system

Abstract

The system according to this embodiment aims to provide a sports viewing experience tailored to the viewer's preferences. [Solution] The system according to the embodiment comprises a camera switching unit, a commentary unit, a display unit, and a highlight unit. The camera switching unit allows viewers to freely switch camera angles. The commentary unit analyzes the camera footage selected by the camera switching unit and provides personalized real-time commentary. The display unit displays statistical data and background information tailored to the viewer's interests based on the commentary provided by the commentary unit. The highlight unit provides highlights and player profiles tailored to the viewer's preferences after the match has ended, based on the information displayed by the display unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for viewers to watch according to their own interests and concerns in watching sports events, and there is a lack of personalized information provision.

[0005] <00The system according to this embodiment comprises a camera switching unit, a commentary unit, a display unit, and a highlight unit. The camera switching unit allows viewers to freely switch camera angles. The commentary unit analyzes the camera footage selected by the camera switching unit and provides personalized real-time commentary. The display unit displays statistical data and background information tailored to the viewer's interests based on the commentary provided by the commentary unit. The highlight unit provides highlights and player profiles tailored to the viewer's preferences after the match has ended, based on the information displayed by the display unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a sports viewing experience tailored to the viewer's preferences. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The sports viewing system according to an embodiment of the present invention is a system that allows viewers to freely switch camera angles and displays real-time commentary and statistical data generated by a generating AI. This sports viewing system provides a sports viewing experience tailored to individual preferences by allowing viewers to freely switch camera angles and display real-time commentary and statistical data generated by a generating AI. In addition, it provides customizable highlights and player profiles after the match. For example, viewers can freely switch between multiple cameras installed on the court and enjoy the match from different perspectives. The generating AI analyzes the camera footage selected by the viewer in real time and provides personalized commentary. Statistical data and background information tailored to the viewer's interests are also displayed. After the match, highlights and player profiles tailored to the viewer's preferences are provided, and viewers can customize the highlights to suit their needs. This system allows viewers to enjoy the match from a perspective that suits their interests and preferences, and provides a more engaging viewing experience through personalized commentary and information. In addition, viewers can enjoy highlights tailored to their preferences after the match, which improves viewer satisfaction. Thus, the sports viewing system can provide a sports viewing experience tailored to the viewer's preferences.

[0029] The sports viewing system according to this embodiment comprises a camera switching unit, a commentary unit, a display unit, and a highlight unit. The camera switching unit allows viewers to freely switch camera angles. For example, viewers can select camera angles using a remote controller. The camera switching unit also allows viewers to switch camera angles using a smartphone or tablet. Furthermore, viewers can change camera angles using voice commands. The commentary unit uses generation AI to analyze the camera footage selected by the viewer and provides personalized real-time commentary. For example, the commentary unit generates commentary tailored to the viewer's interests based on the viewer's viewing history and profile information. The commentary unit can also analyze viewers' real-time reactions and adjust the content of the commentary. Furthermore, the commentary unit can collect viewer feedback and use it to improve the quality of the commentary. The display unit displays statistical data and background information tailored to the viewer's interests based on the commentary provided by the commentary unit. For example, the display unit displays statistical data such as player performance and match history. The display unit can also display information such as background information on the match and player profiles. Furthermore, the display unit can update information in real time according to the viewer's interests. The highlight unit provides highlights and player profiles tailored to the viewer's preferences after the match, based on the information displayed by the display unit. For example, the highlight unit provides important scenes selected by the viewer as highlights. The highlight unit can also customize the content of the highlights according to the viewer's preferences. In addition, the highlight unit can collect viewer feedback and use it to improve the quality of the highlights. As a result, the sports viewing system according to this embodiment can provide a sports viewing experience tailored to the viewer's preferences.

[0030] The camera switching unit allows viewers to freely switch camera angles. Specifically, viewers can use a remote controller to select from multiple camera angles. For example, they can enjoy the match from various perspectives, such as a camera that overlooks the entire stadium, a camera that focuses on a specific player, or a camera that is close to the goal. The camera switching unit can also be used to switch camera angles using a smartphone or tablet, allowing viewers to intuitively change camera angles by operating the touchscreen. Furthermore, it is also possible to change camera angles using voice commands, so viewers can instantly switch to their desired angle simply by giving a voice command such as "Switch to the camera near the goal." This allows viewers to watch the match from the optimal viewpoint according to their preferences, resulting in a more immersive experience. The camera switching unit also has a function that records the viewer's operation history and automatically suggests preferred angles for the next viewing. For example, if a viewer frequently selected a camera angle that focused on a specific player in the past, the unit will prioritize displaying a similar angle in the next match, improving viewer convenience. Furthermore, the camera switching unit is designed to allow for smooth switching even when multiple viewers select different angles simultaneously, providing a comfortable experience when watching with family and friends. This enables flexible camera angle selection to meet the diverse needs of viewers, further enhancing the enjoyment of watching sports.

[0031] The commentary team uses generative AI to analyze camera footage selected by viewers and provide personalized real-time commentary. Specifically, the generative AI generates commentary tailored to the viewer's interests based on their viewing history and profile information. For example, viewers interested in a particular player will receive detailed commentary on that player's play, while viewers interested in tactics and techniques will receive detailed commentary on the tactical aspects of the match. The generative AI can also analyze viewers' real-time reactions and dynamically adjust the content of the commentary. For example, if a viewer is excited about a particular play, additional information and background details about that play can be provided to further pique their interest. Furthermore, the commentary team collects viewer feedback and uses it to improve the quality of the commentary. By providing ratings and comments on the commentary, viewers can learn from this feedback and improve the content of future commentaries. This allows the commentary team to consistently provide high-quality commentary that meets the needs of viewers. The commentary team supports multiple languages ​​and can provide commentary according to the viewer's language settings. For example, by providing real-time commentary in the viewer's selected language, such as English, Spanish, or Japanese, it can also cater to an international audience. Furthermore, the commentary section includes a feature that allows viewers to request detailed explanations of specific plays or scenes, enabling it to provide in-depth information on moments that interest viewers. This allows the commentary section to provide personalized commentary tailored to viewers' interests, further enhancing the enjoyment of watching sports.

[0032] The display unit shows statistical data and background information tailored to the viewer's interests, based on the commentary provided by the commentary unit. Specifically, it can display statistical data such as player statistics and match history. For example, by visually displaying a player's past performance or real-time results in the current match, viewers can gain a deeper understanding of the match's progress. The display unit can also display background information about the match and player profiles. This makes it easier for viewers to understand the context of the match and the players' backgrounds, allowing them to become more immersed in the game. Furthermore, the display unit can update information in real time according to the viewer's interests. For example, if an important event occurs during a match, it can instantly display information related to that event, providing viewers with the latest information. The display unit also has a function that allows viewers to customize the information displayed, so viewers can select what is displayed according to their interests. For example, it is possible to set it to display only the statistics of a specific player or specific statistical data, allowing viewers to obtain information tailored to their preferences. The display unit can also display links and buttons that provide more detailed information on information that the viewer is interested in. This allows viewers to delve deeper into the information that interests them, resulting in a more fulfilling viewing experience. The display unit can continuously improve its content based on viewer feedback, providing optimal information tailored to viewer needs. This allows the display unit to provide statistical data and background information aligned with viewer interests, further enhancing the enjoyment of watching sports.

[0033] The highlights section provides viewers with personalized highlights and player profiles after the match, based on the information displayed by the display section. Specifically, it can provide highlights of important scenes selected by the viewer. For example, it can create highlight videos compiling moments that viewers found particularly interesting, such as goals or decisive plays, and provide them to viewers after the match. The highlights section can also customize the content of the highlights according to the viewer's preferences. For example, it can customize highlights that only feature the plays of a specific player or highlights that compile important scenes from a specific match, according to the viewer's requests. Furthermore, the highlights section can collect viewer feedback and use it to improve the quality of the highlights. By providing ratings and comments on the highlights, viewers can have them reflected in the creation of future highlights. This ensures that the highlights section always provides high-quality highlights that meet the needs of viewers. The highlights section also has a feature that allows viewers to share highlights, so viewers can share their favorite highlight videos with friends and family via social media and messaging apps. This makes it easier for viewers to share the excitement and emotion of the match with others, expanding the enjoyment of watching sports. The highlights section also provides a feature that allows viewers to save highlights from past matches as an archive and play them at any time. This allows viewers to enjoy their favorite matches and players' performances over and over again. The highlights section can then provide highlights and player profiles tailored to the viewer's preferences, further enhancing the enjoyment of watching sports.

[0034] The commentary unit can analyze the camera footage selected by the viewer and provide personalized real-time commentary. For example, the commentary unit can generate commentary tailored to the viewer's interests based on their viewing history and profile information. The commentary unit can also analyze the viewer's real-time reactions and adjust the content of the commentary. Furthermore, the commentary unit can collect viewer feedback and use it to improve the quality of the commentary. This enhances the viewing experience by providing real-time commentary based on the viewer's choices. Some or all of the above processing in the commentary unit may be performed using generative AI or not. For example, the commentary unit can input the camera footage selected by the viewer into the generative AI and have the generative AI execute personalized real-time commentary.

[0035] The display unit can show statistical data and background information tailored to the viewer's interests. For example, the display unit can show statistical data such as player statistics and match history. It can also show information such as background information on matches and player profiles. Furthermore, the display unit can update information in real time according to the viewer's interests. This improves the viewing experience by providing information tailored to the viewer's interests. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input statistical data and background information tailored to the viewer's interests into the AI ​​and have the AI ​​select the information to display.

[0036] The highlights section can provide viewers with personalized highlights and player profiles after the match. For example, it can provide highlights of key scenes selected by the viewer. The highlights section can also customize the content of the highlights according to the viewer's preferences. Furthermore, the highlights section can collect viewer feedback and use it to improve the quality of the highlights. This enhances the viewing experience by providing highlights tailored to the viewer's preferences. Some or all of the above processing in the highlights section may be performed using AI or not. For example, the highlights section can have AI generate highlights tailored to the viewer's preferences.

[0037] The camera switching unit can analyze a viewer's past viewing history and automatically suggest the optimal camera angle. For example, the camera switching unit can prioritize suggesting similar angles based on angles the viewer has previously preferred to watch. Furthermore, if the viewer is interested in a particular player or play, the camera switching unit can suggest camera angles centered on that player or play. In addition, the camera switching unit can analyze the viewer's past viewing history to suggest preferred angles in specific match situations. This improves the viewing experience by suggesting the optimal camera angle based on the viewer's past viewing history. Some or all of the above processing in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input the viewer's past viewing history data into AI and have the AI ​​suggest the optimal camera angle.

[0038] The camera switching unit can filter camera angles based on the viewer's current interests and preferences. For example, if a viewer is interested in a particular player, the camera switching unit will switch the camera angle to focus on that player. It can also select an angle that emphasizes a particular play if the viewer is interested in that play. Furthermore, if a viewer is interested in a particular part of the game, the camera switching unit can adjust the camera angle to focus on that part. This improves the viewing experience by adjusting the camera angle based on the viewer's current interests and preferences. Some or all of the above processing in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input data on the viewer's current interests and preferences into an AI and have the AI ​​perform the camera angle filtering.

[0039] The camera switching unit can prioritize displaying angles that are highly relevant to the viewer's geographical location when switching camera angles. For example, if the viewer is at a specific location, the camera switching unit will prioritize displaying angles that are close to the viewer's local perspective. Furthermore, if the viewer is in a specific region, the camera switching unit can prioritize angles related to players or teams in that region. Additionally, if the viewer is overseas, the camera switching unit can prioritize displaying angles from an international perspective. This improves the viewing experience by displaying angles that are highly relevant based on the viewer's geographical location. Some or all of the above processing in the camera switching unit may be performed using AI, or not. For example, the camera switching unit can input the viewer's geographical location information into AI and have the AI ​​select the most relevant angles.

[0040] The camera switching unit can analyze viewers' social media activity and suggest relevant angles when switching camera angles. For example, if a viewer mentions a specific player on social media, the camera switching unit can suggest a camera angle centered on that player. It can also suggest an angle that emphasizes a specific play if the viewer comments on that play. Furthermore, if a viewer discusses a particular part of a match, the camera switching unit can adjust the camera angle to focus on that part. This improves the viewing experience by suggesting relevant angles based on viewers' social media activity. Some or all of the above processing in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input viewers' social media activity data into an AI and have the AI ​​suggest relevant angles.

[0041] The commentary unit can adjust the level of detail in the commentary based on the importance of the match when generating the commentary. For example, the commentary unit can provide detailed commentary for important matches. It can also provide concise commentary for friendly or practice matches. Furthermore, it can provide particularly detailed commentary for tournament finals. By adjusting the level of detail in the commentary according to the importance of the match, the viewing experience is improved. Some or all of the above processing in the commentary unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the commentary unit can input match importance data into a generation AI and have the generation AI adjust the level of detail in the commentary.

[0042] The commentary unit can apply different commentary algorithms depending on the category of the match when generating commentary. For example, in a soccer match, the commentary unit can apply a commentary algorithm specialized in goal scenes and foul scenes. In a basketball match, it can also apply a commentary algorithm specialized in scoring scenes and rebound scenes. Furthermore, in a tennis match, it can apply a commentary algorithm specialized in rally length and serve speed. By applying a commentary algorithm according to the category of the match, the viewing experience is improved. Some or all of the above processing in the commentary unit may be performed using a generation AI, or not. For example, the commentary unit can input match category data into a generation AI and apply a commentary algorithm to the generation AI.

[0043] The commentary unit can determine the priority of commentary based on the progress of the match when generating commentary. For example, in the early stages of the match, the commentary unit may prioritize commentary on player introductions and team strategies. In the middle stages of the match, it may also prioritize commentary on the flow of the game and important plays. Furthermore, in the later stages of the match, it may prioritize commentary on the match result and future prospects. This improves the viewing experience by determining the priority of commentary according to the progress of the match. Some or all of the above processing in the commentary unit may be performed using a generation AI, or not. For example, the commentary unit can input match progress data into a generation AI and have the generation AI determine the priority of commentary.

[0044] The commentary unit can adjust the order of commentary based on the relevance of the matches during the commentary generation process. For example, in important matches, the commentary unit can provide commentary in an order that follows the flow of the game. In friendly or practice matches, the commentary unit can also focus on important plays. Furthermore, in tournament finals, the commentary unit can prioritize commentary related to the match result. This improves the viewing experience by adjusting the order of commentary according to the relevance of the matches. Some or all of the above processing in the commentary unit may be performed using a generation AI, or not. For example, the commentary unit can input match relevance data into a generation AI and have the generation AI adjust the order of commentary.

[0045] The display unit can display the most relevant information by referring to the viewer's past viewing history. For example, the display unit can prioritize displaying statistical data that the viewer has shown interest in in the past. The display unit can also display background information that the viewer has shown interest in in the past. Furthermore, the display unit can display information related to a specific match situation based on the viewer's past viewing history. This improves the viewing experience by displaying the most relevant information based on the viewer's past viewing history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the viewer's past viewing history data into AI and have the AI ​​select the most relevant information.

[0046] The display unit can customize information based on the viewer's current interests and preferences when displaying it. For example, if a viewer is interested in a particular player, the display unit can display statistical data related to that player. It can also display background information related to a particular play if the viewer is interested in that play. Furthermore, if a viewer is interested in a particular part of a match, the display unit can display information related to that part. This improves the viewing experience by customizing information based on the viewer's current interests and preferences. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input data on the viewer's current interests and preferences into the AI ​​and have the AI ​​perform the information customization.

[0047] The display unit can prioritize displaying highly relevant information by considering the viewer's geographical location. For example, if the viewer is in a specific location, the display unit will prioritize displaying information that is closer to the local perspective. Furthermore, if the viewer is in a specific region, the display unit can prioritize information about players or teams related to that region. Additionally, if the viewer is overseas, the display unit can prioritize displaying information from an international perspective. This improves the viewing experience by displaying highly relevant information based on the viewer's geographical location. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input the viewer's geographical location information into AI and have the AI ​​select highly relevant information.

[0048] The display unit can analyze the viewer's social media activity and display relevant information when displaying content. For example, if a viewer mentions a particular player on social media, the display unit can display information related to that player. It can also display information related to a specific play if the viewer comments on that play. Furthermore, if a viewer discusses a particular part of a match, the display unit can display information related to that part. This improves the viewing experience by displaying relevant information based on the viewer's social media activity. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the viewer's social media activity data into an AI and have the AI ​​select relevant information.

[0049] The highlights section can provide optimal highlights by referencing the viewer's past viewing history when generating highlights. For example, the highlights section can provide similar scenes as highlights based on scenes the viewer has enjoyed watching in the past. Furthermore, if the viewer is interested in a particular player or play, the highlights section can focus on that player or play. In addition, the highlights section can analyze the viewer's past viewing history to identify preferred scenes from specific match situations and provide those as highlights. This improves the viewing experience by providing optimal highlights based on the viewer's past viewing history. Some or all of the above processing in the highlights section may be performed using AI or not. For example, the highlights section can input the viewer's past viewing history data into AI and have the AI ​​select the optimal highlights.

[0050] The highlights section can customize highlights based on the viewer's current interests and preferences when generating them. For example, if a viewer is interested in a particular player, the highlights section can customize the highlights to focus on that player. It can also customize the highlights to emphasize a specific play if the viewer is interested in that play. Furthermore, if a viewer is interested in a particular part of the match, the highlights section can customize the highlights to focus on that part. This improves the viewing experience by customizing highlights based on the viewer's current interests and preferences. Some or all of the above processing in the highlights section may be performed using AI or not. For example, the highlights section can input data on the viewer's current interests and preferences into an AI and have the AI ​​perform the highlight customization.

[0051] The highlights section can provide optimal highlights by considering the viewer's geographical location when generating highlights. For example, if the viewer is at the location, the highlights section can provide highlights that are close to the local perspective. Furthermore, if the viewer is in a specific region, the highlights section can provide highlights related to players or teams in that region. Additionally, if the viewer is overseas, the highlights section can provide highlights from an international perspective. This improves the viewing experience by providing optimal highlights based on the viewer's geographical location. Some or all of the above processing in the highlights section may be performed using AI or not. For example, the highlights section can input the viewer's geographical location information into AI and have the AI ​​select the optimal highlights.

[0052] The highlights section can analyze viewers' social media activity when generating highlights and provide relevant highlights. For example, if a viewer mentions a particular player on social media, the highlights section can provide highlights related to that player. It can also provide highlights related to a specific play if a viewer comments on that play. Furthermore, if a viewer discusses a particular part of a match, the highlights section can provide highlights related to that part. This improves the viewing experience by providing relevant highlights based on viewers' social media activity. Some or all of the above processing in the highlights section may be performed using AI or not. For example, the highlights section can input viewers' social media activity data into an AI and have the AI ​​select relevant highlights.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The camera switching unit can analyze the viewer's past viewing history and automatically suggest the optimal camera angle. For example, it can prioritize suggesting similar angles based on angles the viewer has previously enjoyed watching. Furthermore, if the viewer is interested in a particular player or play, it can suggest camera angles centered around that player or play. It can also analyze the viewer's past viewing history to suggest preferred angles in specific match situations. This improves the viewing experience by suggesting the optimal camera angle based on the viewer's past viewing history. Some or all of the above processing in the camera switching unit may be performed using AI, or not. For example, the camera switching unit can input the viewer's past viewing history data into an AI and have the AI ​​suggest the optimal camera angle.

[0055] The camera switching unit can filter camera angles based on the viewer's current interests and preferences. For example, if a viewer is interested in a particular player, the camera angle can be switched to focus on that player. If a viewer is interested in a particular play, an angle emphasizing that play can be selected. Furthermore, if a viewer is interested in a specific part of the game, the camera angle can be adjusted to focus on that part. This improves the viewing experience by adjusting the camera angle based on the viewer's current interests and preferences. Some or all of the above processing in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input viewer interest data into the AI ​​and have the AI ​​perform the camera angle filtering.

[0056] The camera switching unit can prioritize displaying angles that are highly relevant to the viewer's geographical location when switching camera angles. For example, if the viewer is at a specific location, it can prioritize displaying angles that are close to the viewer's local perspective. Furthermore, if the viewer is in a specific region, it can prioritize angles related to players or teams in that region. Additionally, if the viewer is overseas, it can prioritize displaying angles from an international perspective. This improves the viewing experience by displaying angles that are highly relevant based on the viewer's geographical location. Some or all of the above processing in the camera switching unit may be performed using AI, or not. For example, the camera switching unit can input the viewer's geographical location information into the AI ​​and have the AI ​​select the most relevant angles.

[0057] The camera switching unit can analyze viewers' social media activity and suggest relevant angles when switching camera angles. For example, if a viewer mentions a specific player on social media, it can suggest a camera angle that focuses on that player. It can also suggest an angle that emphasizes a specific play if the viewer comments on it. Furthermore, if a viewer discusses a particular part of a match, it can adjust the camera angle to focus on that part. This improves the viewing experience by suggesting relevant angles based on the viewer's social media activity. Some or all of the above processing in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input viewer social media activity data into an AI and have the AI ​​suggest relevant angles.

[0058] The commentary unit can adjust the level of detail in the commentary based on the importance of the match during the commentary generation process. For example, it can provide detailed commentary for important matches, and concise commentary for friendly or practice matches. Furthermore, it can provide particularly detailed commentary for tournament finals. By adjusting the level of detail in the commentary according to the importance of the match, the viewing experience is improved. Some or all of the above processing in the commentary unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the commentary unit can input match importance data into a generation AI and have the generation AI adjust the level of detail in the commentary.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The camera switching section allows viewers to freely switch camera angles. Viewers can select or change camera angles using a remote controller, smartphone, tablet, or voice commands. Step 2: The commentary section uses generative AI to analyze the camera footage selected by the viewer and provide personalized real-time commentary. The commentary section generates commentary based on viewing history and profile information, and adjusts the content of the commentary by analyzing the viewer's real-time reactions. It also collects viewer feedback to improve the quality of the commentary. Step 3: The display unit displays statistical data and background information tailored to the viewer's interests, based on the commentary provided by the commentary unit. The display unit shows statistical data such as player performance and match history, background information on the match, and player profiles, and updates the information in real time according to the viewer's interests. Step 4: The highlights section provides viewers with personalized highlights and player profiles after the match, based on the information displayed by the display section. The highlights section provides key scenes selected by viewers as highlights and customizes the content of the highlights according to the viewers' preferences. It also collects viewer feedback to improve the quality of the highlights.

[0061] (Example of form 2) The sports viewing system according to an embodiment of the present invention is a system that allows viewers to freely switch camera angles and displays real-time commentary and statistical data generated by a generating AI. This sports viewing system provides a sports viewing experience tailored to individual preferences by allowing viewers to freely switch camera angles and display real-time commentary and statistical data generated by a generating AI. In addition, it provides customizable highlights and player profiles after the match. For example, viewers can freely switch between multiple cameras installed on the court and enjoy the match from different perspectives. The generating AI analyzes the camera footage selected by the viewer in real time and provides personalized commentary. Statistical data and background information tailored to the viewer's interests are also displayed. After the match, highlights and player profiles tailored to the viewer's preferences are provided, and viewers can customize the highlights to suit their needs. This system allows viewers to enjoy the match from a perspective that suits their interests and preferences, and provides a more engaging viewing experience through personalized commentary and information. In addition, viewers can enjoy highlights tailored to their preferences after the match, which improves viewer satisfaction. Thus, the sports viewing system can provide a sports viewing experience tailored to the viewer's preferences.

[0062] The sports viewing system according to this embodiment comprises a camera switching unit, a commentary unit, a display unit, and a highlight unit. The camera switching unit allows viewers to freely switch camera angles. For example, viewers can select camera angles using a remote controller. The camera switching unit also allows viewers to switch camera angles using a smartphone or tablet. Furthermore, viewers can change camera angles using voice commands. The commentary unit uses generation AI to analyze the camera footage selected by the viewer and provides personalized real-time commentary. For example, the commentary unit generates commentary tailored to the viewer's interests based on the viewer's viewing history and profile information. The commentary unit can also analyze viewers' real-time reactions and adjust the content of the commentary. Furthermore, the commentary unit can collect viewer feedback and use it to improve the quality of the commentary. The display unit displays statistical data and background information tailored to the viewer's interests based on the commentary provided by the commentary unit. For example, the display unit displays statistical data such as player performance and match history. The display unit can also display information such as background information on the match and player profiles. Furthermore, the display unit can update information in real time according to the viewer's interests. The highlight unit provides highlights and player profiles tailored to the viewer's preferences after the match, based on the information displayed by the display unit. For example, the highlight unit provides important scenes selected by the viewer as highlights. The highlight unit can also customize the content of the highlights according to the viewer's preferences. In addition, the highlight unit can collect viewer feedback and use it to improve the quality of the highlights. As a result, the sports viewing system according to this embodiment can provide a sports viewing experience tailored to the viewer's preferences.

[0063] The camera switching unit allows viewers to freely switch camera angles. Specifically, viewers can use a remote controller to select from multiple camera angles. For example, they can enjoy the match from various perspectives, such as a camera that overlooks the entire stadium, a camera that focuses on a specific player, or a camera that is close to the goal. The camera switching unit can also be used to switch camera angles using a smartphone or tablet, allowing viewers to intuitively change camera angles by operating the touchscreen. Furthermore, it is also possible to change camera angles using voice commands, so viewers can instantly switch to their desired angle simply by giving a voice command such as "Switch to the camera near the goal." This allows viewers to watch the match from the optimal viewpoint according to their preferences, resulting in a more immersive experience. The camera switching unit also has a function that records the viewer's operation history and automatically suggests preferred angles for the next viewing. For example, if a viewer frequently selected a camera angle that focused on a specific player in the past, the unit will prioritize displaying a similar angle in the next match, improving viewer convenience. Furthermore, the camera switching unit is designed to allow for smooth switching even when multiple viewers select different angles simultaneously, providing a comfortable experience when watching with family and friends. This enables flexible camera angle selection to meet the diverse needs of viewers, further enhancing the enjoyment of watching sports.

[0064] The commentary team uses generative AI to analyze camera footage selected by viewers and provide personalized real-time commentary. Specifically, the generative AI generates commentary tailored to the viewer's interests based on their viewing history and profile information. For example, viewers interested in a particular player will receive detailed commentary on that player's play, while viewers interested in tactics and techniques will receive detailed commentary on the tactical aspects of the match. The generative AI can also analyze viewers' real-time reactions and dynamically adjust the content of the commentary. For example, if a viewer is excited about a particular play, additional information and background details about that play can be provided to further pique their interest. Furthermore, the commentary team collects viewer feedback and uses it to improve the quality of the commentary. By providing ratings and comments on the commentary, viewers can learn from this feedback and improve the content of future commentaries. This allows the commentary team to consistently provide high-quality commentary that meets the needs of viewers. The commentary team supports multiple languages ​​and can provide commentary according to the viewer's language settings. For example, by providing real-time commentary in the viewer's selected language, such as English, Spanish, or Japanese, it can also cater to an international audience. Furthermore, the commentary section includes a feature that allows viewers to request detailed explanations of specific plays or scenes, enabling it to provide in-depth information on moments that interest viewers. This allows the commentary section to provide personalized commentary tailored to viewers' interests, further enhancing the enjoyment of watching sports.

[0065] The display unit shows statistical data and background information tailored to the viewer's interests, based on the commentary provided by the commentary unit. Specifically, it can display statistical data such as player statistics and match history. For example, by visually displaying a player's past performance or real-time results in the current match, viewers can gain a deeper understanding of the match's progress. The display unit can also display background information about the match and player profiles. This makes it easier for viewers to understand the context of the match and the players' backgrounds, allowing them to become more immersed in the game. Furthermore, the display unit can update information in real time according to the viewer's interests. For example, if an important event occurs during a match, it can instantly display information related to that event, providing viewers with the latest information. The display unit also has a function that allows viewers to customize the information displayed, so viewers can select what is displayed according to their interests. For example, it is possible to set it to display only the statistics of a specific player or specific statistical data, allowing viewers to obtain information tailored to their preferences. The display unit can also display links and buttons that provide more detailed information on information that the viewer is interested in. This allows viewers to delve deeper into the information that interests them, resulting in a more fulfilling viewing experience. The display unit can continuously improve its content based on viewer feedback, providing optimal information tailored to viewer needs. This allows the display unit to provide statistical data and background information aligned with viewer interests, further enhancing the enjoyment of watching sports.

[0066] The highlights section provides viewers with personalized highlights and player profiles after the match, based on the information displayed by the display section. Specifically, it can provide highlights of important scenes selected by the viewer. For example, it can create highlight videos compiling moments that viewers found particularly interesting, such as goals or decisive plays, and provide them to viewers after the match. The highlights section can also customize the content of the highlights according to the viewer's preferences. For example, it can customize highlights that only feature the plays of a specific player or highlights that compile important scenes from a specific match, according to the viewer's requests. Furthermore, the highlights section can collect viewer feedback and use it to improve the quality of the highlights. By providing ratings and comments on the highlights, viewers can have them reflected in the creation of future highlights. This ensures that the highlights section always provides high-quality highlights that meet the needs of viewers. The highlights section also has a feature that allows viewers to share highlights, so viewers can share their favorite highlight videos with friends and family via social media and messaging apps. This makes it easier for viewers to share the excitement and emotion of the match with others, expanding the enjoyment of watching sports. The highlights section also provides a feature that allows viewers to save highlights from past matches as an archive and play them at any time. This allows viewers to enjoy their favorite matches and players' performances over and over again. The highlights section can then provide highlights and player profiles tailored to the viewer's preferences, further enhancing the enjoyment of watching sports.

[0067] The commentary unit can analyze the camera footage selected by the viewer and provide personalized real-time commentary. For example, the commentary unit can generate commentary tailored to the viewer's interests based on their viewing history and profile information. The commentary unit can also analyze the viewer's real-time reactions and adjust the content of the commentary. Furthermore, the commentary unit can collect viewer feedback and use it to improve the quality of the commentary. This enhances the viewing experience by providing real-time commentary based on the viewer's choices. Some or all of the above processing in the commentary unit may be performed using generative AI or not. For example, the commentary unit can input the camera footage selected by the viewer into the generative AI and have the generative AI execute personalized real-time commentary.

[0068] The display unit can show statistical data and background information tailored to the viewer's interests. For example, the display unit can show statistical data such as player statistics and match history. It can also show information such as background information on matches and player profiles. Furthermore, the display unit can update information in real time according to the viewer's interests. This improves the viewing experience by providing information tailored to the viewer's interests. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input statistical data and background information tailored to the viewer's interests into the AI ​​and have the AI ​​select the information to display.

[0069] The highlights section can provide viewers with personalized highlights and player profiles after the match. For example, it can provide highlights of key scenes selected by the viewer. The highlights section can also customize the content of the highlights according to the viewer's preferences. Furthermore, the highlights section can collect viewer feedback and use it to improve the quality of the highlights. This enhances the viewing experience by providing highlights tailored to the viewer's preferences. Some or all of the above processing in the highlights section may be performed using AI or not. For example, the highlights section can have AI generate highlights tailored to the viewer's preferences.

[0070] The camera switching unit can estimate the viewer's emotions and adjust the timing of camera angle changes based on the estimated viewer emotions. For example, if the viewer is excited, the camera switching unit can frequently switch camera angles to emphasize action scenes. Alternatively, if the viewer is relaxed, the camera switching unit can maintain a fixed angle for extended periods, providing a stable viewing experience. Furthermore, if the viewer is tense, the camera switching unit can appropriately adjust the camera angle to focus on important moments. This improves the viewing experience by adjusting the timing of camera angle changes according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input viewer emotion data into a generative AI and have the generative AI execute the timing of camera angle changes.

[0071] The camera switching unit can analyze a viewer's past viewing history and automatically suggest the optimal camera angle. For example, the camera switching unit can prioritize suggesting similar angles based on angles the viewer has previously preferred to watch. Furthermore, if the viewer is interested in a particular player or play, the camera switching unit can suggest camera angles centered on that player or play. In addition, the camera switching unit can analyze the viewer's past viewing history to suggest preferred angles in specific match situations. This improves the viewing experience by suggesting the optimal camera angle based on the viewer's past viewing history. Some or all of the above processing in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input the viewer's past viewing history data into AI and have the AI ​​suggest the optimal camera angle.

[0072] The camera switching unit can filter camera angles based on the viewer's current interests and preferences. For example, if a viewer is interested in a particular player, the camera switching unit will switch the camera angle to focus on that player. It can also select an angle that emphasizes a particular play if the viewer is interested in that play. Furthermore, if a viewer is interested in a particular part of the game, the camera switching unit can adjust the camera angle to focus on that part. This improves the viewing experience by adjusting the camera angle based on the viewer's current interests and preferences. Some or all of the above processing in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input data on the viewer's current interests and preferences into an AI and have the AI ​​perform the camera angle filtering.

[0073] The camera switching unit can estimate the viewer's emotions and prioritize camera angles based on those emotions. For example, if the viewer is excited, the camera switching unit may prioritize displaying action scenes. It may also prioritize wide-angle shots that show the overall flow if the viewer is relaxed. Furthermore, if the viewer is tense, the camera switching unit may prioritize close-up shots that capture important moments. This improves the viewing experience by prioritizing camera angles according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input viewer emotion data into a generative AI and have the generative AI prioritize camera angles.

[0074] The camera switching unit can prioritize displaying angles that are highly relevant to the viewer's geographical location when switching camera angles. For example, if the viewer is at a specific location, the camera switching unit will prioritize displaying angles that are close to the viewer's local perspective. Furthermore, if the viewer is in a specific region, the camera switching unit can prioritize angles related to players or teams in that region. Additionally, if the viewer is overseas, the camera switching unit can prioritize displaying angles from an international perspective. This improves the viewing experience by displaying angles that are highly relevant based on the viewer's geographical location. Some or all of the above processing in the camera switching unit may be performed using AI, or not. For example, the camera switching unit can input the viewer's geographical location information into AI and have the AI ​​select the most relevant angles.

[0075] The camera switching unit can analyze viewers' social media activity and suggest relevant angles when switching camera angles. For example, if a viewer mentions a specific player on social media, the camera switching unit can suggest a camera angle centered on that player. It can also suggest an angle that emphasizes a specific play if the viewer comments on that play. Furthermore, if a viewer discusses a particular part of a match, the camera switching unit can adjust the camera angle to focus on that part. This improves the viewing experience by suggesting relevant angles based on viewers' social media activity. Some or all of the above processing in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input viewers' social media activity data into an AI and have the AI ​​suggest relevant angles.

[0076] The commentary section can estimate the viewer's emotions and adjust the style of commentary based on those emotions. For example, if the viewer is excited, the commentary section can use an energetic style of commentary. If the viewer is relaxed, the commentary section can use a calm tone of commentary. Furthermore, if the viewer is tense, the commentary section can use a calm and detailed style of commentary. By adjusting the style of commentary according to the viewer's emotions, the viewing experience is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the commentary section may be performed using or without generative AI. For example, the commentary section can input viewer emotion data into a generative AI and have the generative AI adjust the style of commentary.

[0077] The commentary unit can adjust the level of detail in the commentary based on the importance of the match when generating the commentary. For example, the commentary unit can provide detailed commentary for important matches. It can also provide concise commentary for friendly or practice matches. Furthermore, it can provide particularly detailed commentary for tournament finals. By adjusting the level of detail in the commentary according to the importance of the match, the viewing experience is improved. Some or all of the above processing in the commentary unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the commentary unit can input match importance data into a generation AI and have the generation AI adjust the level of detail in the commentary.

[0078] The commentary unit can apply different commentary algorithms depending on the category of the match when generating commentary. For example, in a soccer match, the commentary unit can apply a commentary algorithm specialized in goal scenes and foul scenes. In a basketball match, it can also apply a commentary algorithm specialized in scoring scenes and rebound scenes. Furthermore, in a tennis match, it can apply a commentary algorithm specialized in rally length and serve speed. By applying a commentary algorithm according to the category of the match, the viewing experience is improved. Some or all of the above processing in the commentary unit may be performed using a generation AI, or not. For example, the commentary unit can input match category data into a generation AI and apply a commentary algorithm to the generation AI.

[0079] The commentary section can estimate the viewer's emotions and adjust the length of the commentary based on the estimated emotions. For example, if the viewer is excited, the commentary section can provide a short, concise commentary. If the viewer is relaxed, it can provide a detailed commentary. Furthermore, if the viewer is tense, it can provide a calm and concise commentary. By adjusting the length of the commentary according to the viewer's emotions, the viewing experience is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the commentary section may be performed using or without generative AI. For example, the commentary section can input viewer emotion data into a generative AI and have the generative AI adjust the length of the commentary.

[0080] The commentary unit can determine the priority of commentary based on the progress of the match when generating commentary. For example, in the early stages of the match, the commentary unit may prioritize commentary on player introductions and team strategies. In the middle stages of the match, it may also prioritize commentary on the flow of the game and important plays. Furthermore, in the later stages of the match, it may prioritize commentary on the match result and future prospects. This improves the viewing experience by determining the priority of commentary according to the progress of the match. Some or all of the above processing in the commentary unit may be performed using a generation AI, or not. For example, the commentary unit can input match progress data into a generation AI and have the generation AI determine the priority of commentary.

[0081] The commentary unit can adjust the order of commentary based on the relevance of the matches during the commentary generation process. For example, in important matches, the commentary unit can provide commentary in an order that follows the flow of the game. In friendly or practice matches, the commentary unit can also focus on important plays. Furthermore, in tournament finals, the commentary unit can prioritize commentary related to the match result. This improves the viewing experience by adjusting the order of commentary according to the relevance of the matches. Some or all of the above processing in the commentary unit may be performed using a generation AI, or not. For example, the commentary unit can input match relevance data into a generation AI and have the generation AI adjust the order of commentary.

[0082] The display unit can estimate the viewer's emotions and select statistical data and background information to display based on the estimated emotions. For example, if the viewer is excited, the display unit can highlight important statistical data. If the viewer is relaxed, the display unit can also display detailed background information. Furthermore, if the viewer is tense, the display unit can display concise and to-the-point statistical data. This improves the viewing experience by selecting information to display according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input viewer emotion data into a generative AI and have the generative AI select the statistical data and background information to display.

[0083] The display unit can display the most relevant information by referring to the viewer's past viewing history. For example, the display unit can prioritize displaying statistical data that the viewer has shown interest in in the past. The display unit can also display background information that the viewer has shown interest in in the past. Furthermore, the display unit can display information related to a specific match situation based on the viewer's past viewing history. This improves the viewing experience by displaying the most relevant information based on the viewer's past viewing history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the viewer's past viewing history data into AI and have the AI ​​select the most relevant information.

[0084] The display unit can customize information based on the viewer's current interests and preferences when displaying it. For example, if a viewer is interested in a particular player, the display unit can display statistical data related to that player. It can also display background information related to a particular play if the viewer is interested in that play. Furthermore, if a viewer is interested in a particular part of a match, the display unit can display information related to that part. This improves the viewing experience by customizing information based on the viewer's current interests and preferences. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input data on the viewer's current interests and preferences into the AI ​​and have the AI ​​perform the information customization.

[0085] The display unit can estimate the viewer's emotions and determine the priority of information to display based on the estimated emotions. For example, if the viewer is excited, the display unit may prioritize displaying important statistical data. It may also prioritize displaying detailed background information if the viewer is relaxed. Furthermore, if the viewer is tense, the display unit may prioritize displaying concise and to-the-point information. This improves the viewing experience by prioritizing information according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input viewer emotion data into a generative AI and have the generative AI determine the priority of information.

[0086] The display unit can prioritize displaying highly relevant information by considering the viewer's geographical location. For example, if the viewer is in a specific location, the display unit will prioritize displaying information that is closer to the local perspective. Furthermore, if the viewer is in a specific region, the display unit can prioritize information about players or teams related to that region. Additionally, if the viewer is overseas, the display unit can prioritize displaying information from an international perspective. This improves the viewing experience by displaying highly relevant information based on the viewer's geographical location. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input the viewer's geographical location information into AI and have the AI ​​select highly relevant information.

[0087] The display unit can analyze the viewer's social media activity and display relevant information when displaying content. For example, if a viewer mentions a particular player on social media, the display unit can display information related to that player. It can also display information related to a specific play if the viewer comments on that play. Furthermore, if a viewer discusses a particular part of a match, the display unit can display information related to that part. This improves the viewing experience by displaying relevant information based on the viewer's social media activity. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the viewer's social media activity data into an AI and have the AI ​​select relevant information.

[0088] The highlight section can estimate the viewer's emotions and select highlights based on those emotions. For example, if the viewer is excited, the highlight section will select highlights that focus on action scenes. If the viewer is relaxed, the highlight section can also select highlights that showcase the overall flow. Furthermore, if the viewer is tense, the highlight section can select highlights that capture important moments. This improves the viewing experience by selecting highlights according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the highlight section may be performed using AI or not. For example, the highlight section can input viewer emotion data into a generative AI and have the generative AI perform the highlight selection.

[0089] The highlights section can provide optimal highlights by referencing the viewer's past viewing history when generating highlights. For example, the highlights section can provide similar scenes as highlights based on scenes the viewer has enjoyed watching in the past. Furthermore, if the viewer is interested in a particular player or play, the highlights section can focus on that player or play. In addition, the highlights section can analyze the viewer's past viewing history to identify preferred scenes from specific match situations and provide those as highlights. This improves the viewing experience by providing optimal highlights based on the viewer's past viewing history. Some or all of the above processing in the highlights section may be performed using AI or not. For example, the highlights section can input the viewer's past viewing history data into AI and have the AI ​​select the optimal highlights.

[0090] The highlights section can customize highlights based on the viewer's current interests and preferences when generating them. For example, if a viewer is interested in a particular player, the highlights section can customize the highlights to focus on that player. It can also customize the highlights to emphasize a specific play if the viewer is interested in that play. Furthermore, if a viewer is interested in a particular part of the match, the highlights section can customize the highlights to focus on that part. This improves the viewing experience by customizing highlights based on the viewer's current interests and preferences. Some or all of the above processing in the highlights section may be performed using AI or not. For example, the highlights section can input data on the viewer's current interests and preferences into an AI and have the AI ​​perform the highlight customization.

[0091] The highlight section can estimate the viewer's emotions and prioritize highlights based on those emotions. For example, if the viewer is excited, the highlight section may prioritize action scenes as highlights. If the viewer is relaxed, the highlight section may prioritize scenes that show the overall flow as highlights. Furthermore, if the viewer is tense, the highlight section may prioritize scenes that capture important moments as highlights. This improves the viewing experience by prioritizing highlights according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the highlight section may be performed using AI or not. For example, the highlight section can input viewer emotion data into a generative AI and have the generative AI determine the priority of highlights.

[0092] The highlights section can provide optimal highlights by considering the viewer's geographical location when generating highlights. For example, if the viewer is at the location, the highlights section can provide highlights that are close to the local perspective. Furthermore, if the viewer is in a specific region, the highlights section can provide highlights related to players or teams in that region. Additionally, if the viewer is overseas, the highlights section can provide highlights from an international perspective. This improves the viewing experience by providing optimal highlights based on the viewer's geographical location. Some or all of the above processing in the highlights section may be performed using AI or not. For example, the highlights section can input the viewer's geographical location information into AI and have the AI ​​select the optimal highlights.

[0093] The highlights section can analyze viewers' social media activity when generating highlights and provide relevant highlights. For example, if a viewer mentions a particular player on social media, the highlights section can provide highlights related to that player. It can also provide highlights related to a specific play if a viewer comments on that play. Furthermore, if a viewer discusses a particular part of a match, the highlights section can provide highlights related to that part. This improves the viewing experience by providing relevant highlights based on viewers' social media activity. Some or all of the above processing in the highlights section may be performed using AI or not. For example, the highlights section can input viewers' social media activity data into an AI and have the AI ​​select relevant highlights.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The camera switching unit can estimate the viewer's emotions and adjust the timing of camera angle changes based on the estimated viewer emotions. For example, if the viewer is excited, the camera angle can be changed frequently to emphasize action scenes. If the viewer is relaxed, a fixed angle can be maintained for a long period to provide a stable viewing experience. Furthermore, if the viewer is tense, the camera angle can be appropriately adjusted to focus on important moments. In this way, the viewing experience is improved by adjusting the timing of camera angle changes according to the viewer's emotions. 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 camera switching unit may be performed using AI or not. For example, the camera switching unit can input viewer emotion data into a generative AI and have the generative AI execute the timing of camera angle changes.

[0096] The commentary section can estimate the viewer's emotions and adjust the style of commentary based on those emotions. For example, if the viewer is excited, the commentary can be delivered in an energetic style. If the viewer is relaxed, the commentary can be delivered in a calm tone. Furthermore, if the viewer is tense, the commentary can be delivered in a calm and detailed style. By adjusting the style of commentary according to the viewer's emotions, the viewing experience is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the commentary section may be performed using generative AI or not. For example, the commentary section can input viewer emotion data into a generative AI and have the generative AI adjust the style of commentary.

[0097] The display unit can estimate the viewer's emotions and select statistical data and background information to display based on the estimated emotions. For example, if the viewer is excited, important statistical data can be highlighted. If the viewer is relaxed, detailed background information can be displayed. Furthermore, if the viewer is tense, concise and to-the-point statistical data can be displayed. This improves the viewing experience by selecting information to display according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input viewer emotion data into a generative AI and have the generative AI select the statistical data and background information to display.

[0098] The highlights section can estimate the viewer's emotions and select highlights based on those emotions. For example, if the viewer is excited, the highlights can focus on action scenes. If the viewer is relaxed, the highlights can focus on the overall flow of the story. Furthermore, if the viewer is tense, the highlights can focus on capturing important moments. By selecting highlights according to the viewer's emotions, the viewing experience is enhanced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the highlights section may be performed using AI or not. For example, the highlights section can input viewer emotion data into a generative AI and have the generative AI perform the highlight selection.

[0099] The display unit can estimate the viewer's emotions and determine the priority of information to display based on the estimated viewer emotions. For example, if the viewer is excited, important statistical data can be displayed preferentially. If the viewer is relaxed, detailed background information can be displayed preferentially. Furthermore, if the viewer is tense, concise and to-the-point information can be displayed preferentially. This improves the viewing experience by determining the priority of information to display according to the viewer's emotions. 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 display unit may be performed using AI or not. For example, the display unit can input viewer emotion data into a generative AI and have the generative AI determine the priority of information.

[0100] The camera switching unit can analyze the viewer's past viewing history and automatically suggest the optimal camera angle. For example, it can prioritize suggesting similar angles based on angles the viewer has previously enjoyed watching. Furthermore, if the viewer is interested in a particular player or play, it can suggest camera angles centered around that player or play. It can also analyze the viewer's past viewing history to suggest preferred angles in specific match situations. This improves the viewing experience by suggesting the optimal camera angle based on the viewer's past viewing history. Some or all of the above processing in the camera switching unit may be performed using AI, or not. For example, the camera switching unit can input the viewer's past viewing history data into an AI and have the AI ​​suggest the optimal camera angle.

[0101] The camera switching unit can filter camera angles based on the viewer's current interests and preferences. For example, if a viewer is interested in a particular player, the camera angle can be switched to focus on that player. If a viewer is interested in a particular play, an angle emphasizing that play can be selected. Furthermore, if a viewer is interested in a specific part of the game, the camera angle can be adjusted to focus on that part. This improves the viewing experience by adjusting the camera angle based on the viewer's current interests and preferences. Some or all of the above processing in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input viewer interest data into the AI ​​and have the AI ​​perform the camera angle filtering.

[0102] The camera switching unit can prioritize displaying angles that are highly relevant to the viewer's geographical location when switching camera angles. For example, if the viewer is at a specific location, it can prioritize displaying angles that are close to the viewer's local perspective. Furthermore, if the viewer is in a specific region, it can prioritize angles related to players or teams in that region. Additionally, if the viewer is overseas, it can prioritize displaying angles from an international perspective. This improves the viewing experience by displaying angles that are highly relevant based on the viewer's geographical location. Some or all of the above processing in the camera switching unit may be performed using AI, or not. For example, the camera switching unit can input the viewer's geographical location information into the AI ​​and have the AI ​​select the most relevant angles.

[0103] The camera switching unit can analyze viewers' social media activity and suggest relevant angles when switching camera angles. For example, if a viewer mentions a specific player on social media, it can suggest a camera angle that focuses on that player. It can also suggest an angle that emphasizes a specific play if the viewer comments on it. Furthermore, if a viewer discusses a particular part of a match, it can adjust the camera angle to focus on that part. This improves the viewing experience by suggesting relevant angles based on the viewer's social media activity. Some or all of the above processing in the camera switching unit may be performed using AI or not. For example, the camera switching unit can input viewer social media activity data into an AI and have the AI ​​suggest relevant angles.

[0104] The commentary unit can adjust the level of detail in the commentary based on the importance of the match during the commentary generation process. For example, it can provide detailed commentary for important matches, and concise commentary for friendly or practice matches. Furthermore, it can provide particularly detailed commentary for tournament finals. By adjusting the level of detail in the commentary according to the importance of the match, the viewing experience is improved. Some or all of the above processing in the commentary unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the commentary unit can input match importance data into a generation AI and have the generation AI adjust the level of detail in the commentary.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The camera switching section allows viewers to freely switch camera angles. Viewers can select or change camera angles using a remote controller, smartphone, tablet, or voice commands. Step 2: The commentary section uses generative AI to analyze the camera footage selected by the viewer and provide personalized real-time commentary. The commentary section generates commentary based on viewing history and profile information, and adjusts the content of the commentary by analyzing the viewer's real-time reactions. It also collects viewer feedback to improve the quality of the commentary. Step 3: The display unit displays statistical data and background information tailored to the viewer's interests, based on the commentary provided by the commentary unit. The display unit shows statistical data such as player performance and match history, background information on the match, and player profiles, and updates the information in real time according to the viewer's interests. Step 4: The highlights section provides viewers with personalized highlights and player profiles after the match, based on the information displayed by the display section. The highlights section provides key scenes selected by viewers as highlights and customizes the content of the highlights according to the viewers' preferences. It also collects viewer feedback to improve the quality of the highlights.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the camera switching unit, commentary unit, display unit, and highlight unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the camera switching unit is implemented by the control unit 46A of the smart device 14, allowing viewers to select camera angles using a remote controller or smartphone. The commentary unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses generating AI to analyze the camera footage selected by the viewer and provides personalized real-time commentary. The display unit is implemented by, for example, the display 40A of the smart device 14, which displays statistical data and background information tailored to the viewer's interests. The highlight unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides highlights and player profiles tailored to the viewer's preferences after the match ends. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the camera switching unit, commentary unit, display unit, and highlight unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the camera switching unit is implemented by the control unit 46A of the smart glasses 214, allowing viewers to select camera angles using the smart glasses. The commentary unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses generating AI to analyze the camera footage selected by the viewer and provides personalized real-time commentary. The display unit is implemented by, for example, the display of the smart glasses 214, which displays statistical data and background information tailored to the viewer's interests. The highlight unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides highlights and player profiles tailored to the viewer's preferences after the match ends. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the camera switching unit, commentary unit, display unit, and highlight unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the camera switching unit is implemented by the control unit 46A of the headset terminal 314, allowing viewers to select camera angles using the headset. The commentary unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses generating AI to analyze the camera footage selected by the viewer and provides personalized real-time commentary. The display unit is implemented by, for example, the display 343 of the headset terminal 314, which displays statistical data and background information tailored to the viewer's interests. The highlight unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides highlights and player profiles tailored to the viewer's preferences after the match ends. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the camera switching unit, commentary unit, display unit, and highlight unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the camera switching unit is implemented by the control unit 46A of the robot 414, allowing viewers to select camera angles using the robot. The commentary unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses generating AI to analyze the camera footage selected by the viewer and provides personalized real-time commentary. The display unit is implemented by, for example, the display of the robot 414, which displays statistical data and background information tailored to the viewer's interests. The highlight unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides highlights and player profiles tailored to the viewer's preferences after the match ends. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A camera switching unit that allows viewers to freely switch camera angles, The camera switching unit analyzes the camera image selected by the aforementioned camera switching unit and provides a personalized real-time commentary. Based on the explanation provided by the aforementioned commentary unit, a display unit displays statistical data and background information tailored to the viewer's interests. The system includes a highlight unit that, based on the information displayed by the aforementioned display unit, provides highlights and player profiles tailored to the viewer's preferences after the match has ended. A system characterized by the following features. (Note 2) The aforementioned explanatory section is, It analyzes the camera footage selected by the viewer and provides personalized real-time commentary. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is Display statistical data and background information tailored to the viewer's interests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned highlight section is, After the match, we provide highlights and player profiles tailored to the viewer's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned camera switching unit is The system estimates the viewer's emotions and adjusts the timing of camera angle changes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned camera switching unit is It analyzes the viewer's past viewing history and automatically suggests the optimal camera angle. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned camera switching unit is When switching camera angles, filtering is performed based on the viewer's current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned camera switching unit is The system estimates the viewer's emotions and prioritizes camera angles based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned camera switching unit is When switching camera angles, the system prioritizes displaying the most relevant angles, taking into account the viewer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned camera switching unit is When switching camera angles, the system analyzes viewers' social media activity and suggests relevant angles. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned explanatory section is, We estimate the audience's emotions and adjust the way we present the commentary based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned explanatory section is, When generating commentary, adjust the level of detail based on the importance of the match. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned explanatory section is, When generating commentary, different commentary algorithms are applied depending on the match category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned explanatory section is, The system estimates the viewer's emotions and adjusts the length of the commentary based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned explanatory section is, When generating commentary, the priority of the commentary is determined based on the progress of the match. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned explanatory section is, When generating commentary, the order of commentary is adjusted based on the relevance of the matches. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is The system estimates the viewer's emotions and selects statistical data and background information to display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is When displaying content, the system refers to the viewer's past viewing history to show the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is When displayed, the information is customized based on the viewer's current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is It estimates the viewer's emotions and determines the priority of information to display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying content, the system prioritizes showing the most relevant information, taking into account the viewer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is When displayed, the system analyzes the viewer's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned highlight section is, The program estimates the audience's emotions and selects highlights based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned highlight section is, When generating highlights, the program provides the most suitable highlights by referencing the viewer's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned highlight section is, When generating highlights, customize them based on the viewer's current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned highlight section is, It estimates the viewer's emotions and determines the priority of highlights based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned highlight section is, When generating highlights, the program takes into account the viewer's geographical location to provide the most suitable highlights. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned highlight section is, When generating highlights, the system analyzes the audience's social media activity and provides relevant highlights. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A camera switching unit that allows viewers to freely switch camera angles, The camera switching unit analyzes the camera image selected by the aforementioned camera switching unit and provides a personalized real-time commentary. Based on the explanation provided by the aforementioned commentary unit, a display unit displays statistical data and background information tailored to the viewer's interests. The system includes a highlight unit that, based on the information displayed by the aforementioned display unit, provides highlights and player profiles tailored to the viewer's preferences after the match has ended. A system characterized by the following features.

2. The aforementioned explanatory section is, It analyzes the camera footage selected by the viewer and provides personalized real-time commentary. The system according to feature 1.

3. The aforementioned display unit is Display statistical data and background information tailored to the viewer's interests. The system according to feature 1.

4. The aforementioned highlight section is, After the match, we provide highlights and player profiles tailored to the viewer's preferences. The system according to feature 1.

5. The aforementioned camera switching unit is The system estimates the viewer's emotions and adjusts the timing of camera angle changes based on those estimated emotions. The system according to feature 1.

6. The aforementioned camera switching unit is It analyzes the viewer's past viewing history and automatically suggests the optimal camera angle. The system according to feature 1.

7. The aforementioned camera switching unit is When switching camera angles, filtering is performed based on the viewer's current interests and concerns. The system according to feature 1.

8. The aforementioned camera switching unit is The system estimates the viewer's emotions and prioritizes camera angles based on those estimated emotions. The system according to feature 1.

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