System

Abstract

An object of the system according to the embodiment is to automatically extract a super play from a sports game video and generate a digest moving image.SOLUTION: A system according to an embodiment includes a video analysis unit, a play extraction unit, and a moving image generation unit. The video analysis unit analyzes the game video. The play extraction unit extracts a super play from the game video analyzed by the video analysis unit. The moving image generation unit generates a digest moving image based on the super play extracted by the play extraction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to efficiently extract highlights and super plays from sports game footage and generate digest videos.

[0005] The system according to the embodiment aims to automatically extract great plays from sports game footage and generate a digest video. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a play extraction unit, and a video generation unit. The video analysis unit analyzes game video. The play extraction unit extracts great plays from the game video analyzed by the video analysis unit. The video generation unit generates a digest video based on the great plays extracted by the play extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically extract great plays from sports game footage and generate a digest video. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The automatic correction system according to an embodiment of the present invention uses a generation AI to automatically extract super plays that are highlights from sports games around the world and generate a digest video of them. This allows sports fans to efficiently watch only the highlights of each game from among countless games.

[0029] An automatic correction system according to an embodiment includes a video analysis unit, a play extraction unit, and a video generation unit. The video analysis unit analyzes game video. For example, the video analysis unit analyzes game video of soccer, baseball, basketball, tennis, or the like to understand game context, such as the flow of the game, the score, player movements, and audience reactions. The play extraction unit extracts impressive plays from the game video analyzed by the video analysis unit. For example, the play extraction unit extracts impressive plays, such as soccer goal scenes, baseball home runs, and basketball slam dunks. The video generation unit generates a digest video based on the impressive plays extracted by the play extraction unit. For example, the video generation unit effectively edits the extracted impressive plays to generate a visually appealing digest video. As a result, the automatic correction system according to an embodiment can automatically extract highlights from sports games and generate a digest video, thereby improving the viewing experience of sports fans.

[0030] The video analysis unit can analyze the reactions of the spectators during a game and narrow down candidates for great plays based on the volume and frequency of cheers and boos. For example, the video analysis unit can analyze the volume of cheers and boos from the spectators during a game to narrow down candidates for great plays. For example, it can identify the moment when the cheers are at their loudest and extract that scene as a great play. In this way, by narrowing down the great plays based on the reactions of the spectators, it is possible to provide more visually appealing highlights.

[0031] The video analysis unit can analyze the live audio of a game and identify important plays based on the commentator's level of excitement and changes in tone. For example, the video analysis unit can analyze the live audio of a game and identify important plays based on the commentator's level of excitement and changes in tone. For example, the video analysis unit can extract the moment when the commentator's voice rises as a super play. In this way, by identifying important plays based on the commentator's level of excitement and changes in tone, it is possible to provide more interesting highlights for viewers.

[0032] The video analysis unit can simultaneously analyze game footage from different sports and find common patterns of super plays. For example, the video analysis unit can simultaneously analyze game footage from soccer and basketball games and find common patterns of super plays. For example, it can compare the moments of goal scenes and slam dunks. This allows the unit to analyze game footage from different sports and find common patterns of super plays, thereby providing a wider variety of highlights.

[0033] The video analysis unit can analyze interview videos and practice videos of players in addition to game videos, and track fluctuations in player performance. The video analysis unit, for example, analyzes interview videos of players in addition to game videos, and tracks fluctuations in performance. For example, the psychological state of players is analyzed based on the content of interviews before and after a game. In this way, by analyzing interview videos and practice videos of players, fluctuations in player performance can be tracked.

[0034] The play extraction unit can analyze biometric data such as a player's heart rate and body temperature to identify the moment when tension or excitement reaches its peak. For example, the play extraction unit can use generative AI to analyze a player's heart rate data to identify the moment when tension or excitement reaches its peak. For example, the moment when heart rate rises sharply can be extracted as a super play. In this way, by analyzing a player's biometric data and identifying the moment when tension or excitement reaches its peak, more dramatic highlights can be provided.

[0035] The play extraction unit can prioritize extracting important plays that will change the flow of the game, taking into account the score fluctuations and time of day. The play extraction unit, for example, analyzes the score fluctuations of the game and prioritizes extracting important plays that will change the flow of the game. For example, scenes such as tying goals and game-winning home runs are extracted as super plays. In this way, by prioritizing the extraction of important plays that will change the flow of the game, taking into account the score fluctuations and time of day, it is possible to provide viewers with more interesting highlights.

[0036] The play extraction unit can analyze spectators' social media posts in real time and identify plays that are currently trending. For example, the play extraction unit can analyze spectators' social media posts in real time and identify plays that are currently trending. For example, the moment when the number of posts about a particular play suddenly increases can be extracted as a super play. In this way, by analyzing spectators' social media posts in real time and identifying plays that are currently trending, it is possible to provide viewers with more interesting highlights.

[0037] The play extraction unit can compare super plays from different sports and extract common techniques and tactics. For example, the play extraction unit compares super plays from soccer and basketball and extracts common techniques and tactics. For example, it compares dribbling breakthroughs and feint techniques. In this way, by comparing super plays from different sports and extracting common techniques and tactics, a wider variety of highlights can be provided to viewers.

[0038] When extracting great plays, the play extraction unit can refer to the players' past performance data and highlight plays that are good at specific players. The play extraction unit, for example, refers to the players' past performance data and highlights plays that are good at specific players. For example, it extracts shots and passes that are good at specific players as great plays. In this way, by referring to the players' past performance data and highlighting plays that are good at specific players, it is possible to provide more interesting highlights for viewers.

[0039] The video generation unit can generate a digest video with a storyline that includes the scenes before and after the super play. The video generation unit, for example, uses a generation AI to generate a digest video with a storyline that includes the scenes before and after the super play. For example, it can include passing and defensive scenes before and after a goal. In this way, by generating a digest video with a storyline that includes the scenes before and after the super play, it is possible to provide more attractive highlights to viewers.

[0040] The video generation unit automatically adds comments from players and coaches to the digest video, providing viewers with a deeper understanding. The video generation unit, for example, uses generation AI to automatically add comments from players and coaches to the digest video. For example, it inserts a player's comment after a goal is scored. In this way, by automatically adding comments from players and coaches to the digest video, viewers can be provided with a deeper understanding.

[0041] The video generation unit can generate a cross-sports digest video that combines super plays from different sports. For example, the video generation unit generates a cross-sports digest video that combines super plays from soccer and basketball. For example, goal scenes and slam dunks are edited alternately. In this way, a cross-sports digest video that combines super plays from different sports can be generated, providing viewers with a wider variety of highlights.

[0042] The video generation unit can add game statistics data and player profile information to the digest video to provide visually rich content. The video generation unit, for example, adds game statistics data to the digest video to provide visually rich content. For example, the number of goals scored and the number of assists are displayed. In this way, by adding game statistics data and player profile information to the digest video, it is possible to provide richer content for viewers.

[0043] The video analysis unit can analyze game footage to understand game context such as the flow of the game, the score, player movements, and audience reactions. For example, the video analysis unit uses generative AI to analyze game footage to understand game context such as the flow of the game, the score, player movements, and audience reactions. For example, it analyzes important scenes and player movements in the game to understand the flow of the game. This allows the unit to provide more accurate highlights by analyzing game footage and understanding game context such as the flow of the game, the score, player movements, and audience reactions.

[0044] The play extraction unit can extract the moment of a specified super play from the game video. The play extraction unit extracts, for example, the moment of a specified super play from the game video. For example, it extracts the moment of a goal or a home run. By extracting the moment of a specified super play from the game video, it is possible to provide more attractive highlights to viewers.

[0045] The video generation unit can generate a digest video based on the extracted moments of the super plays. The video generation unit generates a digest video based on the extracted moments of the super plays, for example. For example, the digest video is created by editing the moments of goal scenes and slam dunks. In this way, by generating a digest video based on the extracted moments of the super plays, it is possible to provide more attractive highlights to viewers.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The video analysis unit can analyze not only the movements of players during a match, but also footage of players warming up and cooling down before and after a match. For example, by analyzing a player's movements during pre-match warm-ups and their facial expressions during post-match cool-downs, it is possible to understand the player's condition and psychological state. This makes it possible to provide more comprehensive highlights that include the player's condition before and after a match.

[0048] The video analysis unit can simultaneously analyze footage of different sports matches and find common patterns of great plays. For example, it can simultaneously analyze footage of soccer and basketball matches and find common patterns of great plays. This allows it to analyze footage of different sports matches and find common patterns of great plays, thereby providing a wider variety of highlights.

[0049] The video analysis unit can analyze not only game footage but also player interview footage and practice footage to track fluctuations in player performance. For example, in addition to game footage, it can analyze player interview footage to track fluctuations in performance. This makes it possible to track fluctuations in player performance by analyzing player interview footage and practice footage.

[0050] The play extraction unit can analyze biometric data such as a player's heart rate and body temperature to identify the moment when tension or excitement reaches its peak. For example, it can analyze a player's heart rate data to identify the moment when tension or excitement reaches its peak. This allows for more dramatic highlights to be provided by analyzing a player's biometric data and identifying the moment when tension or excitement reaches its peak.

[0051] The play extraction unit takes into account the score fluctuations and time of day in a match and can prioritize the extraction of important plays that will change the flow of the match. For example, it analyzes the score fluctuations in a match and prioritizes the extraction of important plays that will change the flow of the match. This allows for the provision of more interesting highlights to viewers by prioritizing the extraction of important plays that will change the flow of the match, taking into account the score fluctuations and time of day in a match.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The video analysis unit analyzes the game footage. For example, the video analysis unit analyzes game footage of soccer, baseball, basketball, tennis, etc., to understand the game context, such as the flow of the game, the score, player movements, and audience reactions. Step 2: The play extraction unit extracts super plays from the game video analyzed by the video analysis unit. For example, the play extraction unit extracts super plays such as soccer goal scenes, baseball home runs, and basketball slam dunks. Step 3: The video generation unit generates a digest video based on the super plays extracted by the play extraction unit. For example, the video generation unit effectively edits the extracted super plays to generate a visually appealing digest video.

[0054] (Example 2) The automatic correction system according to an embodiment of the present invention uses a generation AI to automatically extract super plays that are highlights from sports games around the world and generate a digest video of them. This allows sports fans to efficiently watch only the highlights of each game from among countless games.

[0055] An automatic correction system according to an embodiment includes a video analysis unit, a play extraction unit, and a video generation unit. The video analysis unit analyzes game video. For example, the video analysis unit analyzes game video of soccer, baseball, basketball, tennis, or the like to understand game context, such as the flow of the game, the score, player movements, and audience reactions. The play extraction unit extracts impressive plays from the game video analyzed by the video analysis unit. For example, the play extraction unit extracts impressive plays, such as soccer goal scenes, baseball home runs, and basketball slam dunks. The video generation unit generates a digest video based on the impressive plays extracted by the play extraction unit. For example, the video generation unit effectively edits the extracted impressive plays to generate a visually appealing digest video. As a result, the automatic correction system according to an embodiment can automatically extract highlights from sports games and generate a digest video, thereby improving the viewing experience of sports fans.

[0056] The video analysis unit can estimate emotions from players' facial expressions and body movements, and identify moments when emotions are heightened. For example, the video analysis unit uses generative AI to analyze players' facial expressions and body movements to estimate moments when emotions are heightened. For example, it identifies moments when emotions are clearly expressed, such as joy immediately after scoring a goal or frustration immediately after conceding a goal. This allows the system to identify when players' emotions are heightened, providing more emotional highlights.

[0057] The video analysis unit can analyze the reactions of the spectators during a game and narrow down candidates for great plays based on the volume and frequency of cheers and boos. For example, the video analysis unit can analyze the volume of cheers and boos from the spectators during a game to narrow down candidates for great plays. For example, it can identify the moment when the cheers are at their loudest and extract that scene as a great play. In this way, by narrowing down the great plays based on the reactions of the spectators, it is possible to provide more visually appealing highlights.

[0058] The video analysis unit can analyze the live audio of a game and identify important plays based on the commentator's level of excitement and changes in tone. For example, the video analysis unit can analyze the live audio of a game and identify important plays based on the commentator's level of excitement and changes in tone. For example, the video analysis unit can extract the moment when the commentator's voice rises as a super play. In this way, by identifying important plays based on the commentator's level of excitement and changes in tone, it is possible to provide more interesting highlights for viewers.

[0059] The video analysis unit can simultaneously analyze game footage from different sports and find common patterns of super plays. For example, the video analysis unit can simultaneously analyze game footage from soccer and basketball games and find common patterns of super plays. For example, it can compare the moments of goal scenes and slam dunks. This allows the unit to analyze game footage from different sports and find common patterns of super plays, thereby providing a wider variety of highlights.

[0060] The video analysis unit can analyze interview videos and practice videos of players in addition to game videos, and track fluctuations in player performance. The video analysis unit, for example, analyzes interview videos of players in addition to game videos, and tracks fluctuations in performance. For example, the psychological state of players is analyzed based on the content of interviews before and after a game. In this way, by analyzing interview videos and practice videos of players, fluctuations in player performance can be tracked.

[0061] The video analysis unit uses the emotion estimation function to analyze changes in the emotions of players and spectators before and after a match, and can emphasize the highlights of the match more emotionally. For example, the video analysis unit uses the emotion estimation function to analyze changes in the emotions of players before and after a match and emphasize the highlights. For example, the tension before the match is compared with the joy after the match. In this way, by analyzing changes in the emotions of players and spectators before and after a match and emphasizing the highlights more emotionally, it is possible to provide more moving highlights for viewers.

[0062] The play extraction unit can analyze biometric data such as a player's heart rate and body temperature to identify the moment when tension or excitement reaches its peak. For example, the play extraction unit can use generative AI to analyze a player's heart rate data to identify the moment when tension or excitement reaches its peak. For example, the moment when heart rate rises sharply can be extracted as a super play. In this way, by analyzing a player's biometric data and identifying the moment when tension or excitement reaches its peak, more dramatic highlights can be provided.

[0063] The play extraction unit can prioritize extracting important plays that will change the flow of the game, taking into account the score fluctuations and time of day. The play extraction unit, for example, analyzes the score fluctuations of the game and prioritizes extracting important plays that will change the flow of the game. For example, scenes such as tying goals and game-winning home runs are extracted as super plays. In this way, by prioritizing the extraction of important plays that will change the flow of the game, taking into account the score fluctuations and time of day, it is possible to provide viewers with more interesting highlights.

[0064] The play extraction unit can analyze spectators' social media posts in real time and identify plays that are currently trending. For example, the play extraction unit can analyze spectators' social media posts in real time and identify plays that are currently trending. For example, the moment when the number of posts about a particular play suddenly increases can be extracted as a super play. In this way, by analyzing spectators' social media posts in real time and identifying plays that are currently trending, it is possible to provide viewers with more interesting highlights.

[0065] The play extraction unit can compare super plays from different sports and extract common techniques and tactics. For example, the play extraction unit compares super plays from soccer and basketball and extracts common techniques and tactics. For example, it compares dribbling breakthroughs and feint techniques. In this way, by comparing super plays from different sports and extracting common techniques and tactics, a wider variety of highlights can be provided to viewers.

[0066] When extracting great plays, the play extraction unit can refer to the players' past performance data and highlight plays that are good at specific players. The play extraction unit, for example, refers to the players' past performance data and highlights plays that are good at specific players. For example, it extracts shots and passes that are good at specific players as great plays. In this way, by referring to the players' past performance data and highlighting plays that are good at specific players, it is possible to provide more interesting highlights for viewers.

[0067] The play extraction unit can use the emotion estimation function to identify the moment when the audience's emotions are at their highest and extract that moment as a super play. The play extraction unit, for example, uses the emotion estimation function to identify the moment when the audience's emotions are at their highest. For example, it extracts the moment when the cheers are at their loudest as a super play. In this way, by identifying the moment when the audience's emotions are at their highest and extracting that moment as a super play, it is possible to provide viewers with a more moving highlight.

[0068] The video generation unit can generate a digest video with a storyline that includes the scenes before and after the super play. The video generation unit, for example, uses a generation AI to generate a digest video with a storyline that includes the scenes before and after the super play. For example, it can include passing and defensive scenes before and after a goal. In this way, by generating a digest video with a storyline that includes the scenes before and after the super play, it is possible to provide more attractive highlights to viewers.

[0069] The video generation unit automatically adds comments from players and coaches to the digest video, providing viewers with a deeper understanding. The video generation unit, for example, uses generation AI to automatically add comments from players and coaches to the digest video. For example, it inserts a player's comment after a goal is scored. In this way, by automatically adding comments from players and coaches to the digest video, viewers can be provided with a deeper understanding.

[0070] The video generation unit can analyze the emotional reactions of the audience and automatically adjust the music and sound effects to match the heightened emotions. For example, the video generation unit analyzes the emotional reactions of the audience and automatically adjusts the music and sound effects to match the heightened emotions. For example, the music can be increased at the moment when cheers begin to rise. In this way, by analyzing the emotional reactions of the audience and automatically adjusting the music and sound effects to match the heightened emotions, it is possible to provide more moving highlights for the viewer.

[0071] The video generation unit can generate a cross-sports digest video that combines super plays from different sports. For example, the video generation unit generates a cross-sports digest video that combines super plays from soccer and basketball. For example, goal scenes and slam dunks are edited alternately. In this way, a cross-sports digest video that combines super plays from different sports can be generated, providing viewers with a wider variety of highlights.

[0072] The video generation unit can add game statistics data and player profile information to the digest video to provide visually rich content. The video generation unit, for example, adds game statistics data to the digest video to provide visually rich content. For example, the number of goals scored and the number of assists are displayed. In this way, by adding game statistics data and player profile information to the digest video, it is possible to provide richer content for viewers.

[0073] The video generation unit uses the emotion estimation function to generate a customized digest video that matches the viewer's emotions, thereby optimizing the individual viewing experience. The video generation unit, for example, uses the emotion estimation function to generate a customized digest video that matches the viewer's emotions. For example, highlight scenes are selected according to the viewer's level of excitement. In this way, the individual viewing experience can be optimized by generating a customized digest video that matches the viewer's emotions.

[0074] The video analysis unit can analyze game footage to understand game context such as the flow of the game, the score, player movements, and audience reactions. For example, the video analysis unit uses generative AI to analyze game footage to understand game context such as the flow of the game, the score, player movements, and audience reactions. For example, it analyzes important scenes and player movements in the game to understand the flow of the game. This allows the unit to provide more accurate highlights by analyzing game footage and understanding game context such as the flow of the game, the score, player movements, and audience reactions.

[0075] The play extraction unit can extract the moment of a specified super play from the game video. The play extraction unit extracts, for example, the moment of a specified super play from the game video. For example, it extracts the moment of a goal or a home run. By extracting the moment of a specified super play from the game video, it is possible to provide more attractive highlights to viewers.

[0076] The video generation unit can generate a digest video based on the extracted moments of the super plays. The video generation unit generates a digest video based on the extracted moments of the super plays, for example. For example, the digest video is created by editing the moments of goal scenes and slam dunks. In this way, by generating a digest video based on the extracted moments of the super plays, it is possible to provide more attractive highlights to viewers.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The video analysis unit can analyze not only the movements of players during a match, but also footage of players warming up and cooling down before and after a match. For example, by analyzing a player's movements during pre-match warm-ups and their facial expressions during post-match cool-downs, it is possible to understand the player's condition and psychological state. This makes it possible to provide more comprehensive highlights that include the player's condition before and after a match.

[0079] The video analysis unit can infer emotions from players' facial expressions and body movements, and identify moments when emotions are heightened. For example, it can identify moments when emotions are clearly expressed, such as joy immediately after scoring a goal or frustration immediately after conceding a goal. This allows the system to identify when players' emotions are heightened, thereby providing more emotional highlights.

[0080] The video analysis unit analyzes the reactions of the spectators during the game and can narrow down the candidates for great plays based on the volume and frequency of cheers and boos. For example, it can identify the moment when the cheers are at their loudest and extract that scene as a great play. This allows the system to provide more visually appealing highlights by narrowing down the great plays based on the spectators' reactions.

[0081] The video analysis unit analyzes the live audio of the game and can identify important plays based on the commentator's excitement level and changes in tone. For example, it can extract the moment when the commentator's voice rises as a super play. This allows it to identify important plays based on the commentator's excitement level and changes in tone, providing viewers with more interesting highlights.

[0082] The video analysis unit can simultaneously analyze footage of different sports matches and find common patterns of great plays. For example, it can simultaneously analyze footage of soccer and basketball matches and find common patterns of great plays. This allows it to analyze footage of different sports matches and find common patterns of great plays, thereby providing a wider variety of highlights.

[0083] The video analysis unit can analyze not only game footage but also player interview footage and practice footage to track fluctuations in player performance. For example, in addition to game footage, it can analyze player interview footage to track fluctuations in performance. This makes it possible to track fluctuations in player performance by analyzing player interview footage and practice footage.

[0084] The video analysis unit uses its emotion estimation function to analyze changes in the emotions of players and spectators before and after a match, enabling it to emphasize the highlights of the match more emotionally. For example, it compares the tension before the match with the joy after the match. This allows it to analyze changes in the emotions of players and spectators before and after a match and emphasize the highlights more emotionally, providing viewers with more moving highlights.

[0085] The play extraction unit can analyze biometric data such as a player's heart rate and body temperature to identify the moment when tension or excitement reaches its peak. For example, it can analyze a player's heart rate data to identify the moment when tension or excitement reaches its peak. This allows for more dramatic highlights to be provided by analyzing a player's biometric data and identifying the moment when tension or excitement reaches its peak.

[0086] The play extraction unit takes into account the score fluctuations and time of day in a match and can prioritize the extraction of important plays that will change the flow of the match. For example, it analyzes the score fluctuations in a match and prioritizes the extraction of important plays that will change the flow of the match. This allows for the provision of more interesting highlights to viewers by prioritizing the extraction of important plays that will change the flow of the match, taking into account the score fluctuations and time of day in a match.

[0087] The play extraction unit can analyze spectators' social media posts in real time and identify plays that are currently trending. For example, it can analyze spectators' social media posts in real time and identify plays that are currently trending. This allows for the provision of more interesting highlights to viewers by analyzing spectators' social media posts in real time and identifying plays that are currently trending.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The video analysis unit analyzes the game footage. For example, the video analysis unit analyzes game footage of soccer, baseball, basketball, tennis, etc., to understand the game context, such as the flow of the game, the score, player movements, and audience reactions. Step 2: The play extraction unit extracts super plays from the game video analyzed by the video analysis unit. For example, the play extraction unit extracts super plays such as soccer goal scenes, baseball home runs, and basketball slam dunks. Step 3: The video generation unit generates a digest video based on the super plays extracted by the play extraction unit. For example, the video generation unit effectively edits the extracted super plays to generate a visually appealing digest video.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0131] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0141] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0142] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0146] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0147] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0148] 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.

[0149] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0150] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0151] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0152] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0153] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0154] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A video analysis department that analyzes game footage, a play extraction unit that extracts great plays from the game video analyzed by the video analysis unit; a video generation unit that generates a digest video based on the super plays extracted by the play extraction unit. A system characterized by:

2. The video analysis unit Inferring emotions from players' facial expressions and body movements to identify moments of heightened emotion 2. The system of claim 1.

3. The video analysis unit Simultaneously analyze the game footage of different sports to find common patterns of the super plays.

2. The system of claim 1.

4. The play extraction unit Analyzing athletes' biometric data, such as heart rate and body temperature, to identify the moment when tension or excitement reaches its peak 2. The system of claim 1.

5. The video generation unit A digest video with a storyline including scenes before and after the super play is generated.

2. The system of claim 1.

6. The video analysis unit Analyzing changes in the emotions of players and spectators before and after a match to enhance the emotional impact of match highlights 2. The system of claim 1.

7. The play extraction unit Identify the moment when the audience's emotions are at their highest and extract that moment as the super play.

2. The system of claim 1.

8. The video generation unit Generates personalized digest videos tailored to the viewer's emotions, optimizing the viewing experience 2. The system of claim 1.

Images