system

Abstract

We provide the system. [Solution] A terminal for users to watch video footage of the match and select scenes that interest them, A processing method for creating video highlights based on selected scenes, A means of transmitting the created video highlights to an information sharing medium, An interface means for other users to react to publicly shared video highlights, A management system that generates user rankings based on the popularity of publicly released video highlights, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 modern sports viewing, users have a need to immediately share a scene that was particularly impressive during the game. However, it takes time to watch the highlights after the game, and there is a problem that the viewpoints are limited in the videos provided by a centralized method. In addition, there is a problem that the trial experience tends to be monotonous because there is a lack of functions for enhancing engagement among users.

Means for Solving the Problems

[0005] This invention provides a terminal that allows users to freely select scenes of interest during a match and easily create and share video highlights based on those scenes. Furthermore, it enhances user engagement by providing an interface that allows users to share the created highlights with other users and receive real-time feedback. In addition, it introduces gamification elements through a ranking function based on the popularity of the published highlights, making the match experience more engaging.

[0006] A "user" is an individual or group that operates this system to view video footage of a match and select scenes.

[0007] A "terminal" is an electronic device used by users to perform operations such as scene selection and highlight creation.

[0008] A "video highlight" is a compilation of selected match scenes edited together into a single video clip.

[0009] An "information sharing medium" is an internet platform where users can publish video highlights they have created, and other users can access them.

[0010] "Transmission means" refers to a device or software that has the function of transmitting the created video highlights to an information sharing medium.

[0011] "Interface means" refers to an operation screen or input device that allows users to react to publicly released video highlights.

[0012] "Management means" refers to a device or software that has the function of collecting and analyzing engagement data of publicly released video highlights and generating rankings.

[0013] A "ranking" is a list of rankings that indicates popularity among users, based on the evaluation of publicly available video highlights.

[0014] "Mechanical means" refers to artificial intelligence and speech generation technologies used to automatically generate opinions from players and related personnel. [Brief explanation of the drawing]

[0015] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), etc.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] The 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.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] In one embodiment of the present invention, users can view real-time video footage of a match using a dedicated terminal application. This application has the function of receiving match data distributed from a server and displaying it on the user's terminal.

[0037] Users can select scenes that interest them during a match on their device. This selection is made through a visual interface by tapping specific buttons, and the timestamp and related data for the selected scene are recorded on the device. This allows users to personalize their viewing experience.

[0038] The selected scenes are combined on the device and edited into a highlight video. The edited highlights can be customized by adding text overlays and music. The completed highlight video is sent from the device to the information sharing medium.

[0039] The server publishes the submitted highlight videos on an information sharing platform tailored to each user. The published videos can receive reactions from other users through an interface. The server collects this data and builds a ranking based on the degree of interaction. This ranking is generated by an internal management system and displayed to users as a ranking.

[0040] Furthermore, after the match, virtual interviews created using automated methods are delivered from the server to the user's device. This provides viewers with the opinions of players and staff, further deepening their understanding of the match.

[0041] Thus, the present invention aims to make the sports viewing experience more interactive and engaging by enabling users to actively participate during and after matches.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server collects video and audio data through multiple cameras and sensors during the match. The collected data is analyzed in real time and tagged to specific events (goals, fouls, etc.). The server then delivers the analyzed data to terminals in a streaming format.

[0045] Step 2:

[0046] The device displays a live stream of the match received from the server to the user. This includes video, audio, and tagged event information. While watching the video, the user can select any scene and mark it by pressing a specific button.

[0047] Step 3:

[0048] When a user selects a scene, the device records the scene's timestamp and associated metadata. This recorded data is later used in the editing process. This allows the user to collect material for custom highlight clips.

[0049] Step 4:

[0050] The device provides an interface for editing highlight videos by combining scenes selected by the user. Users can rearrange multiple selected scenes by dragging and dropping, and add text and music.

[0051] Step 5:

[0052] Once the user has finished editing the highlight video, the device renders the video in the specified format and saves it as a single file. This file is then ready to be uploaded to a sharing platform later.

[0053] Step 6:

[0054] The device uploads the completed highlight video to the information sharing platform via the server. The uploaded video is made public to other users on the platform, allowing them to collect reactions such as comments and "likes."

[0055] Step 7:

[0056] The server collects data on other users' reactions to the published highlight videos. Based on this data, the server calculates user engagement and updates the rankings. The ranking results are fed back to the user's device, allowing the user to check their own ranking.

[0057] Step 8:

[0058] After the match, the server provides a virtual interview generated using automated means. This includes player comments and analysis about the match, and is delivered to the user's device, serving as additional content to deepen their understanding of the game.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Traditional video viewing systems make it difficult for users to easily select scenes that interest them, create individually edited highlights, and share them with others. Furthermore, the lack of mechanisms to appropriately provide user evaluations and rewards based on post-publication feedback results in a one-way viewing experience, limiting opportunities for deeper participant engagement.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for decoding video using an information processing device, for a user to view video of a match and select scenes of interest, for recording a timestamp and related information as metadata for the selected scenes, and for integrating video fragments based on the user's selection and creating edited video by adding text and audio. This makes it possible for users to easily create individually interactive edited highlights and share and interact with other users.

[0064] An "information processing device" is a device that enables the reception, processing, and presentation of electronic data, and is used to perform advanced calculations such as decoding and streaming.

[0065] A "timestamp" is time information used to record when a particular event occurred, and is used to organize and track data.

[0066] Metadata is additional information that accompanies data, describing its attributes and structure, and is used to facilitate its use and management.

[0067] A "video fragment" is a selected portion or fragment from the entire video, which is the material to be edited or combined.

[0068] A "dialogue device" is a means for a user to communicate with a system and provide feedback or responses, and includes interfaces and input devices.

[0069] A "control mechanism" is a mechanism for monitoring and managing various operations of a system and for issuing instructions or adjustments as needed.

[0070] An "incentive" refers to a reward or stimulus provided to encourage behavior and motivate the achievement of a specific goal.

[0071] A "Q&A video" is a video created based on conversations with relevant parties, used to provide information to viewers and facilitate understanding.

[0072] This invention is a system that allows users to select scenes they find interesting during a match, generate edited videos based on those scenes, and share them with other users. The operation of a specific system that is an embodiment of this invention will be described below.

[0073] Users watch match footage in real time using a dedicated terminal. The terminal receives streaming data from the server and plays the video smoothly using its built-in decoding function. The terminal application provides buttons for visually selecting scenes through the user interface. When a user is interested in a particular scene, tapping the corresponding button records the timestamp and associated metadata of the selected scene. This metadata is used as basic information for scene identification and editing processes.

[0074] The device integrates multiple scenes selected by the user and generates an edited video using automated video editing software. This software allows the user to customize the video by adding text overlays and audio according to their specifications. The finished video is previewed on the device, prompting the user to review the edits.

[0075] Next, the terminal converts the generated video into an appropriate format and uploads it to an information sharing medium. The destination for publication is determined based on the user's selection. The server collects feedback from other users on this published video and aggregates reaction data. Based on the data thus obtained, a user ranking is generated by the control system, and the results are presented to the user.

[0076] As a concrete example, during a soccer match, users can select and edit "goal scenes" to create a 3-minute highlight video with appropriate music. The completed video can be shared on social media and receive "likes" and comments from other users. This enables interactive communication among users.

[0077] An example of a prompt for a generative AI model is, "I want to create a soccer match highlight video. Please generate 5 minutes of content, including goal scenes and player interviews." Through this, the AI ​​can create customized videos that meet the user's requests.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server collects real-time video data of the match and encodes it into a streaming format. The input is raw data from cameras and sensors during the match, and the output is compressed streaming data in a format that can be transmitted over the network. During this process, data compression and encoding are performed, preparing the data for transmission to user terminals.

[0081] Step 2:

[0082] The terminal decodes the streaming data received from the server and displays it to the user as video. The input is streaming data from the server, and the output is real-time video of the match that the user can view. The player software on the terminal is responsible for the decoding process and for smoothly playing the video on the screen.

[0083] Step 3:

[0084] Users watch the match on their device and select scenes of interest from the interface. The input is the user's visual judgment, and the output is the timestamp of the selected scene and associated metadata. Selection is made by tapping a button on the device, and this action records the selection data.

[0085] Step 4:

[0086] The terminal extracts and edits selected video segments based on recorded timestamps. The input is user-selected scene information, and the output is the edited video clip. Editing software runs in the background, integrating the video clips and adding text overlays and background music.

[0087] Step 5:

[0088] The user previews the edited video clip on their device to make a final check of the completed video. The input is the edited video clip, and the output is the video reviewed by the user. The user can add any desired changes during the preview.

[0089] Step 6:

[0090] The terminal converts the verified video to the appropriate format and uploads it to the information sharing medium. The input is the video data sent to the cloud after confirmation, and the output is the uploaded edited video. File format conversion and compression are performed as needed.

[0091] Step 7:

[0092] The server collects video viewing data and user reactions from information sharing media and aggregates the data for ranking. The input is interaction data obtained from information sharing media, and the output is a ranking score. A generative AI model processes this data to determine the rankings.

[0093] Step 8:

[0094] Users receive ranking information streamed from the server on their devices and check the evaluation of their videos. The input is the generated ranking data, and the output is the user's viewable ranking information. Incentives and evaluation information are presented according to the ranking.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] In sports viewing, there is a need for a method that allows users to easily select scenes that interest them, personalize their viewing experience, and share it. There is also a need for a system that allows users to easily view post-game comments from players and staff, enriching the viewing experience. Furthermore, a system is needed that allows users to share their selected scenes with other users, thereby improving engagement.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes an information processing device for users to view viewable information during a competition and select scenes of interest, an information processing means for creating a summary of viewable information based on the selected scenes, and a communication device for transmitting the created summary of viewable information to a sharing means. This allows users to easily generate and share highlight videos that reflect their interests, and after the match, they can view conversations generated by an AI model, enabling a deeper viewing experience.

[0100] A "user" is an individual or group that uses this system to experience watching a game.

[0101] "Viewable information" refers to media data that allows for visual or auditory observation of the progress of events such as matches.

[0102] An "information processing device" is a device that processes information selected by the user and converts the data into an appropriate format.

[0103] "Information processing means" refers to software or hardware used to analyze, transform, and process data according to a specific purpose.

[0104] A "communication device" is hardware or a part thereof that has network connectivity capabilities for sending and receiving data.

[0105] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate new data or content.

[0106] "Conversation content" refers to information provided by the generated AI, which is equivalent to the opinions and comments of players and related personnel.

[0107] "Sharing methods" refer to methods or channels for exchanging data among users and making it accessible to other users.

[0108] "Viewing experience" refers to the act of users watching sports events or similar events through video and audio.

[0109] The system that implements this application consists of a user terminal, a server, and a communication network. Users view available information, such as sports matches, in real time using a smartphone or similar information processing device. When a user selects a scene that interests them during viewing, the timestamp of that scene and related data are recorded on the terminal. This allows users to personalize their viewing experience.

[0110] The server has information processing means for generating a summary of viewable information for the user based on selected scenes. This summary is then edited into a highlight video by merging relevant information. Video editing and cutting are performed efficiently using video processing libraries such as FFmpeg. Users can add customizations such as music and text to the generated highlights. The completed highlights are uploaded to the server via a communication device and made available to other users through an information sharing medium.

[0111] Furthermore, the server uses a generative AI model to generate conversation content after the match and deliver it to the user's terminal. This allows users to view the opinions of players and staff after the match, gaining a deeper understanding. As a concrete example, a prompt that simulates a post-match interview with a player could be considered. For example, a prompt such as, "Generate the content of the post-match interview. Player name: Tanaka, Match content: A goal that decided the victory. Please give your thoughts on this goal in a humorous and moving way." In this way, the sports viewing experience becomes highly interactive and personalized.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] Users receive viewing information using their devices and view it in real time. Streaming data of the viewing information is provided as input. The device displays this stream and provides an interface that allows the user to select scenes.

[0115] Step 2:

[0116] When a user selects a scene of interest, the device records the timestamp and associated metadata for that scene. The input here is the user's selection action, and the output—the timestamp and metadata—is saved to a local database. Operationally, the device monitors the event of a selection button press and captures the necessary data.

[0117] Step 3:

[0118] The terminal sends recorded timestamps and metadata to the server. The input is stored data, and the output is communication to the server. Operationally, the terminal uploads data to the server using a secure communication protocol (e.g., HTTPS).

[0119] Step 4:

[0120] The server uses the received data to generate a summary of viewable information using a video processing library such as FFmpeg. The input consists of timestamps, metadata, and the original video data, while the output is the generated highlight video. Operationally, the server extracts a specified range of video and edits it if necessary.

[0121] Step 5:

[0122] The user receives the highlight video generated on their device and customizes the music and text as needed. The input is the generated highlight video, and the output is the customized video. The interface allows the user to easily edit the video.

[0123] Step 6:

[0124] The customized highlights are sent back to the server and made public to other users via an information sharing medium. The input is the customized video, and the output is the publicly available state to other users. In operation, the server uploads the data to the media platform and performs access control.

[0125] Step 7:

[0126] After the match, the server uses a generative AI model to generate conversation content based on the prompt text and delivers it to the user. The input is the prompt text and the AI ​​model, and the output is the generated conversation content. In operation, the server activates the AI ​​model, processes the prompt, and generates the content.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] In implementing this invention, users can view match footage in real time using a dedicated terminal application. The terminal receives streaming data of the match delivered from a server and presents it to the user. While viewing the video, the user uses an interface to select impressive scenes. The data of the selected scenes is stored on the terminal and used to create subsequent highlight videos.

[0129] Furthermore, the device incorporates an emotion engine that recognizes the user's emotions in real time. It analyzes the user's facial expressions and voice tone to determine their emotional state while watching. This data is used to further personalize the viewing experience. For example, it can extract scenes that particularly excite the user and create custom highlights featuring those scenes.

[0130] After creating a highlight video, the device uploads the video to an information sharing platform. This process includes personalized elements created based on sentiment data. The published video can receive reactions from other users, and these reactions are collected on the server.

[0131] The server generates user rankings based on collected reaction data. The rankings are updated in real time, and a system is in place to reward users who rank highly. Furthermore, the aggregated sentiment data is used to analyze the overall emotional trends of the audience, generating reports that can be used for post-match reviews and marketing strategies.

[0132] Through this process, the present invention aims to provide a personalized sports viewing experience that reflects the user's emotions, promote interaction among users, and improve overall engagement.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The server collects live video and audio data from the match, analyzes and tags it in real time, and delivers this information as a stream to the user's device.

[0136] Step 2:

[0137] The device displays video received from the server to the user. Simultaneously, it activates an emotion engine, using the camera and microphone to recognize emotions from the user's facial expressions and voice. The recognized emotion data is recorded along with the video playback.

[0138] Step 3:

[0139] The user selects scenes of interest using the device's interface. The selected scenes are associated with recorded emotional data, along with a timestamp.

[0140] Step 4:

[0141] The device edits a personalized highlight video based on scenes and emotion data selected by the user. The user can rearrange scenes and add commentary and music using the editing interface.

[0142] Step 5:

[0143] The edited highlight video is uploaded to the information sharing platform via the server from the device. The uploaded video is tagged with sentiment data, making it viewable and reactable to by other users.

[0144] Step 6:

[0145] The server collects reactions from other users to videos on the information sharing platform and generates rankings. The rankings take into account emotional engagement, and notifications are sent to top-ranking users.

[0146] Step 7:

[0147] After the match ends, the server analyzes the collected sentiment data and generates a report showing the overall sentiment trends of the viewers. This report is used as insight for future strategies, such as marketing strategies.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] In sports viewing, there are limitations to obtaining a video experience that reflects the viewer's own emotional state, and there is a lack of means to enhance interaction and engagement with other viewers. In particular, it is difficult to provide video that matches the interests and emotions of each individual viewer, and there is a need to deepen the live experience.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes a device for users to view image data during a match and select scenes of interest, processing means for creating digital images based on the data of the selected scenes, and transmission means for publishing the created digital images to an information sharing platform. This enables a personalized sports viewing experience that reflects the emotional state of each individual user.

[0153] "Users" refers to individuals or groups who operate the system to watch sports or view digital videos.

[0154] "Image data" refers to visual information received from a server or terminal during a match and presented to the user.

[0155] A "scene" refers to a portion of the video footage that a user selects during a match to identify or pinpoint something that interests them.

[0156] "Device" refers to an electronic device used by users to view image data and select scenes that interest them.

[0157] "Digital images" refer to visual information that is processed and generated using engineering methods based on selected scenes.

[0158] An "information sharing platform" refers to an online platform where multiple users can publish digital videos and interact with each other.

[0159] "Emotional state" refers to information obtained by analyzing the mental state that expresses the user's mood and emotions.

[0160] "Management means" refers to a function that generates rankings based on the popularity of publicly available digital videos and user reactions.

[0161] This invention is a system that allows users to watch a match, extract scenes of interest, generate highlight videos, and share them with other users. Specifically, it is implemented through interaction between a server, a terminal, and the user.

[0162] Users first use a dedicated terminal to receive match image data from the server in streaming format. Commonly used streaming technologies include HLS (HTTP Live Streaming) and DASH (Dynamic Adaptive Streaming over HTTP). The terminal decodes this data and displays the match video on its screen.

[0163] During viewing, users can select scenes of interest using the interface. The interface includes a timestamp function and saves the selected scenes as data on the device. Based on the data of the selected scenes, the device generates highlights as digital video. Video editing is performed using the FFmpeg library.

[0164] Furthermore, the device is equipped with emotion recognition capabilities, using OpenCV to analyze the user's facial expressions in real time and Librosa to analyze their voice tone. This allows the device to determine the user's emotional state and add personalized elements to the digital video. Personalization based on emotional state further enriches the viewing experience.

[0165] The device uploads the completed digital video to information sharing platforms such as YouTube® and Vimeo using APIs. At this time, a description generated based on the viewer's emotional state is automatically added. The published video gathers reactions from other users, and the server generates a popularity ranking based on these reactions.

[0166] This allows the server to improve user engagement and deepen individual viewing experiences. As part of this process, an example of a prompt for the generative AI model would be: "Create a highlight video by extracting particularly exciting moments from the user's emotional data." In this way, it becomes possible to generate and share digital images that reflect the user's emotions.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user activates a dedicated terminal and begins streaming the match. The terminal receives match image data in real time from the server using the HLS or DASH protocol. The input is the streaming data from the server, and the output is the visual information displayed on the terminal's screen. Specifically, the terminal decodes the received data and buffers it for smooth playback.

[0170] Step 2:

[0171] During viewing, the user selects scenes of interest via the interface. The device saves this selection information, including timestamps. The input is the timestamps of the scenes selected by the user, and the output is a list of scenes stored in an internal database. Specifically, marking the selected points creates data for later scene editing.

[0172] Step 3:

[0173] The device uses its built-in camera and microphone to recognize the user's emotional state. It analyzes facial expressions using OpenCV and voice tone using Librosa. The input is real-time audio and video data of the user, and the output is information on the analyzed emotional state. Specifically, it extracts facial feature points to determine emotions in real time and measures the intensity of emotions by analyzing the pitch and intensity of the voice.

[0174] Step 4:

[0175] The device creates digital highlights using a saved list of scenes and emotional information. It edits selected scenes using the FFmpeg library, incorporating personalized content based on emotional data. The input is a list of selected scenes and the user's emotional information, and the output is a visually organized digital video. Specifically, it crops footage to highlight peak emotional scenes and rearranges it according to a template.

[0176] Step 5:

[0177] The terminal uploads the completed digital video to an information sharing platform via the internet. Using an API, it automatically generates titles and descriptions and configures publication settings. The input is the completed digital video and generated metadata, and the output is the video content published online. Specifically, it establishes a connection to a video sharing service and uploads all necessary files.

[0178] Step 6:

[0179] The server collects reactions from other users to uploaded videos. It then analyzes this data to generate a popularity ranking. The input is reaction data from other users, and the output is a popularity ranking that updates in real time. Specifically, it aggregates viewing time and evaluation comments, and calculates the overall system ranking based on evaluation points.

[0180] Step 7:

[0181] The server comprehensively analyzes the collected sentiment data and reactions to create a report on viewing trends. This information is used for developing marketing strategies, among other things. The input is the sentiment data and reaction statistics of each user, and the output is a report as a result of the analysis. Specifically, the process involves visualizing trends using data mining techniques and generating a report as the output.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] In modern digital entertainment, personalizing content for users is crucial, but many challenges remain, particularly in individualizing the user experience in live video. Traditional methods require users to manually select video highlights, and there is a lack of effective means to leverage emotional data to improve user engagement. Furthermore, systems that generate rankings based on the popularity of video highlights to promote competition among users are also lacking.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes data processing means for automatically detecting scenes that excited the user while they were watching video content, data processing means for generating personalized video highlights based on the detected emotion data, and transmission means for transmitting the generated video highlights to an information transmission medium. This enables real-time video highlight generation utilizing the user's emotion data and the creation of rankings to enhance user engagement.

[0187] A "user" is a viewer who shows excitement or interest after watching video content.

[0188] "Video content" refers to information in video format that is delivered to users through their sight and hearing.

[0189] An "exciting scene" refers to a portion of the video in which the user's emotional data specifically detects a state of excitement.

[0190] "Data processing means" refers to a device or program that has the function of analyzing acquired emotional information and generating target data based on a specific algorithm.

[0191] "Personalized video highlights" are video digests edited to match each user's individual interests and preferences based on their emotional data.

[0192] An "information transmission medium" refers to an online platform or communication method used to show generated video highlights to other users.

[0193] "Transfer means" refers to the technology or device used to transmit generated data to other terminals or platforms.

[0194] A "ranking" is information that shows evaluations and rankings generated based on user activity and responses.

[0195] The system for implementing this invention consists of a server that provides video content, a terminal that detects the user's emotions, and various software that handles the generated data.

[0196] The server delivers video data to the user's device in real time using a streaming API. The user views the video using a smartphone or other digital device. At this time, the device uses its built-in camera and microphone, along with emotion analysis libraries such as OpenCV and TENSORFLOW®, to analyze the user's facial expressions and voice tone and acquire real-time emotion data.

[0197] After collecting emotional data, the device automatically extracts scenes where the user is deemed excited, using video editing libraries such as FFMpeg. This generates personalized video highlights, which are then transferred to a communication medium.

[0198] The server aggregates and analyzes reactions, using a real-time database such as Firebase to calculate user ratings and rankings. The ranking results are continuously updated, and appropriate compensation is provided based on them. This entire system makes users' sports viewing experiences data-driven and personalized, improving overall engagement.

[0199] As a concrete example, during a soccer match, when your team scores, the device analyzes the user's facial expressions and voice to create a special highlight based on those emotions. This highlight can then be shared with friends. An example of a specific prompt message would be, "While watching a soccer match, would you like to have your facial expressions and voice emotions analyzed during scoring moments to create a special highlight that you can share with your friends?" In this way, the user experience becomes richer.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The server transmits match video data to the user's device in real time using a streaming API. The input is the match video data, and the output is the video played on the user's device. During this process, the server manages network bandwidth to ensure stable data transfer.

[0203] Step 2:

[0204] The device plays back received video data while operating its built-in camera and microphone, and analyzes the user's facial expressions and voice tone using OpenCV and TensorFlow. The input is the user's video and audio data, and the output is emotion parameters. The device processes this data in real time and generates emotion analysis results.

[0205] Step 3:

[0206] The device extracts scenes where an excited state is determined based on the emotion analysis results. By setting a specific threshold for extraction, it selects the corresponding video range when an excitement level above a certain level is detected. The input is emotion parameters, and the output is metadata of the selected scenes.

[0207] Step 4:

[0208] The device uses a video editing library such as FFMpeg to aggregate extracted scenes and generate a personalized highlight video. The input is metadata of the selected scenes, and the output is a highlight video file. The generated video is saved in the user's viewing history.

[0209] Step 5:

[0210] The terminal transfers the generated highlight video to a communication medium and shares it with other users via a user interface. The input is a highlight video file, and the output is publicly available on an online platform. Users can receive comments on this video.

[0211] Step 6:

[0212] The server collects reaction data from other users aggregated across information transmission media and generates ratings and rankings using a real-time database such as Firebase. The input is reaction data, and the output is updated ratings and rankings. The server updates the user's profile based on this.

[0213] Step 7:

[0214] The server provides rewards to users who rank highly based on the ranking results. The input is the updated ranking data, and the output is the rewards and benefits provided. The server automatically handles reward transfers and point awards.

[0215] 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.

[0216] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0222] 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.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0224] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] 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.

[0226] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0228] The 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.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0231] In one embodiment of the present invention, users can view real-time video footage of a match using a dedicated terminal application. This application has the function of receiving match data distributed from a server and displaying it on the user's terminal.

[0232] Users can select scenes that interest them during a match on their device. This selection is made through a visual interface by tapping specific buttons, and the timestamp and related data for the selected scene are recorded on the device. This allows users to personalize their viewing experience.

[0233] The selected scenes are combined on the device and edited into a highlight video. The edited highlights can be customized by adding text overlays and music. The completed highlight video is sent from the device to the information sharing medium.

[0234] The server publishes the submitted highlight videos on an information sharing platform tailored to each user. The published videos can receive reactions from other users through an interface. The server collects this data and builds a ranking based on the degree of interaction. This ranking is generated by an internal management system and displayed to users as a ranking.

[0235] Furthermore, after the match, virtual interviews created using automated methods are delivered from the server to the user's device. This provides viewers with the opinions of players and staff, further deepening their understanding of the match.

[0236] Thus, the present invention aims to make the sports viewing experience more interactive and engaging by enabling users to actively participate during and after matches.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The server collects video and audio data through multiple cameras and sensors during the match. The collected data is analyzed in real time and tagged to specific events (goals, fouls, etc.). The server then delivers the analyzed data to terminals in a streaming format.

[0240] Step 2:

[0241] The device displays a live stream of the match received from the server to the user. This includes video, audio, and tagged event information. While watching the video, the user can select any scene and mark it by pressing a specific button.

[0242] Step 3:

[0243] When a user selects a scene, the device records the scene's timestamp and associated metadata. This recorded data is later used in the editing process. This allows the user to collect material for custom highlight clips.

[0244] Step 4:

[0245] The device provides an interface for editing highlight videos by combining scenes selected by the user. Users can rearrange multiple selected scenes by dragging and dropping, and add text and music.

[0246] Step 5:

[0247] Once the user has finished editing the highlight video, the device renders the video in the specified format and saves it as a single file. This file is then ready to be uploaded to a sharing platform later.

[0248] Step 6:

[0249] The device uploads the completed highlight video to the information sharing platform via the server. The uploaded video is made public to other users on the platform, allowing them to collect reactions such as comments and "likes."

[0250] Step 7:

[0251] The server collects data on other users' reactions to the published highlight videos. Based on this data, the server calculates user engagement and updates the rankings. The ranking results are fed back to the user's device, allowing the user to check their own ranking.

[0252] Step 8:

[0253] After the match, the server provides a virtual interview generated using automated means. This includes player comments and analysis about the match, and is delivered to the user's device, serving as additional content to deepen their understanding of the game.

[0254] (Example 1)

[0255] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0256] Traditional video viewing systems make it difficult for users to easily select scenes that interest them, create individually edited highlights, and share them with others. Furthermore, the lack of mechanisms to appropriately provide user evaluations and rewards based on post-publication feedback results in a one-way viewing experience, limiting opportunities for deeper participant engagement.

[0257] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0258] In this invention, the server includes means for decoding video using an information processing device, for a user to view video of a match and select scenes of interest, for recording a timestamp and related information as metadata for the selected scenes, and for integrating video fragments based on the user's selection and creating edited video by adding text and audio. This makes it possible for users to easily create individually interactive edited highlights and share and interact with other users.

[0259] An "information processing device" is a device that enables the reception, processing, and presentation of electronic data, and is used to perform advanced calculations such as decoding and streaming.

[0260] A "timestamp" is time information used to record when a particular event occurred, and is used to organize and track data.

[0261] Metadata is additional information that accompanies data, describing its attributes and structure, and is used to facilitate its use and management.

[0262] A "video fragment" is a selected portion or fragment from the entire video, which is the material to be edited or combined.

[0263] A "dialogue device" is a means for a user to communicate with a system and provide feedback or responses, and includes interfaces and input devices.

[0264] A "control mechanism" is a mechanism for monitoring and managing various operations of a system and for issuing instructions or adjustments as needed.

[0265] An "incentive" refers to a reward or stimulus provided to encourage behavior and motivate the achievement of a specific goal.

[0266] A "Q&A video" is a video created based on conversations with relevant parties, used to provide information to viewers and facilitate understanding.

[0267] This invention is a system that allows users to select scenes they find interesting during a match, generate edited videos based on those scenes, and share them with other users. The operation of a specific system that is an embodiment of this invention will be described below.

[0268] Users watch match footage in real time using a dedicated terminal. The terminal receives streaming data from the server and plays the video smoothly using its built-in decoding function. The terminal application provides buttons for visually selecting scenes through the user interface. When a user is interested in a particular scene, tapping the corresponding button records the timestamp and associated metadata of the selected scene. This metadata is used as basic information for scene identification and editing processes.

[0269] The device integrates multiple scenes selected by the user and generates an edited video using automated video editing software. This software allows the user to customize the video by adding text overlays and audio according to their specifications. The finished video is previewed on the device, prompting the user to review the edits.

[0270] Next, the terminal converts the generated video into an appropriate format and uploads it to an information sharing medium. The destination for publication is determined based on the user's selection. The server collects feedback from other users on this published video and aggregates reaction data. Based on the data thus obtained, a user ranking is generated by the control system, and the results are presented to the user.

[0271] As a concrete example, during a soccer match, users can select and edit "goal scenes" to create a 3-minute highlight video with appropriate music. The completed video can be shared on social media and receive "likes" and comments from other users. This enables interactive communication among users.

[0272] An example of a prompt for a generative AI model is, "I want to create a soccer match highlight video. Please generate 5 minutes of content, including goal scenes and player interviews." Through this, the AI ​​can create customized videos that meet the user's requests.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The server collects real-time video data of the match and encodes it into a streaming format. The input is raw data from cameras and sensors during the match, and the output is compressed streaming data in a format that can be transmitted over the network. During this process, data compression and encoding are performed, preparing the data for transmission to user terminals.

[0276] Step 2:

[0277] The terminal decodes the streaming data received from the server and displays it to the user as video. The input is streaming data from the server, and the output is real-time video of the match that the user can view. The player software on the terminal is responsible for the decoding process and for smoothly playing the video on the screen.

[0278] Step 3:

[0279] Users watch the match on their device and select scenes of interest from the interface. The input is the user's visual judgment, and the output is the timestamp of the selected scene and associated metadata. Selection is made by tapping a button on the device, and this action records the selection data.

[0280] Step 4:

[0281] The terminal extracts and edits the selected video segment based on the recorded timestamp. The input is the scene information selected by the user, and the output is the edited video clip. The editing software operates in the background, integrates the video clips, and also performs operations such as adding text overlays and BGM.

[0282] Step 5:

[0283] The user previews the edited video clip on the terminal and performs a final check on the completed video. The input is the edited video clip, and the output is the video confirmed by the user. During the preview, the user can add the changes they desire.

[0284] Step 6:

[0285] The terminal converts the confirmed video into an appropriate format and uploads it to the information sharing medium. The input is the video data to be sent to the confirmed cloud, and the output is the uploaded edited video. File format conversion and compression processing are performed as necessary.

[0286] Step 7:

[0287] The server collects the viewing data of the video and the user's reaction from the information sharing medium, and aggregates the data for ranking. The input is the interaction data obtained from the information sharing medium, and the output is the ranking score. The generated AI model processes this data for ranking.

[0288] Step 8:

[0289] The user receives the ranking information distributed from the server on the terminal and checks the evaluation of their own video. The input is the generated ranking data, and the output is the ranking information that can be viewed by the user. Incentives and evaluation information according to the ranking are presented.

[0290] (Application Example 1)

[0291] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0292] In sports viewing, there is a need for a method that allows users to easily select scenes that interest them, personalize their viewing experience, and share it. There is also a need for a system that allows users to easily view post-game comments from players and staff, enriching the viewing experience. Furthermore, a system is needed that allows users to share their selected scenes with other users, thereby improving engagement.

[0293] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0294] In this invention, the server includes an information processing device for users to view viewable information during a competition and select scenes of interest, an information processing means for creating a summary of viewable information based on the selected scenes, and a communication device for transmitting the created summary of viewable information to a sharing means. This allows users to easily generate and share highlight videos that reflect their interests, and after the match, they can view conversations generated by an AI model, enabling a deeper viewing experience.

[0295] A "user" is an individual or group that uses this system to experience watching a game.

[0296] "Viewable information" refers to media data that allows for visual or auditory observation of the progress of events such as matches.

[0297] An "information processing device" is a device that processes information selected by the user and converts the data into an appropriate format.

[0298] "Information processing means" refers to software or hardware used to analyze, transform, and process data according to a specific purpose.

[0299] A "communication device" is hardware or a part thereof that has network connectivity capabilities for sending and receiving data.

[0300] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate new data or content.

[0301] "Conversation content" refers to information provided by the generated AI, which is equivalent to the opinions and comments of players and related personnel.

[0302] "Sharing methods" refer to methods or channels for exchanging data among users and making it accessible to other users.

[0303] "Viewing experience" refers to the act of users watching sports events or similar events through video and audio.

[0304] The system that implements this application consists of a user terminal, a server, and a communication network. Users view available information, such as sports matches, in real time using a smartphone or similar information processing device. When a user selects a scene that interests them during viewing, the timestamp of that scene and related data are recorded on the terminal. This allows users to personalize their viewing experience.

[0305] The server has information processing means for generating a summary of viewable information for the user based on selected scenes. This summary is then edited into a highlight video by merging relevant information. Video editing and cutting are performed efficiently using video processing libraries such as FFmpeg. Users can add customizations such as music and text to the generated highlights. The completed highlights are uploaded to the server via a communication device and made available to other users through an information sharing medium.

[0306] Furthermore, the server uses the generative AI model to generate the conversation content after the game and distribute it to the user terminal. As a result, the user can view the opinions of the players and related parties after the game and gain a further understanding. As a specific example, a prompt sentence for simulating the interview of the player after the game can be considered. For example, prompts such as "Please generate the content of the interview after the game. Player's name: Player Tanaka, Game content: The goal that decided the victory. Please describe your feelings about this goal humorously and movingly." can be cited. In this way, the sports viewing experience becomes highly interactive and personalized.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The user receives the viewable information using the terminal and views it in real time. As input, streaming data of the viewable information is provided. As an operation, the terminal displays this stream and provides an interface so that the user can select a scene.

[0310] Step 2:

[0311] When the user selects a scene of interest, the terminal records the timestamp and related metadata of that scene. The input here is the user's selection operation, and as output, the timestamp and metadata are stored in the local database. As an operation, the terminal monitors the event of pressing the selection button and captures the necessary data.

[0312] Step 3:

[0313] The terminal sends the recorded timestamp and metadata to the server. The input is the stored data, and as output, communication to the server is performed. As an operation, the terminal uploads the data to the server using a secure communication protocol (e.g., HTTPS).

[0314] Step 4:

[0315] The server uses the received data to generate a summary of viewable information using a video processing library such as FFmpeg. The input consists of timestamps, metadata, and the original video data, while the output is the generated highlight video. Operationally, the server extracts a specified range of video and edits it if necessary.

[0316] Step 5:

[0317] The user receives the highlight video generated on their device and customizes the music and text as needed. The input is the generated highlight video, and the output is the customized video. The interface allows the user to easily edit the video.

[0318] Step 6:

[0319] The customized highlights are sent back to the server and made public to other users via an information sharing medium. The input is the customized video, and the output is the publicly available state to other users. In operation, the server uploads the data to the media platform and performs access control.

[0320] Step 7:

[0321] After the match, the server uses a generative AI model to generate conversation content based on the prompt text and delivers it to the user. The input is the prompt text and the AI ​​model, and the output is the generated conversation content. In operation, the server activates the AI ​​model, processes the prompt, and generates the content.

[0322] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0323] In implementing this invention, users can view match footage in real time using a dedicated terminal application. The terminal receives streaming data of the match delivered from a server and presents it to the user. While viewing the video, the user uses an interface to select impressive scenes. The data of the selected scenes is stored on the terminal and used to create subsequent highlight videos.

[0324] Furthermore, the device incorporates an emotion engine that recognizes the user's emotions in real time. It analyzes the user's facial expressions and voice tone to determine their emotional state while watching. This data is used to further personalize the viewing experience. For example, it can extract scenes that particularly excite the user and create custom highlights featuring those scenes.

[0325] After creating a highlight video, the device uploads the video to an information sharing platform. This process includes personalized elements created based on sentiment data. The published video can receive reactions from other users, and these reactions are collected on the server.

[0326] The server generates user rankings based on collected reaction data. The rankings are updated in real time, and a system is in place to reward users who rank highly. Furthermore, the aggregated sentiment data is used to analyze the overall emotional trends of the audience, generating reports that can be used for post-match reviews and marketing strategies.

[0327] Through this process, the present invention aims to provide a personalized sports viewing experience that reflects the user's emotions, promote interaction among users, and improve overall engagement.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The server collects live video and audio data from the match, analyzes and tags it in real time, and delivers this information as a stream to the user's device.

[0331] Step 2:

[0332] The device displays video received from the server to the user. Simultaneously, it activates an emotion engine, using the camera and microphone to recognize emotions from the user's facial expressions and voice. The recognized emotion data is recorded along with the video playback.

[0333] Step 3:

[0334] The user selects scenes of interest using the device's interface. The selected scenes are associated with recorded emotional data, along with a timestamp.

[0335] Step 4:

[0336] The device edits a personalized highlight video based on scenes and emotion data selected by the user. The user can rearrange scenes and add commentary and music using the editing interface.

[0337] Step 5:

[0338] The edited highlight video is uploaded to the information sharing platform via the server from the device. The uploaded video is tagged with sentiment data, making it viewable and reactable to by other users.

[0339] Step 6:

[0340] The server collects reactions from other users to videos on the information sharing platform and generates rankings. The rankings take into account emotional engagement, and notifications are sent to top-ranking users.

[0341] Step 7:

[0342] After the match ends, the server analyzes the collected sentiment data and generates a report showing the overall sentiment trends of the viewers. This report is used as insight for future strategies, such as marketing strategies.

[0343] (Example 2)

[0344] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0345] In sports viewing, there are limitations to obtaining a video experience that reflects the viewer's own emotional state, and there is a lack of means to enhance interaction and engagement with other viewers. In particular, it is difficult to provide video that matches the interests and emotions of each individual viewer, and there is a need to deepen the live experience.

[0346] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0347] In this invention, the server includes a device for users to view image data during a match and select scenes of interest, processing means for creating digital images based on the data of the selected scenes, and transmission means for publishing the created digital images to an information sharing platform. This enables a personalized sports viewing experience that reflects the emotional state of each individual user.

[0348] "Users" refers to individuals or groups who operate the system to watch sports or view digital videos.

[0349] "Image data" refers to visual information received from a server or terminal during a match and presented to the user.

[0350] A "scene" refers to a portion of the video footage that a user selects during a match to identify or pinpoint something that interests them.

[0351] "Device" refers to an electronic device used by users to view image data and select scenes that interest them.

[0352] "Digital images" refer to visual information that is processed and generated using engineering methods based on selected scenes.

[0353] An "information sharing platform" refers to an online platform where multiple users can publish digital videos and interact with each other.

[0354] "Emotional state" refers to information obtained by analyzing the mental state that expresses the user's mood and emotions.

[0355] "Management means" refers to a function that generates rankings based on the popularity of publicly available digital videos and user reactions.

[0356] This invention is a system that allows users to watch a match, extract scenes of interest, generate highlight videos, and share them with other users. Specifically, it is implemented through interaction between a server, a terminal, and the user.

[0357] Users first use a dedicated terminal to receive match image data from the server in streaming format. Commonly used streaming technologies include HLS (HTTP Live Streaming) and DASH (Dynamic Adaptive Streaming over HTTP). The terminal decodes this data and displays the match video on its screen.

[0358] During viewing, users can select scenes of interest using the interface. The interface includes a timestamp function and saves the selected scenes as data on the device. Based on the data of the selected scenes, the device generates highlights as digital video. Video editing is performed using the FFmpeg library.

[0359] Furthermore, the device is equipped with emotion recognition capabilities, using OpenCV to analyze the user's facial expressions in real time and Librosa to analyze their voice tone. This allows the device to determine the user's emotional state and add personalized elements to the digital video. Personalization based on emotional state further enriches the viewing experience.

[0360] The device uploads the completed digital video to information sharing platforms such as YouTube and Vimeo using APIs. A description generated based on the viewer's emotional state is also automatically added. The published video gathers reactions from other users, and the server generates a popularity ranking based on these reactions.

[0361] This allows the server to improve user engagement and deepen individual viewing experiences. As part of this process, an example of a prompt for the generative AI model would be: "Create a highlight video by extracting particularly exciting moments from the user's emotional data." In this way, it becomes possible to generate and share digital images that reflect the user's emotions.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The user activates a dedicated terminal and begins streaming the match. The terminal receives match image data in real time from the server using the HLS or DASH protocol. The input is the streaming data from the server, and the output is the visual information displayed on the terminal's screen. Specifically, the terminal decodes the received data and buffers it for smooth playback.

[0365] Step 2:

[0366] During viewing, the user selects scenes of interest via the interface. The device saves this selection information, including timestamps. The input is the timestamps of the scenes selected by the user, and the output is a list of scenes stored in an internal database. Specifically, marking the selected points creates data for later scene editing.

[0367] Step 3:

[0368] The device uses its built-in camera and microphone to recognize the user's emotional state. It analyzes facial expressions using OpenCV and voice tone using Librosa. The input is real-time audio and video data of the user, and the output is information on the analyzed emotional state. Specifically, it extracts facial feature points to determine emotions in real time and measures the intensity of emotions by analyzing the pitch and intensity of the voice.

[0369] Step 4:

[0370] The device creates digital highlights using a saved list of scenes and emotional information. It edits selected scenes using the FFmpeg library, incorporating personalized content based on emotional data. The input is a list of selected scenes and the user's emotional information, and the output is a visually organized digital video. Specifically, it crops footage to highlight peak emotional scenes and rearranges it according to a template.

[0371] Step 5:

[0372] The terminal uploads the completed digital video to an information sharing platform via the internet. Using an API, it automatically generates titles and descriptions and configures publication settings. The input is the completed digital video and generated metadata, and the output is the video content published online. Specifically, it establishes a connection to a video sharing service and uploads all necessary files.

[0373] Step 6:

[0374] The server collects reactions from other users to uploaded videos. It then analyzes this data to generate a popularity ranking. The input is reaction data from other users, and the output is a popularity ranking that updates in real time. Specifically, it aggregates viewing time and evaluation comments, and calculates the overall system ranking based on evaluation points.

[0375] Step 7:

[0376] The server comprehensively analyzes the collected sentiment data and reactions to create a report on viewing trends. This information is used for developing marketing strategies, among other things. The input is the sentiment data and reaction statistics of each user, and the output is a report as a result of the analysis. Specifically, the process involves visualizing trends using data mining techniques and generating a report as the output.

[0377] (Application Example 2)

[0378] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0379] In modern digital entertainment, personalizing content for users is crucial, but many challenges remain, particularly in individualizing the user experience in live video. Traditional methods require users to manually select video highlights, and there is a lack of effective means to leverage emotional data to improve user engagement. Furthermore, systems that generate rankings based on the popularity of video highlights to promote competition among users are also lacking.

[0380] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0381] In this invention, the server includes data processing means for automatically detecting scenes that excited the user while they were watching video content, data processing means for generating personalized video highlights based on the detected emotion data, and transmission means for transmitting the generated video highlights to an information transmission medium. This enables real-time video highlight generation utilizing the user's emotion data and the creation of rankings to enhance user engagement.

[0382] A "user" is a viewer who shows excitement or interest after watching video content.

[0383] "Video content" refers to information in video format that is delivered to users through their sight and hearing.

[0384] An "exciting scene" refers to a portion of the video in which the user's emotional data specifically detects a state of excitement.

[0385] "Data processing means" refers to a device or program that has the function of analyzing acquired emotional information and generating target data based on a specific algorithm.

[0386] "Personalized video highlights" are video digests edited to match each user's individual interests and preferences based on their emotional data.

[0387] An "information transmission medium" refers to an online platform or communication method used to show generated video highlights to other users.

[0388] "Transfer means" refers to the technology or device used to transmit generated data to other terminals or platforms.

[0389] A "ranking" is information that shows evaluations and rankings generated based on user activity and responses.

[0390] The system for implementing this invention consists of a server that provides video content, a terminal that detects the user's emotions, and various software that handles the generated data.

[0391] The server delivers video data to the user's device in real time using a streaming API. The user views the video using a smartphone or other digital device. At this time, the device uses its built-in camera and microphone, along with emotion analysis libraries such as OpenCV and TensorFlow, to analyze the user's facial expressions and voice tone and acquire real-time emotion data.

[0392] After collecting emotional data, the device automatically extracts scenes where the user is deemed excited, using video editing libraries such as FFMpeg. This generates personalized video highlights, which are then transferred to a communication medium.

[0393] The server aggregates and analyzes reactions, using a real-time database such as Firebase to calculate user ratings and rankings. The ranking results are continuously updated, and appropriate compensation is provided based on them. This entire system makes users' sports viewing experiences data-driven and personalized, improving overall engagement.

[0394] As a concrete example, during a soccer match, when your team scores, the device analyzes the user's facial expressions and voice to create a special highlight based on those emotions. This highlight can then be shared with friends. An example of a specific prompt message would be, "While watching a soccer match, would you like to have your facial expressions and voice emotions analyzed during scoring moments to create a special highlight that you can share with your friends?" In this way, the user experience becomes richer.

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] The server transmits match video data to the user's device in real time using a streaming API. The input is the match video data, and the output is the video played on the user's device. During this process, the server manages network bandwidth to ensure stable data transfer.

[0398] Step 2:

[0399] The device plays back received video data while operating its built-in camera and microphone, and analyzes the user's facial expressions and voice tone using OpenCV and TensorFlow. The input is the user's video and audio data, and the output is emotion parameters. The device processes this data in real time and generates emotion analysis results.

[0400] Step 3:

[0401] The device extracts scenes where an excited state is determined based on the emotion analysis results. By setting a specific threshold for extraction, it selects the corresponding video range when an excitement level above a certain level is detected. The input is emotion parameters, and the output is metadata of the selected scenes.

[0402] Step 4:

[0403] The device uses a video editing library such as FFMpeg to aggregate extracted scenes and generate a personalized highlight video. The input is metadata of the selected scenes, and the output is a highlight video file. The generated video is saved in the user's viewing history.

[0404] Step 5:

[0405] The terminal transfers the generated highlight video to a communication medium and shares it with other users via a user interface. The input is a highlight video file, and the output is publicly available on an online platform. Users can receive comments on this video.

[0406] Step 6:

[0407] The server collects reaction data from other users aggregated across information transmission media and generates ratings and rankings using a real-time database such as Firebase. The input is reaction data, and the output is updated ratings and rankings. The server updates the user's profile based on this.

[0408] Step 7:

[0409] The server provides rewards to users who rank highly based on the ranking results. The input is the updated ranking data, and the output is the rewards and benefits provided. The server automatically handles reward transfers and point awards.

[0410] 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.

[0411] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0412] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0413] [Third Embodiment]

[0414] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0415] 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.

[0416] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0417] 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.

[0418] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0419] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0420] 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.

[0421] 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.

[0422] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0423] The 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.

[0424] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0425] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0426] In one embodiment of the present invention, users can view real-time video footage of a match using a dedicated terminal application. This application has the function of receiving match data distributed from a server and displaying it on the user's terminal.

[0427] Users can select scenes that interest them during a match on their device. This selection is made through a visual interface by tapping specific buttons, and the timestamp and related data for the selected scene are recorded on the device. This allows users to personalize their viewing experience.

[0428] The selected scenes are combined on the device and edited into a highlight video. The edited highlights can be customized by adding text overlays and music. The completed highlight video is sent from the device to the information sharing medium.

[0429] The server publishes the submitted highlight videos on an information sharing platform tailored to each user. The published videos can receive reactions from other users through an interface. The server collects this data and builds a ranking based on the degree of interaction. This ranking is generated by an internal management system and displayed to users as a ranking.

[0430] Furthermore, after the match, virtual interviews created using automated methods are delivered from the server to the user's device. This provides viewers with the opinions of players and staff, further deepening their understanding of the match.

[0431] Thus, the present invention aims to make the sports viewing experience more interactive and engaging by enabling users to actively participate during and after matches.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The server collects video and audio data through multiple cameras and sensors during the match. The collected data is analyzed in real time and tagged to specific events (goals, fouls, etc.). The server then delivers the analyzed data to terminals in a streaming format.

[0435] Step 2:

[0436] The device displays a live stream of the match received from the server to the user. This includes video, audio, and tagged event information. While watching the video, the user can select any scene and mark it by pressing a specific button.

[0437] Step 3:

[0438] When a user selects a scene, the device records the scene's timestamp and associated metadata. This recorded data is later used in the editing process. This allows the user to collect material for custom highlight clips.

[0439] Step 4:

[0440] The device provides an interface for editing highlight videos by combining scenes selected by the user. Users can rearrange multiple selected scenes by dragging and dropping, and add text and music.

[0441] Step 5:

[0442] Once the user has finished editing the highlight video, the device renders the video in the specified format and saves it as a single file. This file is then ready to be uploaded to a sharing platform later.

[0443] Step 6:

[0444] The device uploads the completed highlight video to the information sharing platform via the server. The uploaded video is made public to other users on the platform, allowing them to collect reactions such as comments and "likes."

[0445] Step 7:

[0446] The server collects data on other users' reactions to the published highlight videos. Based on this data, the server calculates user engagement and updates the rankings. The ranking results are fed back to the user's device, allowing the user to check their own ranking.

[0447] Step 8:

[0448] After the match, the server provides a virtual interview generated using automated means. This includes player comments and analysis about the match, and is delivered to the user's device, serving as additional content to deepen their understanding of the game.

[0449] (Example 1)

[0450] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0451] Traditional video viewing systems make it difficult for users to easily select scenes that interest them, create individually edited highlights, and share them with others. Furthermore, the lack of mechanisms to appropriately provide user evaluations and rewards based on post-publication feedback results in a one-way viewing experience, limiting opportunities for deeper participant engagement.

[0452] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0453] In this invention, the server includes means for decoding video using an information processing device, for a user to view video of a match and select scenes of interest, for recording a timestamp and related information as metadata for the selected scenes, and for integrating video fragments based on the user's selection and creating edited video by adding text and audio. This makes it possible for users to easily create individually interactive edited highlights and share and interact with other users.

[0454] An "information processing device" is a device that enables the reception, processing, and presentation of electronic data, and is used to perform advanced calculations such as decoding and streaming.

[0455] A "timestamp" is time information used to record when a particular event occurred, and is used to organize and track data.

[0456] Metadata is additional information that accompanies data, describing its attributes and structure, and is used to facilitate its use and management.

[0457] A "video fragment" is a selected portion or fragment from the entire video, which is the material to be edited or combined.

[0458] A "dialogue device" is a means for a user to communicate with a system and provide feedback or responses, and includes interfaces and input devices.

[0459] A "control mechanism" is a mechanism for monitoring and managing various operations of a system and for issuing instructions or adjustments as needed.

[0460] An "incentive" refers to a reward or stimulus provided to encourage behavior and motivate the achievement of a specific goal.

[0461] A "Q&A video" is a video created based on conversations with relevant parties, used to provide information to viewers and facilitate understanding.

[0462] This invention is a system that allows users to select scenes they find interesting during a match, generate edited videos based on those scenes, and share them with other users. The operation of a specific system that is an embodiment of this invention will be described below.

[0463] Users watch match footage in real time using a dedicated terminal. The terminal receives streaming data from the server and plays the video smoothly using its built-in decoding function. The terminal application provides buttons for visually selecting scenes through the user interface. When a user is interested in a particular scene, tapping the corresponding button records the timestamp and associated metadata of the selected scene. This metadata is used as basic information for scene identification and editing processes.

[0464] The device integrates multiple scenes selected by the user and generates an edited video using automated video editing software. This software allows the user to customize the video by adding text overlays and audio according to their specifications. The finished video is previewed on the device, prompting the user to review the edits.

[0465] Next, the terminal converts the generated video into an appropriate format and uploads it to an information sharing medium. The destination for publication is determined based on the user's selection. The server collects feedback from other users on this published video and aggregates reaction data. Based on the data thus obtained, a user ranking is generated by the control system, and the results are presented to the user.

[0466] As a concrete example, during a soccer match, users can select and edit "goal scenes" to create a 3-minute highlight video with appropriate music. The completed video can be shared on social media and receive "likes" and comments from other users. This enables interactive communication among users.

[0467] An example of a prompt for a generative AI model is, "I want to create a soccer match highlight video. Please generate 5 minutes of content, including goal scenes and player interviews." Through this, the AI ​​can create customized videos that meet the user's requests.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] The server collects real-time video data of the match and encodes it into a streaming format. The input is raw data from cameras and sensors during the match, and the output is compressed streaming data in a format that can be transmitted over the network. During this process, data compression and encoding are performed, preparing the data for transmission to user terminals.

[0471] Step 2:

[0472] The terminal decodes the streaming data received from the server and displays it to the user as video. The input is streaming data from the server, and the output is real-time video of the match that the user can view. The player software on the terminal is responsible for the decoding process and for smoothly playing the video on the screen.

[0473] Step 3:

[0474] Users watch the match on their device and select scenes of interest from the interface. The input is the user's visual judgment, and the output is the timestamp of the selected scene and associated metadata. Selection is made by tapping a button on the device, and this action records the selection data.

[0475] Step 4:

[0476] The terminal extracts and edits selected video segments based on recorded timestamps. The input is user-selected scene information, and the output is the edited video clip. Editing software runs in the background, integrating the video clips and adding text overlays and background music.

[0477] Step 5:

[0478] The user previews the edited video clip on their device to make a final check of the completed video. The input is the edited video clip, and the output is the video reviewed by the user. The user can add any desired changes during the preview.

[0479] Step 6:

[0480] The terminal converts the verified video to the appropriate format and uploads it to the information sharing medium. The input is the video data sent to the cloud after confirmation, and the output is the uploaded edited video. File format conversion and compression are performed as needed.

[0481] Step 7:

[0482] The server collects video viewing data and user reactions from information sharing media and aggregates the data for ranking. The input is interaction data obtained from information sharing media, and the output is a ranking score. A generative AI model processes this data to determine the rankings.

[0483] Step 8:

[0484] Users receive ranking information streamed from the server on their devices and check the evaluation of their videos. The input is the generated ranking data, and the output is the user's viewable ranking information. Incentives and evaluation information are presented according to the ranking.

[0485] (Application Example 1)

[0486] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] In sports viewing, there is a need for a method that allows users to easily select scenes that interest them, personalize their viewing experience, and share it. There is also a need for a system that allows users to easily view post-game comments from players and staff, enriching the viewing experience. Furthermore, a system is needed that allows users to share their selected scenes with other users, thereby improving engagement.

[0488] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0489] In this invention, the server includes an information processing device for users to view viewable information during a competition and select scenes of interest, an information processing means for creating a summary of viewable information based on the selected scenes, and a communication device for transmitting the created summary of viewable information to a sharing means. This allows users to easily generate and share highlight videos that reflect their interests, and after the match, they can view conversations generated by an AI model, enabling a deeper viewing experience.

[0490] A "user" is an individual or group that uses this system to experience watching a game.

[0491] "Viewable information" refers to media data that allows for visual or auditory observation of the progress of events such as matches.

[0492] An "information processing device" is a device that processes information selected by the user and converts the data into an appropriate format.

[0493] "Information processing means" refers to software or hardware used to analyze, transform, and process data according to a specific purpose.

[0494] A "communication device" is hardware or a part thereof that has network connectivity capabilities for sending and receiving data.

[0495] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate new data or content.

[0496] "Conversation content" refers to information provided by the generated AI, which is equivalent to the opinions and comments of players and related personnel.

[0497] "Sharing methods" refer to methods or channels for exchanging data among users and making it accessible to other users.

[0498] "Viewing experience" refers to the act of users watching sports events or similar events through video and audio.

[0499] The system that implements this application consists of a user terminal, a server, and a communication network. Users view available information, such as sports matches, in real time using a smartphone or similar information processing device. When a user selects a scene that interests them during viewing, the timestamp of that scene and related data are recorded on the terminal. This allows users to personalize their viewing experience.

[0500] The server has information processing means for generating a summary of viewable information for the user based on selected scenes. This summary is then edited into a highlight video by merging relevant information. Video editing and cutting are performed efficiently using video processing libraries such as FFmpeg. Users can add customizations such as music and text to the generated highlights. The completed highlights are uploaded to the server via a communication device and made available to other users through an information sharing medium.

[0501] Furthermore, the server uses a generative AI model to generate conversation content after the match and deliver it to the user's terminal. This allows users to view the opinions of players and staff after the match, gaining a deeper understanding. As a concrete example, a prompt that simulates a post-match interview with a player could be considered. For example, a prompt such as, "Generate the content of the post-match interview. Player name: Tanaka, Match content: A goal that decided the victory. Please give your thoughts on this goal in a humorous and moving way." In this way, the sports viewing experience becomes highly interactive and personalized.

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] Users receive viewing information using their devices and view it in real time. Streaming data of the viewing information is provided as input. The device displays this stream and provides an interface that allows the user to select scenes.

[0505] Step 2:

[0506] When a user selects a scene of interest, the device records the timestamp and associated metadata for that scene. The input here is the user's selection action, and the output—the timestamp and metadata—is saved to a local database. Operationally, the device monitors the event of a selection button press and captures the necessary data.

[0507] Step 3:

[0508] The terminal sends recorded timestamps and metadata to the server. The input is stored data, and the output is communication to the server. Operationally, the terminal uploads data to the server using a secure communication protocol (e.g., HTTPS).

[0509] Step 4:

[0510] The server uses the received data to generate a summary of viewable information using a video processing library such as FFmpeg. The input consists of timestamps, metadata, and the original video data, while the output is the generated highlight video. Operationally, the server extracts a specified range of video and edits it if necessary.

[0511] Step 5:

[0512] The user receives the highlight video generated on their device and customizes the music and text as needed. The input is the generated highlight video, and the output is the customized video. The interface allows the user to easily edit the video.

[0513] Step 6:

[0514] The customized highlights are sent back to the server and made public to other users via an information sharing medium. The input is the customized video, and the output is the publicly available state to other users. In operation, the server uploads the data to the media platform and performs access control.

[0515] Step 7:

[0516] After the match, the server uses a generative AI model to generate conversation content based on the prompt text and delivers it to the user. The input is the prompt text and the AI ​​model, and the output is the generated conversation content. In operation, the server activates the AI ​​model, processes the prompt, and generates the content.

[0517] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0518] In implementing this invention, users can view match footage in real time using a dedicated terminal application. The terminal receives streaming data of the match delivered from a server and presents it to the user. While viewing the video, the user uses an interface to select impressive scenes. The data of the selected scenes is stored on the terminal and used to create subsequent highlight videos.

[0519] Furthermore, the device incorporates an emotion engine that recognizes the user's emotions in real time. It analyzes the user's facial expressions and voice tone to determine their emotional state while watching. This data is used to further personalize the viewing experience. For example, it can extract scenes that particularly excite the user and create custom highlights featuring those scenes.

[0520] After creating a highlight video, the device uploads the video to an information sharing platform. This process includes personalized elements created based on sentiment data. The published video can receive reactions from other users, and these reactions are collected on the server.

[0521] The server generates user rankings based on collected reaction data. The rankings are updated in real time, and a system is in place to reward users who rank highly. Furthermore, the aggregated sentiment data is used to analyze the overall emotional trends of the audience, generating reports that can be used for post-match reviews and marketing strategies.

[0522] Through this process, the present invention aims to provide a personalized sports viewing experience that reflects the user's emotions, promote interaction among users, and improve overall engagement.

[0523] The following describes the processing flow.

[0524] Step 1:

[0525] The server collects live video and audio data from the match, analyzes and tags it in real time, and delivers this information as a stream to the user's device.

[0526] Step 2:

[0527] The device displays video received from the server to the user. Simultaneously, it activates an emotion engine, using the camera and microphone to recognize emotions from the user's facial expressions and voice. The recognized emotion data is recorded along with the video playback.

[0528] Step 3:

[0529] The user selects scenes of interest using the device's interface. The selected scenes are associated with recorded emotional data, along with a timestamp.

[0530] Step 4:

[0531] The device edits a personalized highlight video based on scenes and emotion data selected by the user. The user can rearrange scenes and add commentary and music using the editing interface.

[0532] Step 5:

[0533] The edited highlight video is uploaded to the information sharing platform via the server from the device. The uploaded video is tagged with sentiment data, making it viewable and reactable to by other users.

[0534] Step 6:

[0535] The server collects reactions from other users to videos on the information sharing platform and generates rankings. The rankings take into account emotional engagement, and notifications are sent to top-ranking users.

[0536] Step 7:

[0537] After the match ends, the server analyzes the collected sentiment data and generates a report showing the overall sentiment trends of the viewers. This report is used as insight for future strategies, such as marketing strategies.

[0538] (Example 2)

[0539] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0540] In sports viewing, there are limitations to obtaining a video experience that reflects the viewer's own emotional state, and there is a lack of means to enhance interaction and engagement with other viewers. In particular, it is difficult to provide video that matches the interests and emotions of each individual viewer, and there is a need to deepen the live experience.

[0541] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0542] In this invention, the server includes a device for users to view image data during a match and select scenes of interest, processing means for creating digital images based on the data of the selected scenes, and transmission means for publishing the created digital images to an information sharing platform. This enables a personalized sports viewing experience that reflects the emotional state of each individual user.

[0543] "Users" refers to individuals or groups who operate the system to watch sports or view digital videos.

[0544] "Image data" refers to visual information received from a server or terminal during a match and presented to the user.

[0545] A "scene" refers to a portion of the video footage that a user selects during a match to identify or pinpoint something that interests them.

[0546] "Device" refers to an electronic device used by users to view image data and select scenes that interest them.

[0547] "Digital images" refer to visual information that is processed and generated using engineering methods based on selected scenes.

[0548] An "information sharing platform" refers to an online platform where multiple users can publish digital videos and interact with each other.

[0549] "Emotional state" refers to information obtained by analyzing the mental state that expresses the user's mood and emotions.

[0550] "Management means" refers to a function that generates rankings based on the popularity of publicly available digital videos and user reactions.

[0551] This invention is a system that allows users to watch a match, extract scenes of interest, generate highlight videos, and share them with other users. Specifically, it is implemented through interaction between a server, a terminal, and the user.

[0552] Users first use a dedicated terminal to receive match image data from the server in streaming format. Commonly used streaming technologies include HLS (HTTP Live Streaming) and DASH (Dynamic Adaptive Streaming over HTTP). The terminal decodes this data and displays the match video on its screen.

[0553] During viewing, users can select scenes of interest using the interface. The interface includes a timestamp function and saves the selected scenes as data on the device. Based on the data of the selected scenes, the device generates highlights as digital video. Video editing is performed using the FFmpeg library.

[0554] Furthermore, the device is equipped with emotion recognition capabilities, using OpenCV to analyze the user's facial expressions in real time and Librosa to analyze their voice tone. This allows the device to determine the user's emotional state and add personalized elements to the digital video. Personalization based on emotional state further enriches the viewing experience.

[0555] The device uploads the completed digital video to information sharing platforms such as YouTube and Vimeo using APIs. A description generated based on the viewer's emotional state is also automatically added. The published video gathers reactions from other users, and the server generates a popularity ranking based on these reactions.

[0556] This allows the server to improve user engagement and deepen individual viewing experiences. As part of this process, an example of a prompt for the generative AI model would be: "Create a highlight video by extracting particularly exciting moments from the user's emotional data." In this way, it becomes possible to generate and share digital images that reflect the user's emotions.

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] The user activates a dedicated terminal and begins streaming the match. The terminal receives match image data in real time from the server using the HLS or DASH protocol. The input is the streaming data from the server, and the output is the visual information displayed on the terminal's screen. Specifically, the terminal decodes the received data and buffers it for smooth playback.

[0560] Step 2:

[0561] During viewing, the user selects scenes of interest via the interface. The device saves this selection information, including timestamps. The input is the timestamps of the scenes selected by the user, and the output is a list of scenes stored in an internal database. Specifically, marking the selected points creates data for later scene editing.

[0562] Step 3:

[0563] The device uses its built-in camera and microphone to recognize the user's emotional state. It analyzes facial expressions using OpenCV and voice tone using Librosa. The input is real-time audio and video data of the user, and the output is information on the analyzed emotional state. Specifically, it extracts facial feature points to determine emotions in real time and measures the intensity of emotions by analyzing the pitch and intensity of the voice.

[0564] Step 4:

[0565] The device creates digital highlights using a saved list of scenes and emotional information. It edits selected scenes using the FFmpeg library, incorporating personalized content based on emotional data. The input is a list of selected scenes and the user's emotional information, and the output is a visually organized digital video. Specifically, it crops footage to highlight peak emotional scenes and rearranges it according to a template.

[0566] Step 5:

[0567] The terminal uploads the completed digital video to an information sharing platform via the internet. Using an API, it automatically generates titles and descriptions and configures publication settings. The input is the completed digital video and generated metadata, and the output is the video content published online. Specifically, it establishes a connection to a video sharing service and uploads all necessary files.

[0568] Step 6:

[0569] The server collects reactions from other users to uploaded videos. It then analyzes this data to generate a popularity ranking. The input is reaction data from other users, and the output is a popularity ranking that updates in real time. Specifically, it aggregates viewing time and evaluation comments, and calculates the overall system ranking based on evaluation points.

[0570] Step 7:

[0571] The server comprehensively analyzes the collected sentiment data and reactions to create a report on viewing trends. This information is used for developing marketing strategies, among other things. The input is the sentiment data and reaction statistics of each user, and the output is a report as a result of the analysis. Specifically, the process involves visualizing trends using data mining techniques and generating a report as the output.

[0572] (Application Example 2)

[0573] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0574] In modern digital entertainment, personalizing content for users is crucial, but many challenges remain, particularly in individualizing the user experience in live video. Traditional methods require users to manually select video highlights, and there is a lack of effective means to leverage emotional data to improve user engagement. Furthermore, systems that generate rankings based on the popularity of video highlights to promote competition among users are also lacking.

[0575] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0576] In this invention, the server includes data processing means for automatically detecting scenes that excited the user while they were watching video content, data processing means for generating personalized video highlights based on the detected emotion data, and transmission means for transmitting the generated video highlights to an information transmission medium. This enables real-time video highlight generation utilizing the user's emotion data and the creation of rankings to enhance user engagement.

[0577] A "user" is a viewer who shows excitement or interest after watching video content.

[0578] "Video content" refers to information in video format that is delivered to users through their sight and hearing.

[0579] An "exciting scene" refers to a portion of the video in which the user's emotional data specifically detects a state of excitement.

[0580] "Data processing means" refers to a device or program that has the function of analyzing acquired emotional information and generating target data based on a specific algorithm.

[0581] "Personalized video highlights" are video digests edited to match each user's individual interests and preferences based on their emotional data.

[0582] An "information transmission medium" refers to an online platform or communication method used to show generated video highlights to other users.

[0583] "Transfer means" refers to the technology or device used to transmit generated data to other terminals or platforms.

[0584] A "ranking" is information that shows evaluations and rankings generated based on user activity and responses.

[0585] The system for implementing this invention consists of a server that provides video content, a terminal that detects the user's emotions, and various software that handles the generated data.

[0586] The server delivers video data to the user's device in real time using a streaming API. The user views the video using a smartphone or other digital device. At this time, the device uses its built-in camera and microphone, along with emotion analysis libraries such as OpenCV and TensorFlow, to analyze the user's facial expressions and voice tone and acquire real-time emotion data.

[0587] After collecting emotional data, the device automatically extracts scenes where the user is deemed excited, using video editing libraries such as FFMpeg. This generates personalized video highlights, which are then transferred to a communication medium.

[0588] The server aggregates and analyzes reactions, using a real-time database such as Firebase to calculate user ratings and rankings. The ranking results are continuously updated, and appropriate compensation is provided based on them. This entire system makes users' sports viewing experiences data-driven and personalized, improving overall engagement.

[0589] As a concrete example, during a soccer match, when your team scores, the device analyzes the user's facial expressions and voice to create a special highlight based on those emotions. This highlight can then be shared with friends. An example of a specific prompt message would be, "While watching a soccer match, would you like to have your facial expressions and voice emotions analyzed during scoring moments to create a special highlight that you can share with your friends?" In this way, the user experience becomes richer.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The server transmits match video data to the user's device in real time using a streaming API. The input is the match video data, and the output is the video played on the user's device. During this process, the server manages network bandwidth to ensure stable data transfer.

[0593] Step 2:

[0594] The device plays back received video data while operating its built-in camera and microphone, and analyzes the user's facial expressions and voice tone using OpenCV and TensorFlow. The input is the user's video and audio data, and the output is emotion parameters. The device processes this data in real time and generates emotion analysis results.

[0595] Step 3:

[0596] The device extracts scenes where an excited state is determined based on the emotion analysis results. By setting a specific threshold for extraction, it selects the corresponding video range when an excitement level above a certain level is detected. The input is emotion parameters, and the output is metadata of the selected scenes.

[0597] Step 4:

[0598] The device uses a video editing library such as FFMpeg to aggregate extracted scenes and generate a personalized highlight video. The input is metadata of the selected scenes, and the output is a highlight video file. The generated video is saved in the user's viewing history.

[0599] Step 5:

[0600] The terminal transfers the generated highlight video to a communication medium and shares it with other users via a user interface. The input is a highlight video file, and the output is publicly available on an online platform. Users can receive comments on this video.

[0601] Step 6:

[0602] The server collects reaction data from other users aggregated across information transmission media and generates ratings and rankings using a real-time database such as Firebase. The input is reaction data, and the output is updated ratings and rankings. The server updates the user's profile based on this.

[0603] Step 7:

[0604] The server provides rewards to users who rank highly based on the ranking results. The input is the updated ranking data, and the output is the rewards and benefits provided. The server automatically handles reward transfers and point awards.

[0605] 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.

[0606] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0607] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0608] [Fourth Embodiment]

[0609] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0610] 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.

[0611] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0612] 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.

[0613] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0614] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0615] 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.

[0616] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0617] 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.

[0618] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0619] The 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.

[0620] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0621] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] In one embodiment of the present invention, users can view real-time video footage of a match using a dedicated terminal application. This application has the function of receiving match data distributed from a server and displaying it on the user's terminal.

[0623] Users can select scenes that interest them during a match on their device. This selection is made through a visual interface by tapping specific buttons, and the timestamp and related data for the selected scene are recorded on the device. This allows users to personalize their viewing experience.

[0624] The selected scenes are combined on the device and edited into a highlight video. The edited highlights can be customized by adding text overlays and music. The completed highlight video is sent from the device to the information sharing medium.

[0625] The server publishes the submitted highlight videos on an information sharing platform tailored to each user. The published videos can receive reactions from other users through an interface. The server collects this data and builds a ranking based on the degree of interaction. This ranking is generated by an internal management system and displayed to users as a ranking.

[0626] Furthermore, after the match, virtual interviews created using automated methods are delivered from the server to the user's device. This provides viewers with the opinions of players and staff, further deepening their understanding of the match.

[0627] Thus, the present invention aims to make the sports viewing experience more interactive and engaging by enabling users to actively participate during and after matches.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The server collects video and audio data through multiple cameras and sensors during the match. The collected data is analyzed in real time and tagged to specific events (goals, fouls, etc.). The server then delivers the analyzed data to terminals in a streaming format.

[0631] Step 2:

[0632] The device displays a live stream of the match received from the server to the user. This includes video, audio, and tagged event information. While watching the video, the user can select any scene and mark it by pressing a specific button.

[0633] Step 3:

[0634] When a user selects a scene, the device records the scene's timestamp and associated metadata. This recorded data is later used in the editing process. This allows the user to collect material for custom highlight clips.

[0635] Step 4:

[0636] The device provides an interface for editing highlight videos by combining scenes selected by the user. Users can rearrange multiple selected scenes by dragging and dropping, and add text and music.

[0637] Step 5:

[0638] Once the user has finished editing the highlight video, the device renders the video in the specified format and saves it as a single file. This file is then ready to be uploaded to a sharing platform later.

[0639] Step 6:

[0640] The device uploads the completed highlight video to the information sharing platform via the server. The uploaded video is made public to other users on the platform, allowing them to collect reactions such as comments and "likes."

[0641] Step 7:

[0642] The server collects data on other users' reactions to the published highlight videos. Based on this data, the server calculates user engagement and updates the rankings. The ranking results are fed back to the user's device, allowing the user to check their own ranking.

[0643] Step 8:

[0644] After the match, the server provides a virtual interview generated using automated means. This includes player comments and analysis about the match, and is delivered to the user's device, serving as additional content to deepen their understanding of the game.

[0645] (Example 1)

[0646] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0647] Traditional video viewing systems make it difficult for users to easily select scenes that interest them, create individually edited highlights, and share them with others. Furthermore, the lack of mechanisms to appropriately provide user evaluations and rewards based on post-publication feedback results in a one-way viewing experience, limiting opportunities for deeper participant engagement.

[0648] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0649] In this invention, the server includes means for decoding video using an information processing device, for a user to view video of a match and select scenes of interest, for recording a timestamp and related information as metadata for the selected scenes, and for integrating video fragments based on the user's selection and creating edited video by adding text and audio. This makes it possible for users to easily create individually interactive edited highlights and share and interact with other users.

[0650] An "information processing device" is a device that enables the reception, processing, and presentation of electronic data, and is used to perform advanced calculations such as decoding and streaming.

[0651] A "timestamp" is time information used to record when a particular event occurred, and is used to organize and track data.

[0652] Metadata is additional information that accompanies data, describing its attributes and structure, and is used to facilitate its use and management.

[0653] A "video fragment" is a selected portion or fragment from the entire video, which is the material to be edited or combined.

[0654] A "dialogue device" is a means for a user to communicate with a system and provide feedback or responses, and includes interfaces and input devices.

[0655] A "control mechanism" is a mechanism for monitoring and managing various operations of a system and for issuing instructions or adjustments as needed.

[0656] An "incentive" refers to a reward or stimulus provided to encourage behavior and motivate the achievement of a specific goal.

[0657] A "Q&A video" is a video created based on conversations with relevant parties, used to provide information to viewers and facilitate understanding.

[0658] This invention is a system that allows users to select scenes they find interesting during a match, generate edited videos based on those scenes, and share them with other users. The operation of a specific system that is an embodiment of this invention will be described below.

[0659] Users watch match footage in real time using a dedicated terminal. The terminal receives streaming data from the server and plays the video smoothly using its built-in decoding function. The terminal application provides buttons for visually selecting scenes through the user interface. When a user is interested in a particular scene, tapping the corresponding button records the timestamp and associated metadata of the selected scene. This metadata is used as basic information for scene identification and editing processes.

[0660] The device integrates multiple scenes selected by the user and generates an edited video using automated video editing software. This software allows the user to customize the video by adding text overlays and audio according to their specifications. The finished video is previewed on the device, prompting the user to review the edits.

[0661] Next, the terminal converts the generated video into an appropriate format and uploads it to an information sharing medium. The destination for publication is determined based on the user's selection. The server collects feedback from other users on this published video and aggregates reaction data. Based on the data thus obtained, a user ranking is generated by the control system, and the results are presented to the user.

[0662] As a concrete example, during a soccer match, users can select and edit "goal scenes" to create a 3-minute highlight video with appropriate music. The completed video can be shared on social media and receive "likes" and comments from other users. This enables interactive communication among users.

[0663] An example of a prompt for a generative AI model is, "I want to create a soccer match highlight video. Please generate 5 minutes of content, including goal scenes and player interviews." Through this, the AI ​​can create customized videos that meet the user's requests.

[0664] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0665] Step 1:

[0666] The server collects real-time video data of the match and encodes it into a streaming format. The input is raw data from cameras and sensors during the match, and the output is compressed streaming data in a format that can be transmitted over the network. During this process, data compression and encoding are performed, preparing the data for transmission to user terminals.

[0667] Step 2:

[0668] The terminal decodes the streaming data received from the server and displays it to the user as video. The input is streaming data from the server, and the output is real-time video of the match that the user can view. The player software on the terminal is responsible for the decoding process and for smoothly playing the video on the screen.

[0669] Step 3:

[0670] Users watch the match on their device and select scenes of interest from the interface. The input is the user's visual judgment, and the output is the timestamp of the selected scene and associated metadata. Selection is made by tapping a button on the device, and this action records the selection data.

[0671] Step 4:

[0672] The terminal extracts and edits selected video segments based on recorded timestamps. The input is user-selected scene information, and the output is the edited video clip. Editing software runs in the background, integrating the video clips and adding text overlays and background music.

[0673] Step 5:

[0674] The user previews the edited video clip on their device to make a final check of the completed video. The input is the edited video clip, and the output is the video reviewed by the user. The user can add any desired changes during the preview.

[0675] Step 6:

[0676] The terminal converts the verified video to the appropriate format and uploads it to the information sharing medium. The input is the video data sent to the cloud after confirmation, and the output is the uploaded edited video. File format conversion and compression are performed as needed.

[0677] Step 7:

[0678] The server collects video viewing data and user reactions from information sharing media and aggregates the data for ranking. The input is interaction data obtained from information sharing media, and the output is a ranking score. A generative AI model processes this data to determine the rankings.

[0679] Step 8:

[0680] Users receive ranking information streamed from the server on their devices and check the evaluation of their videos. The input is the generated ranking data, and the output is the user's viewable ranking information. Incentives and evaluation information are presented according to the ranking.

[0681] (Application Example 1)

[0682] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0683] In sports viewing, there is a need for a method that allows users to easily select scenes that interest them, personalize their viewing experience, and share it. There is also a need for a system that allows users to easily view post-game comments from players and staff, enriching the viewing experience. Furthermore, a system is needed that allows users to share their selected scenes with other users, thereby improving engagement.

[0684] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0685] In this invention, the server includes an information processing device for users to view viewable information during a competition and select scenes of interest, an information processing means for creating a summary of viewable information based on the selected scenes, and a communication device for transmitting the created summary of viewable information to a sharing means. This allows users to easily generate and share highlight videos that reflect their interests, and after the match, they can view conversations generated by an AI model, enabling a deeper viewing experience.

[0686] A "user" is an individual or group that uses this system to experience watching a game.

[0687] "Viewable information" refers to media data that allows for visual or auditory observation of the progress of events such as matches.

[0688] An "information processing device" is a device that processes information selected by the user and converts the data into an appropriate format.

[0689] "Information processing means" refers to software or hardware used to analyze, transform, and process data according to a specific purpose.

[0690] A "communication device" is hardware or a part thereof that has network connectivity capabilities for sending and receiving data.

[0691] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate new data or content.

[0692] "Conversation content" refers to information provided by the generated AI, which is equivalent to the opinions and comments of players and related personnel.

[0693] "Sharing methods" refer to methods or channels for exchanging data among users and making it accessible to other users.

[0694] "Viewing experience" refers to the act of users watching sports events or similar events through video and audio.

[0695] The system that implements this application consists of a user terminal, a server, and a communication network. Users view available information, such as sports matches, in real time using a smartphone or similar information processing device. When a user selects a scene that interests them during viewing, the timestamp of that scene and related data are recorded on the terminal. This allows users to personalize their viewing experience.

[0696] The server has information processing means for generating a summary of viewable information for the user based on selected scenes. This summary is then edited into a highlight video by merging relevant information. Video editing and cutting are performed efficiently using video processing libraries such as FFmpeg. Users can add customizations such as music and text to the generated highlights. The completed highlights are uploaded to the server via a communication device and made available to other users through an information sharing medium.

[0697] Furthermore, the server uses a generative AI model to generate conversation content after the match and deliver it to the user's terminal. This allows users to view the opinions of players and staff after the match, gaining a deeper understanding. As a concrete example, a prompt that simulates a post-match interview with a player could be considered. For example, a prompt such as, "Generate the content of the post-match interview. Player name: Tanaka, Match content: A goal that decided the victory. Please give your thoughts on this goal in a humorous and moving way." In this way, the sports viewing experience becomes highly interactive and personalized.

[0698] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0699] Step 1:

[0700] Users receive viewing information using their devices and view it in real time. Streaming data of the viewing information is provided as input. The device displays this stream and provides an interface that allows the user to select scenes.

[0701] Step 2:

[0702] When a user selects a scene of interest, the device records the timestamp and associated metadata for that scene. The input here is the user's selection action, and the output—the timestamp and metadata—is saved to a local database. Operationally, the device monitors the event of a selection button press and captures the necessary data.

[0703] Step 3:

[0704] The terminal sends recorded timestamps and metadata to the server. The input is stored data, and the output is communication to the server. Operationally, the terminal uploads data to the server using a secure communication protocol (e.g., HTTPS).

[0705] Step 4:

[0706] The server uses the received data to generate a summary of viewable information using a video processing library such as FFmpeg. The input consists of timestamps, metadata, and the original video data, while the output is the generated highlight video. Operationally, the server extracts a specified range of video and edits it if necessary.

[0707] Step 5:

[0708] The user receives the highlight video generated on their device and customizes the music and text as needed. The input is the generated highlight video, and the output is the customized video. The interface allows the user to easily edit the video.

[0709] Step 6:

[0710] The customized highlights are sent back to the server and made public to other users via an information sharing medium. The input is the customized video, and the output is the publicly available state to other users. In operation, the server uploads the data to the media platform and performs access control.

[0711] Step 7:

[0712] After the match, the server uses a generative AI model to generate conversation content based on the prompt text and delivers it to the user. The input is the prompt text and the AI ​​model, and the output is the generated conversation content. In operation, the server activates the AI ​​model, processes the prompt, and generates the content.

[0713] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0714] In implementing this invention, users can view match footage in real time using a dedicated terminal application. The terminal receives streaming data of the match delivered from a server and presents it to the user. While viewing the video, the user uses an interface to select impressive scenes. The data of the selected scenes is stored on the terminal and used to create subsequent highlight videos.

[0715] Furthermore, the device incorporates an emotion engine that recognizes the user's emotions in real time. It analyzes the user's facial expressions and voice tone to determine their emotional state while watching. This data is used to further personalize the viewing experience. For example, it can extract scenes that particularly excite the user and create custom highlights featuring those scenes.

[0716] After creating a highlight video, the device uploads the video to an information sharing platform. This process includes personalized elements created based on sentiment data. The published video can receive reactions from other users, and these reactions are collected on the server.

[0717] The server generates user rankings based on collected reaction data. The rankings are updated in real time, and a system is in place to reward users who rank highly. Furthermore, the aggregated sentiment data is used to analyze the overall emotional trends of the audience, generating reports that can be used for post-match reviews and marketing strategies.

[0718] Through this process, the present invention aims to provide a personalized sports viewing experience that reflects the user's emotions, promote interaction among users, and improve overall engagement.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] The server collects live video and audio data from the match, analyzes and tags it in real time, and delivers this information as a stream to the user's device.

[0722] Step 2:

[0723] The device displays video received from the server to the user. Simultaneously, it activates an emotion engine, using the camera and microphone to recognize emotions from the user's facial expressions and voice. The recognized emotion data is recorded along with the video playback.

[0724] Step 3:

[0725] The user selects scenes of interest using the device's interface. The selected scenes are associated with recorded emotional data, along with a timestamp.

[0726] Step 4:

[0727] The device edits a personalized highlight video based on scenes and emotion data selected by the user. The user can rearrange scenes and add commentary and music using the editing interface.

[0728] Step 5:

[0729] The edited highlight video is uploaded to the information sharing platform via the server from the device. The uploaded video is tagged with sentiment data, making it viewable and reactable to by other users.

[0730] Step 6:

[0731] The server collects reactions from other users to videos on the information sharing platform and generates rankings. The rankings take into account emotional engagement, and notifications are sent to top-ranking users.

[0732] Step 7:

[0733] After the match ends, the server analyzes the collected sentiment data and generates a report showing the overall sentiment trends of the viewers. This report is used as insight for future strategies, such as marketing strategies.

[0734] (Example 2)

[0735] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] In sports viewing, there are limitations to obtaining a video experience that reflects the viewer's own emotional state, and there is a lack of means to enhance interaction and engagement with other viewers. In particular, it is difficult to provide video that matches the interests and emotions of each individual viewer, and there is a need to deepen the live experience.

[0737] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0738] In this invention, the server includes a device for users to view image data during a match and select scenes of interest, processing means for creating digital images based on the data of the selected scenes, and transmission means for publishing the created digital images to an information sharing platform. This enables a personalized sports viewing experience that reflects the emotional state of each individual user.

[0739] "Users" refers to individuals or groups who operate the system to watch sports or view digital videos.

[0740] "Image data" refers to visual information received from a server or terminal during a match and presented to the user.

[0741] A "scene" refers to a portion of the video footage that a user selects during a match to identify or pinpoint something that interests them.

[0742] "Device" refers to an electronic device used by users to view image data and select scenes that interest them.

[0743] "Digital images" refer to visual information that is processed and generated using engineering methods based on selected scenes.

[0744] An "information sharing platform" refers to an online platform where multiple users can publish digital videos and interact with each other.

[0745] "Emotional state" refers to information obtained by analyzing the mental state that expresses the user's mood and emotions.

[0746] "Management means" refers to a function that generates rankings based on the popularity of publicly available digital videos and user reactions.

[0747] This invention is a system that allows users to watch a match, extract scenes of interest, generate highlight videos, and share them with other users. Specifically, it is implemented through interaction between a server, a terminal, and the user.

[0748] Users first use a dedicated terminal to receive match image data from the server in streaming format. Commonly used streaming technologies include HLS (HTTP Live Streaming) and DASH (Dynamic Adaptive Streaming over HTTP). The terminal decodes this data and displays the match video on its screen.

[0749] During viewing, users can select scenes of interest using the interface. The interface includes a timestamp function and saves the selected scenes as data on the device. Based on the data of the selected scenes, the device generates highlights as digital video. Video editing is performed using the FFmpeg library.

[0750] Furthermore, the device is equipped with emotion recognition capabilities, using OpenCV to analyze the user's facial expressions in real time and Librosa to analyze their voice tone. This allows the device to determine the user's emotional state and add personalized elements to the digital video. Personalization based on emotional state further enriches the viewing experience.

[0751] The device uploads the completed digital video to information sharing platforms such as YouTube and Vimeo using APIs. A description generated based on the viewer's emotional state is also automatically added. The published video gathers reactions from other users, and the server generates a popularity ranking based on these reactions.

[0752] This allows the server to improve user engagement and deepen individual viewing experiences. As part of this process, an example of a prompt for the generative AI model would be: "Create a highlight video by extracting particularly exciting moments from the user's emotional data." In this way, it becomes possible to generate and share digital images that reflect the user's emotions.

[0753] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0754] Step 1:

[0755] The user activates a dedicated terminal and begins streaming the match. The terminal receives match image data in real time from the server using the HLS or DASH protocol. The input is the streaming data from the server, and the output is the visual information displayed on the terminal's screen. Specifically, the terminal decodes the received data and buffers it for smooth playback.

[0756] Step 2:

[0757] During viewing, the user selects scenes of interest via the interface. The device saves this selection information, including timestamps. The input is the timestamps of the scenes selected by the user, and the output is a list of scenes stored in an internal database. Specifically, marking the selected points creates data for later scene editing.

[0758] Step 3:

[0759] The device uses its built-in camera and microphone to recognize the user's emotional state. It analyzes facial expressions using OpenCV and voice tone using Librosa. The input is real-time audio and video data of the user, and the output is information on the analyzed emotional state. Specifically, it extracts facial feature points to determine emotions in real time and measures the intensity of emotions by analyzing the pitch and intensity of the voice.

[0760] Step 4:

[0761] The device creates digital highlights using a saved list of scenes and emotional information. It edits selected scenes using the FFmpeg library, incorporating personalized content based on emotional data. The input is a list of selected scenes and the user's emotional information, and the output is a visually organized digital video. Specifically, it crops footage to highlight peak emotional scenes and rearranges it according to a template.

[0762] Step 5:

[0763] The terminal uploads the completed digital video to an information sharing platform via the internet. Using an API, it automatically generates titles and descriptions and configures publication settings. The input is the completed digital video and generated metadata, and the output is the video content published online. Specifically, it establishes a connection to a video sharing service and uploads all necessary files.

[0764] Step 6:

[0765] The server collects reactions from other users to uploaded videos. It then analyzes this data to generate a popularity ranking. The input is reaction data from other users, and the output is a popularity ranking that updates in real time. Specifically, it aggregates viewing time and evaluation comments, and calculates the overall system ranking based on evaluation points.

[0766] Step 7:

[0767] The server comprehensively analyzes the collected sentiment data and reactions to create a report on viewing trends. This information is used for developing marketing strategies, among other things. The input is the sentiment data and reaction statistics of each user, and the output is a report as a result of the analysis. Specifically, the process involves visualizing trends using data mining techniques and generating a report as the output.

[0768] (Application Example 2)

[0769] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0770] In modern digital entertainment, personalizing content for users is crucial, but many challenges remain, particularly in individualizing the user experience in live video. Traditional methods require users to manually select video highlights, and there is a lack of effective means to leverage emotional data to improve user engagement. Furthermore, systems that generate rankings based on the popularity of video highlights to promote competition among users are also lacking.

[0771] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0772] In this invention, the server includes data processing means for automatically detecting scenes that excited the user while they were watching video content, data processing means for generating personalized video highlights based on the detected emotion data, and transmission means for transmitting the generated video highlights to an information transmission medium. This enables real-time video highlight generation utilizing the user's emotion data and the creation of rankings to enhance user engagement.

[0773] A "user" is a viewer who shows excitement or interest after watching video content.

[0774] "Video content" refers to information in video format that is delivered to users through their sight and hearing.

[0775] An "exciting scene" refers to a portion of the video in which the user's emotional data specifically detects a state of excitement.

[0776] "Data processing means" refers to a device or program that has the function of analyzing acquired emotional information and generating target data based on a specific algorithm.

[0777] "Personalized video highlights" are video digests edited to match each user's individual interests and preferences based on their emotional data.

[0778] An "information transmission medium" refers to an online platform or communication method used to show generated video highlights to other users.

[0779] "Transfer means" refers to the technology or device used to transmit generated data to other terminals or platforms.

[0780] A "ranking" is information that shows evaluations and rankings generated based on user activity and responses.

[0781] The system for implementing this invention consists of a server that provides video content, a terminal that detects the user's emotions, and various software that handles the generated data.

[0782] The server delivers video data to the user's device in real time using a streaming API. The user views the video using a smartphone or other digital device. At this time, the device uses its built-in camera and microphone, along with emotion analysis libraries such as OpenCV and TensorFlow, to analyze the user's facial expressions and voice tone and acquire real-time emotion data.

[0783] After collecting emotional data, the device automatically extracts scenes where the user is deemed excited, using video editing libraries such as FFMpeg. This generates personalized video highlights, which are then transferred to a communication medium.

[0784] The server aggregates and analyzes reactions, using a real-time database such as Firebase to calculate user ratings and rankings. The ranking results are continuously updated, and appropriate compensation is provided based on them. This entire system makes users' sports viewing experiences data-driven and personalized, improving overall engagement.

[0785] As a concrete example, during a soccer match, when your team scores, the device analyzes the user's facial expressions and voice to create a special highlight based on those emotions. This highlight can then be shared with friends. An example of a specific prompt message would be, "While watching a soccer match, would you like to have your facial expressions and voice emotions analyzed during scoring moments to create a special highlight that you can share with your friends?" In this way, the user experience becomes richer.

[0786] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0787] Step 1:

[0788] The server transmits match video data to the user's device in real time using a streaming API. The input is the match video data, and the output is the video played on the user's device. During this process, the server manages network bandwidth to ensure stable data transfer.

[0789] Step 2:

[0790] The device plays back received video data while operating its built-in camera and microphone, and analyzes the user's facial expressions and voice tone using OpenCV and TensorFlow. The input is the user's video and audio data, and the output is emotion parameters. The device processes this data in real time and generates emotion analysis results.

[0791] Step 3:

[0792] The device extracts scenes where an excited state is determined based on the emotion analysis results. By setting a specific threshold for extraction, it selects the corresponding video range when an excitement level above a certain level is detected. The input is emotion parameters, and the output is metadata of the selected scenes.

[0793] Step 4:

[0794] The device uses a video editing library such as FFMpeg to aggregate extracted scenes and generate a personalized highlight video. The input is metadata of the selected scenes, and the output is a highlight video file. The generated video is saved in the user's viewing history.

[0795] Step 5:

[0796] The terminal transfers the generated highlight video to a communication medium and shares it with other users via a user interface. The input is a highlight video file, and the output is publicly available on an online platform. Users can receive comments on this video.

[0797] Step 6:

[0798] The server collects reaction data from other users aggregated across information transmission media and generates ratings and rankings using a real-time database such as Firebase. The input is reaction data, and the output is updated ratings and rankings. The server updates the user's profile based on this.

[0799] Step 7:

[0800] The server provides rewards to users who rank highly based on the ranking results. The input is the updated ranking data, and the output is the rewards and benefits provided. The server automatically handles reward transfers and point awards.

[0801] 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.

[0802] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0803] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0804] 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.

[0805] Figure 9 shows an 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.

[0806] 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.

[0807] 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.

[0808] 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, motorcycles, etc., 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, for example, based 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.

[0809] 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."

[0810] 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.

[0811] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0812] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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 the like 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.

[0821] 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 as being incorporated by reference.

[0822] The following is further disclosed regarding the embodiments described above.

[0823] (Claim 1)

[0824] A terminal for users to watch video footage of the match and select scenes that interest them,

[0825] A processing method for creating video highlights based on selected scenes,

[0826] A means of transmitting the created video highlights to an information sharing medium,

[0827] An interface means for other users to react to publicly shared video highlights,

[0828] A management system that generates user rankings based on the popularity of publicly released video highlights,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, further comprising means for providing rewards to top-ranking users based on the aforementioned ranking.

[0832] (Claim 3)

[0833] The system according to claim 1, further comprising means for distributing interview videos generated by mechanical means in order to provide opinions from players and related personnel after a match.

[0834] "Example 1"

[0835] (Claim 1)

[0836] An information processing device is used to decode video, allowing users to view video footage of a match and select scenes of interest.

[0837] A means of recording a timestamp and related information as metadata for the selected scene,

[0838] A means of creating an edited video by integrating video fragments based on user selection and adding text and audio,

[0839] A means of converting the created edited video and sending it to an appropriate information sharing medium,

[0840] A dialogue device for other users to react to edited videos that have been made public,

[0841] A control means that generates user positioning based on reaction data of the published edited video,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for providing incentives to higher-ranking users based on the aforementioned positioning.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising means for distributing a Q&A video generated using mechanical processing in order to provide the views of participants and related parties after the match.

[0847] "Application Example 1"

[0848] (Claim 1)

[0849] An information processing device for users to view information available during a competition and select scenes that interest them,

[0850] An information processing means that creates a summary of viewable information based on selected scenes,

[0851] A communication device that transmits a summary of the created viewable information to a sharing means,

[0852] A dialogue device for other users to respond to summaries of viewable information shared,

[0853] An information processing means that generates a ranking of users based on the popularity of summaries of publicly available viewable information,

[0854] A communication device for exchanging selected scenes with other users through a sharing mechanism,

[0855] A communication device for distributing conversation content generated by a generative AI model using knowledge base information after a match,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, further comprising processing means for providing rewards to top-ranking users based on the aforementioned ranking.

[0859] (Claim 3)

[0860] The system according to claim 1, further comprising information processing means for generating opinions from players and related parties after a match using a generative AI model.

[0861] "Example 2 of combining an emotion engine"

[0862] (Claim 1)

[0863] A device for users to view image data from a match and select scenes that interest them,

[0864] A processing means for creating a digital image based on data from selected scenes,

[0865] A means of transmitting the created digital video to an information sharing platform,

[0866] A means for other users to react to publicly available digital videos,

[0867] A management system that generates user rankings based on the popularity of publicly available digital videos,

[0868] A means of sentiment analysis to recognize the emotional state of the user,

[0869] A means of personalizing digital images based on emotional state,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, further comprising means for providing rewards to higher-ranking users based on the aforementioned ranking.

[0873] (Claim 3)

[0874] The system according to claim 1, further comprising means for distributing interactive video generated by mechanical means in order to provide opinions from athletes and related parties after a match.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] A data processing method for automatically detecting scenes that excited users while they were watching video content,

[0878] A data processing means that generates personalized video highlights based on detected emotion data,

[0879] A transmission means for transmitting the generated video highlights to an information transmission medium,

[0880] A user interface means for reacting to video highlights posted by other users,

[0881] A management system for creating user ratings based on the acceptance rate of the transmitted video highlights,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, further comprising means for providing compensation to higher-ranking users based on the aforementioned evaluation.

[0885] (Claim 3)

[0886] The system according to claim 1, further comprising means for delivering a rapidly generated video of a conversation in order to provide the opinions of a person who became interested after watching the video content. [Explanation of Symbols]

[0887] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A terminal for users to watch video footage of the match and select scenes that interest them, A processing method for creating video highlights based on selected scenes, A means of transmitting the created video highlights to an information sharing medium, An interface means for other users to react to publicly shared video highlights, A management system that generates user rankings based on the popularity of publicly released video highlights, A system that includes this.

2. The system according to claim 1, further comprising means for providing rewards to top-ranking users based on the aforementioned ranking.

3. The system according to claim 1, further comprising means for distributing interview videos generated by mechanical means in order to provide opinions from players and related personnel after a match.

Images