will generate a highlight video that emphasizes the flow of the match. In this way, the generation unit can provide the optimal highlight video according to the user's emotions. The generation unit can also edit the highlight video to give it a storyline for specific players or teams. For example, it can edit to focus on the highlight scenes of a particular player and emphasize that player's story. It can also add scenes that explain the strategies and tactics of a particular team to deepen the understanding of the match. In this way, the generation unit can provide not just a simple highlight video, but also story-driven content that matches the user's interests and concerns. Furthermore, the generation unit can evaluate the quality of the generated highlight video and regenerate or modify it as needed. This allows the generation unit to consistently provide high-quality highlight videos, thereby improving user satisfaction.

[0070] The service provider provides highlight videos generated by the generation unit. For example, the service provider can create original highlight videos focusing on specific players. Specifically, when a user selects a particular player, it provides a highlight video edited to highlight that player's performance. The service provider can also estimate the user's emotions and adjust the display method of the highlight video based on those emotions. For example, if the user is excited, it provides a highlight video with more dynamic effects and presentation; if the user is calm, it provides a simpler, more subdued highlight video. This allows the service provider to provide the optimal display method according to the user's emotions. The service provider can also incorporate features for application to other sports, security cameras, and the security industry. For example, adding functions to collect, analyze, generate, and provide match data from other sports can cater to a wider range of sports fans. Furthermore, in the security camera and security industry, it can support efficient monitoring and rapid response by extracting important scenes and generating and providing highlight videos. This allows the service provider to offer a flexible system applicable not only to the sports field but also to other fields. Additionally, the service provider can collect user feedback and continuously improve the quality and display method of the content it provides. This allows the service provider to consistently deliver optimal content tailored to user needs, thereby improving the overall reliability and satisfaction of the system.

[0071] The service provider can create original highlight videos that focus on specific players. For example, the service provider can generate videos that compile scenes of a specific player scoring goals or performing well. The service provider can also include interviews and training footage of the specific player. The service provider can also collect post-match comments and impressions from a specific player and incorporate them into the highlight video. This allows for increased fan satisfaction by providing original highlight videos that focus on specific players. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input data on a specific player into a generating AI and have the generating AI generate an original highlight video.

[0072] The data collection unit can collect information about players outside of matches. For example, it can collect information about players' training, interviews, and daily lives. The data collection unit can also collect information about players' pre-match warm-ups and post-match cool-downs. The data collection unit can also collect information about players' off-day activities and hobbies. By collecting information about players outside of matches, a variety of content can be provided to fans. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can take pictures of players with a camera, input that data into a generating AI, and have the generating AI perform the analysis.

[0073] The analysis unit can extract at least one important scene from among scoring scenes and fine plays. The analysis unit can extract based, for example, on the type of scoring scene and the importance of the goal. The analysis unit can also extract based on the type of fine play and the difficulty of the play. The analysis unit can also include players' emotional expressions and spectator reactions in its analysis. The analysis unit can also include players' tactical movements and team coordination in its analysis. This allows for the improvement of the quality of the highlight video by extracting important scenes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match video data into a generating AI and have the generating AI perform the extraction of important scenes.

[0074] The generation unit can perform fine-tuning using prompts. For example, the generation unit can use prompts to adjust the order and length of scenes in the highlight video. The generation unit can also use prompts to prioritize the inclusion of specific scenes or players. The generation unit can also use prompts to customize the content of the highlight video. This allows for improved accuracy of the highlight video through fine-tuning using prompts. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input prompts into a generation AI and have the generation AI perform the fine-tuning.

[0075] The service provider can be equipped with functions based on horizontal expansion to other sports or the security camera and security industry. For example, the service provider can add a function to provide highlight videos of other sports events. The service provider can also add a function to analyze security camera footage and provide important scenes. The service provider can also add a function to extract and provide important scenes for the security industry. This allows the service provider to accommodate a wide range of applications by incorporating functions that consider horizontal expansion to other sports, security cameras, and the security industry. 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 data from other sports events into a generating AI and have the generating AI perform the generation of highlight videos.

[0076] The data collection unit can estimate the user's emotions and adjust the types of video data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize collecting highlight scenes from matches. If the user is relaxed, the data collection unit can collect relaxing scenes such as player interviews or practice footage. If the user is sad, the data collection unit can collect videos containing emotional scenes or encouraging messages. By adjusting the types of video data collected based on the user's emotions, content tailored to the user's needs 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. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the types of video data to collect.

[0077] The data collection unit can prioritize the collection of important scenes in real time according to the progress of the match. For example, the data collection unit can collect the climax scenes of the match in real time. The data collection unit can also prioritize the collection of important plays immediately after the start of the match. The data collection unit can also collect decisive scenes near the end of the match in real time. This enables real-time content delivery by prioritizing the collection of important scenes according to the progress of the match. 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 match progress data into a generating AI and have the generating AI perform the collection of important scenes.

[0078] The data collection unit can automatically collect scenes where a specific player performs well by referring to past match data. For example, the data collection unit can automatically collect scoring scenes of players who were the top scorers in past matches. The data collection unit can also automatically collect scenes of players who made a series of spectacular plays in past matches. The data collection unit can also automatically collect highlights of players who were selected as MVP in past matches. This allows for the provision of engaging content for fans by collecting scenes where a specific player performs well by referring to past match data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past match data into a generating AI and have the generating AI collect scenes where a specific player performs well.

[0079] The data collection unit can estimate the user's emotions and determine the priority of video data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize collecting scoring scenes. If the user is relaxed, the data collection unit can also prioritize collecting relaxed scenes of players. If the user is sad, the data collection unit can also prioritize collecting emotional scenes. In this way, by determining the priority of video data to collect based on the user's emotions, content can be provided that meets the user's needs. 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, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of video data to collect.

[0080] The data collection unit can include player interviews and practice footage when collecting information about players outside of matches. For example, the data collection unit can collect post-match interviews with players. The data collection unit can also collect footage of players practicing. The data collection unit can also collect information about players on their days off. By collecting information about players outside of matches, a variety of content can be provided to fans. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data from player interviews and practice footage into a generating AI and have the generating AI perform the analysis.

[0081] The data collection unit can also include video data from other sports and entertainment events in its collection targets. For example, the data collection unit can collect highlight scenes from other sports events. The data collection unit can also collect performance scenes from entertainment events. The data collection unit can also collect athlete interviews from other sports events. This allows for the provision of a wide range of content by including video data from other sports and entertainment events in the collection targets. 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 data from other sports and entertainment events into a generating AI and have the generating AI perform the collection.

[0082] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can improve the accuracy of analyzing scoring scenes. If the user is relaxed, the analysis unit can also improve the accuracy of analyzing relaxed players. If the user is sad, the analysis unit can also improve the accuracy of analyzing emotional scenes. In this way, by adjusting the accuracy of the analysis based on the user's emotions, the analysis results can be provided that meet the user's needs. 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 may be performed using AI, for example, or without 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 analysis accuracy.

[0083] The analysis unit can analyze not only scoring scenes and spectacular plays, but also players' tactical movements and teamwork. For example, the analysis unit can analyze players' tactical movements. The analysis unit can also analyze teamwork. The analysis unit can also analyze players' positioning. By including players' tactical movements and teamwork in the analysis, it is possible to provide more detailed analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match video data into a generating AI and have the generating AI perform the analysis of tactical movements and teamwork.

[0084] The analysis unit can dynamically change the criteria for extracting important scenes based on the match situation and context. For example, the analysis unit can extract the climax of the match as an important scene. It can also extract important plays immediately after the start of the match. It can also extract decisive scenes near the end of the match. By dynamically changing the criteria for extracting important scenes, taking into account the match situation and context, it is possible to generate more appropriate highlight videos. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match situation data into a generating AI and have the generating AI perform the changes to the criteria for extracting important scenes.

[0085] 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 highlight scoring scenes. If the user is relaxed, the analysis unit can highlight the relaxed appearance of the players. If the user is sad, the analysis unit can highlight emotional scenes. In this way, by adjusting the display method of the analysis results based on the user's emotions, it becomes possible to display information that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0086] The analysis unit can include, in addition to analyzing scoring scenes and spectacular plays, the emotional expressions of players and the reactions of spectators in its analysis. For example, the analysis unit can analyze the emotional expressions of players after scoring a goal. The analysis unit can also analyze the reactions of spectators. The analysis unit can also analyze the emotional expressions of players during a match. By including the emotional expressions of players and the reactions of spectators in the analysis, more detailed analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input match video data into a generating AI and have the generating AI perform the analysis of emotional expressions and spectator reactions.

[0087] The analysis unit can be equipped with the ability to analyze video data from other sports and entertainment events. For example, the analysis unit can analyze scoring scenes from other sports events. It can also analyze performance scenes from entertainment events. It can also analyze athlete interviews from other sports events. By adding the ability to analyze video data from other sports and entertainment events, it can be used for a wide range of applications. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from other sports or entertainment events into a generating AI and have the generating AI perform the analysis.

[0088] The generation unit can estimate the user's emotions and adjust the content of the generated highlight video based on the estimated emotions. For example, if the user is excited, the generation unit can generate a highlight video that includes many scoring scenes. If the user is relaxed, the generation unit can also generate a highlight video that includes many relaxed scenes of the players. If the user is sad, the generation unit can also generate a highlight video that includes many emotional scenes. In this way, by adjusting the content of the generated highlight video based on the user's emotions, content that meets the user's needs 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the content of the highlight video.

[0089] The generation unit can edit the highlight video to give it a narrative structure, focusing on specific players or teams. For example, it can edit the video to focus on a specific player's scoring plays. It can also edit the video to focus on a team's coordinated plays. It can also edit the flow of the match to give it a narrative structure. By editing the video to give it a narrative structure, it is possible to attract viewers' interest and provide a more engaging highlight video. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input data on specific players or teams into a generation AI and have the generation AI perform the editing to give it a narrative structure.

[0090] The generation unit can provide diverse visual experiences by combining different viewpoints and camera angles when generating highlight videos. For example, the generation unit can combine scoring scenes from different camera angles. It can also combine spectacular plays from different viewpoints. It can also combine emotional expressions of players from different camera angles. In this way, by combining different viewpoints and camera angles, it can provide viewers with diverse visual experiences. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on different viewpoints and camera angles into a generation AI and have the generation AI perform the generation of highlight videos.

[0091] The generation unit can estimate the user's emotions and adjust the length of the generated highlight video based on the estimated emotions. For example, if the user is excited, the generation unit can generate a short, concise highlight video. If the user is relaxed, the generation unit can generate a longer highlight video. If the user is sad, the generation unit can generate a longer highlight video containing many emotional scenes. By adjusting the length of the highlight video based on the user's emotions, content tailored to the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the highlight video.

[0092] The generation unit can prioritize including specific scenes or players specified by the user when making fine adjustments based on prompts. For example, the generation unit can prioritize including scoring scenes specified by the user. It can also prioritize including highlight scenes of players specified by the user. It can also prioritize including climactic scenes from matches specified by the user. This allows the generation unit to provide highlight videos that meet the user's needs by prioritizing the inclusion of specific scenes or players specified by the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input data on scenes or players specified by the user into a generation AI and have the generation AI perform the generation of the highlight video.

[0093] The generation unit can be equipped with the ability to generate highlight videos of other sports and entertainment events. For example, the generation unit can generate highlight videos of other sports events. The generation unit can also generate highlight videos of entertainment events. The generation unit can also generate highlight videos of other sports events that include athlete interviews. By adding the ability to generate highlight videos of other sports and entertainment events, the generation unit can be adapted to a wide range of applications. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data of other sports or entertainment events into a generation AI and have the generation AI perform the generation of highlight videos.

[0094] The service provider can estimate the user's emotions and adjust how the highlight video is displayed based on the estimated emotions. For example, if the user is excited, the service provider can highlight scoring scenes. If the user is relaxed, the service provider can highlight relaxed scenes of the players. If the user is sad, the service provider can highlight emotional scenes. By adjusting how the highlight video is displayed based on the user's emotions, it becomes possible to display content that meets the user's needs. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust how the highlight video is displayed.

[0095] The service provider can display personalized recommended videos based on the user's viewing history and preferences when providing highlight videos. For example, the service provider can recommend highlight videos of players the user has watched in the past. The service provider can also recommend highlight videos of matches that match the user's preferences based on their viewing history. The service provider can also recommend highlight videos of related players based on the user's viewing history. This improves user satisfaction by displaying personalized recommended videos based on the user's viewing history and preferences. 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 viewing history data into a generating AI and have the generating AI generate recommended videos.

[0096] The content provider can collect real-time viewer reactions when providing highlight videos and reflect them in the content of future content. For example, the content provider can collect real-time viewer reactions and reflect them in the next highlight video. The content provider can also collect real-time viewer comments and reflect them in the next highlight video. The content provider can also collect real-time viewer ratings and reflect them in the next highlight video. This allows for the provision of more appropriate content by collecting real-time viewer reactions and reflecting them in the content of future content. Some or all of the above processing in the content provider may be performed using AI, for example, or not using AI. For example, the content provider can input real-time viewer reaction data into a generating AI and have the generating AI adjust the content of the next content.

[0097] The service provider can estimate the user's emotions and adjust the order of the highlight videos based on the estimated emotions. For example, if the user is excited, the service provider can display scoring scenes first. If the user is relaxed, the service provider can display relaxed scenes of the players first. If the user is sad, the service provider can display emotional scenes first. By adjusting the order of the highlight videos based on the user's emotions, it is possible to display content that meets the user's needs. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the order of the highlight videos.

[0098] The service provider can be equipped with features that allow for horizontal expansion to other sports, security cameras, and the security industry. For example, the service provider can add a function to provide highlight videos of other sports events. The service provider can also add a function to analyze security camera footage and provide important scenes. The service provider can also add a function to extract and provide important scenes for the security industry. This allows the service provider to accommodate a wide range of applications by incorporating features that allow for horizontal expansion to other sports, security cameras, and the security industry. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data from other sports events or security cameras into a generating AI and have the generating AI extract and provide important scenes.

[0099] The service provider can prioritize displaying videos that focus on specific players or scenes selected by the user when providing highlight videos. For example, the service provider can prioritize displaying scoring scenes of players selected by the user. The service provider can also prioritize displaying climactic scenes from matches selected by the user. The service provider can also prioritize displaying interviews with players selected by the user. This improves user satisfaction by prioritizing the display of videos that focus on specific players or scenes selected by the user. 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 data on players or scenes selected by the user into a generating AI and have the generating AI generate focused videos.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can collect not only video data of the match but also data on the audience's reactions. For example, it can collect audio data of the audience's cheers and applause to reflect the excitement of the match. It can also capture the facial expressions and movements of the audience with cameras and collect that data. This can enhance the sense of realism in the match and improve fan satisfaction. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input audience reaction data into a generating AI and have the generating AI perform the analysis.

[0102] The content provider can emphasize a player's emotional expression when creating original highlight videos that focus on a specific player. For example, it can highlight and display expressions of joy after scoring a goal or frustration after making a mistake. In addition, by including interviews with the player or post-match comments, the player's emotions can be conveyed more deeply. This is expected to allow fans to empathize with the player's emotions and increase their desire to support them even more. Some or all of the above processing in the content provider may be performed using AI or not. For example, the content provider can input player emotional data into a generating AI and have the generating AI perform the generation of the highlight video.

[0103] The data collection unit can collect footage of players interacting with their families and friends outside of matches. For example, it can collect footage of players spending time with their families or relaxing with friends. It can also collect footage of players showcasing their hobbies and special skills. This allows fans to learn about the players' human side and feel a greater sense of connection with them. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input data of players interacting with their families and friends into a generating AI and have the generating AI perform the analysis.

[0104] The analysis unit can also consider the player's physical condition and fatigue level when extracting at least one important scene from scoring plays and outstanding plays. For example, it can extract outstanding plays when the player is tired, or scoring scenes when the player is close to their physical limit. It can also analyze vital data such as the player's heart rate and respiratory rate and reflect this in the extraction of important scenes. This makes it possible to provide highlight videos that more strongly emphasize the player's effort and hard work. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the player's vital data into a generating AI and have the generating AI perform the extraction of important scenes.

[0105] The generation unit can customize the highlight videos based on the user's viewing history and preferences when making fine adjustments based on prompts. For example, it can analyze the trends of highlight videos the user has watched in the past and prioritize including scenes that match their preferences. Also, if the user supports a particular player or team, it can include more scenes of that player or team. This allows for the provision of highlight videos tailored to the user's preferences, improving the viewing experience. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's viewing history data into a generation AI and have the generation AI perform the customization of the highlight videos.

[0106] The service provider can incorporate features that allow for horizontal expansion into other sports, security camera, or security industries. For example, when providing highlight videos of other sporting events, it can add features for tactical analysis of matches and evaluation of player performance. It can also add features to analyze security camera footage and detect abnormal behavior or suspicious individuals. Furthermore, for the security industry, it can add features to notify important scenes in real time. By incorporating features that allow for horizontal expansion into other sports, security camera, and security industries, it can accommodate a wide range of applications. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can input data from other sporting events or security cameras into a generating AI and have the generating AI perform the analysis and notification.

[0107] The data collection unit can estimate the user's emotions and adjust the types of video data to collect based on the estimated emotions. For example, if the user is excited, it can prioritize collecting highlight scenes from matches. If the user is relaxed, it can collect relaxing scenes such as player interviews or practice footage. If the user is sad, it can collect videos containing emotional scenes or encouraging messages. In this way, by adjusting the types of video data collected based on the user's emotions, content tailored to the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the types of video data to collect.

[0108] The collection unit can prioritize the collection of important scenes in real time according to the progress of the match. For example, it can collect the climax of the match in real time. It can also prioritize the collection of important plays immediately after the start of the match. It can also collect decisive scenes near the end of the match in real time. This makes it possible to provide content in real time by prioritizing the collection of important scenes according to the progress of the match. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input match progress data into a generating AI and have the generating AI perform the collection of important scenes.

[0109] The data collection unit can automatically collect scenes where a specific player performs well by referring to past match data. For example, it can automatically collect scoring scenes of players who were the top scorers in past matches. It can also automatically collect scenes of players who made a series of spectacular plays in past matches. It can also automatically collect highlights of players who were selected as MVP in past matches. This allows for the provision of attractive content for fans by collecting scenes where a specific player performs well by referring to past match data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past match data into a generating AI and have the generating AI perform well.

[0110] The data collection unit can estimate the user's emotions and determine the priority of video data to collect based on the estimated emotions. For example, if the user is excited, scoring scenes can be prioritized for collection. If the user is relaxed, scenes of players relaxing can be prioritized for collection. If the user is sad, emotional scenes can be prioritized for collection. In this way, by determining the priority of video data to collect based on the user's emotions, content can be provided that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, such as 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. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of video data to collect.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects video data of the match. The collection unit can collect video data of the match using cameras or sensors, for example. The collection unit can also collect video data of the match in real time. In addition, it can automatically collect scenes where a specific player performs well by referring to past match data. Step 2: The analysis unit analyzes the video data collected by the collection unit and extracts important scenes. The analysis unit can extract important scenes such as scoring scenes and spectacular plays. It can also dynamically change the criteria for extracting important scenes, taking into account the situation and context of the match. Furthermore, it can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. Step 3: The generation unit combines the scenes extracted by the analysis unit to generate a highlight video. The generation unit can, for example, make fine adjustments via prompts. It can also estimate the user's emotions and adjust the content of the generated highlight video based on those emotions. It can also edit the video to give a storyline to specific players or teams. Step 4: The provider unit provides the highlight video generated by the generator unit. The provider unit can, for example, create an original highlight video focusing on a specific player. It can also estimate the user's emotions and adjust how the highlight video is displayed based on those emotions. It can also include features that allow for expansion to other sports, security cameras, and the security industry.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 unit 12. For example, the collection unit can collect video data of the match using the camera 42 and sensors of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected video data to extract important scenes. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and combines the extracted scenes to generate a highlight video. The provision unit is implemented in the control unit 46A of the smart device 14, for example, and provides the generated highlight video to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect video data of the match using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected video data and extracts important scenes. The generation unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which combines the extracted scenes to generate a highlight video. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, which provides the generated highlight video to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 can collect video data of the match using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected video data to extract important scenes. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and combines the extracted scenes to generate a highlight video. The provision unit is implemented in the control unit 46A of the headset terminal 314, for example, and provides the generated highlight video to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect video data of the match using the camera 42 and sensors of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected video data and extracts important scenes. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which combines the extracted scenes to generate a highlight video. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides the generated highlight video to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) The collection department collects video data of the matches, An analysis unit analyzes the video data collected by the aforementioned collection unit and extracts important scenes, A generation unit that generates a highlight video by combining scenes extracted by the analysis unit, The system includes a providing unit that provides the highlight video generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Create original highlight videos that focus on specific players. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect information about players outside of matches. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Extract at least one important scene from among the scoring scenes and the outstanding plays. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Make fine adjustments using prompts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It features functions that can be applied to other sports or the security camera and security industry. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of video data collected based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system prioritizes collecting important scenes in real time, depending on the progress of the match. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is By referencing past match data, the system automatically collects scenes where a specific player performs at their best. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of video data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information about players outside of matches, include player interviews and practice footage. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Video data from other sporting and entertainment events will also be included in the data collection. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The analysis will include not only scoring opportunities and spectacular plays, but also players' tactical movements and teamwork. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The criteria for extracting important scenes are dynamically changed based on the match situation and context. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned analysis unit, In addition to analyzing scoring plays and spectacular plays, the analysis will also include players' emotional expressions and spectator reactions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Add a feature to analyze video data from other sports and entertainment events. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the content of the generated highlight video based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When creating highlight videos, editing is done to give a storyline to specific players or teams. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating highlight videos, we combine different viewpoints and camera angles to provide a diverse visual experience. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the generated highlight video based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When making fine adjustments via prompts, prioritize including specific scenes or players specified by the user. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is Add a feature to generate highlight videos of other sports and entertainment events. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the highlight videos are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing highlight videos, we will display personalized recommended videos based on the user's viewing history and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing highlight videos, we collect viewer reactions in real time and incorporate them into the content of future videos. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order of the highlight videos provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It features functions designed for application in other sports, security cameras, and the security industry. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing highlight videos, the system prioritizes displaying videos that focus on specific players or scenes selected by the user. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects video data of the matches, An analysis unit analyzes the video data collected by the aforementioned collection unit and extracts important scenes, A generation unit that generates a highlight video by combining scenes extracted by the analysis unit, The system includes a providing unit that provides the highlight video generated by the generation unit. A system characterized by the following features.

2. The aforementioned supply unit is, Create original highlight videos that focus on specific players. The system according to feature 1.

3. The aforementioned collection unit is Collect information about players outside of matches. The system according to feature 1.

4. The aforementioned analysis unit, Extract at least one important scene from among the scoring scenes and the outstanding plays. The system according to feature 1.

5. The generating unit is Make fine adjustments using prompts. The system according to feature 1.

6. The aforementioned supply unit is, It features functions that can be applied to other sports or the security camera and security industry. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of video data collected based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is The system prioritizes collecting important scenes in real time, depending on the progress of the match. The system according to feature 1.

9. The aforementioned collection unit is By referencing past match data, the system automatically collects scenes where a specific player performs at their best. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of video data to collect based on the estimated user emotions. The system according to feature 1.

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