System

Abstract

A system is provided.SOLUTION: A system includes a means for a user to register a plurality of video distribution channels, a means for automatically detecting a newly posted video, a means for analyzing the content of the newly posted video using a generated artificial intelligence model and extracting an important part, a means for automatically generating a short video based on the extracted important part, and a means for providing the generated short video to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Viewing long video content takes a lot of time, making it difficult for users to quickly find information that interests them from a large number of videos. For this reason, there is a need for a method to efficiently grasp the overview of videos and select videos worth watching. In addition, because short videos alone do not provide deep learning, a service that allows users to efficiently use long videos is needed. [Means for solving the problem]

[0005] The present invention provides a system including a means for users to register for multiple video distribution channels, a means for automatically detecting newly posted videos, a means for analyzing the content of the new videos using a generative artificial intelligence model and extracting key parts, a means for automatically generating short videos based on the extracted key parts, and a means for providing the generated short videos to users. This allows users to quickly grasp the overview of videos and efficiently acquire information by watching only videos that interest them. The system also includes a means for periodically detecting new videos and generating and storing metadata about the generated short videos, thereby ensuring that users can consistently access the latest content.

[0006] "User" means an individual or organization that uses the system.

[0007] A "video distribution channel" is an online platform or service that provides video content.

[0008] "Newly posted videos" are video contents that have been newly uploaded to a video distribution channel.

[0009] A "generative artificial intelligence model" is a machine learning algorithm or system that analyzes data and generates information based on the results.

[0010] A "short video" is a short video content created by summarizing the content of a full-length video and extracting the important points.

[0011] "Metadata" is data that describes information about data, and in this case refers to additional information about the generated short video (such as the original video URL, title, and generation date and time). [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] The system of the present invention allows users to efficiently monitor multiple video distribution channels and quickly identify content they are interested in. This system is primarily composed of a user terminal, a server, and a generative AI model.

[0034] System Overview

[0035] The basic operation of the system is that users subscribe to video distribution channels, and when new videos are uploaded, the system automatically detects them, analyzes the important parts, and generates short videos. This allows users to quickly get an overview of the video through the short video, and if they are interested, they can watch the original full-length video.

[0036] User terminal operation

[0037] 1. User registration and login:

[0038] A user installs an application and creates a user account.

[0039] You can log in by entering your authentication information on the login screen.

[0040] 2. Video streaming channel registration:

[0041] After logging in, users enter the URL and name of the video distribution channel they wish to register and register it in the system.

[0042] 3. View short videos:

[0043] Receive notifications of short videos generated by the system.

[0044] The user views the short video and gets an overview of it.

[0045] 4. Watching full-length videos:

[0046] Click on the link for the short video that interests you to go to the original full-length video page to watch it.

[0047] Server Operation

[0048] 1. User and channel information management:

[0049] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database.

[0050] 2. New video detection:

[0051] It periodically checks subscribed channels to detect newly uploaded videos, and does this using external APIs such as the YouTube® API.

[0052] 3. Download and analyze the video:

[0053] The detected new videos are downloaded to the server and their content is analyzed using a generative AI model.

[0054] 4. Short video generation:

[0055] Based on the key parts captured by the generative AI model, a short video is automatically generated, which includes elements such as a thumbnail, text, and narration.

[0056] 5. Metadata management for short videos:

[0057] Metadata for the generated short videos is generated and stored in a database.

[0058] 6. Short Video Notifications:

[0059] The user is notified that a short video has been generated and is made available for viewing.

[0060] Specific examples

[0061] For example, if user A is interested in cooking, he / she registers multiple cooking channels in the system. The server periodically checks these channels and detects when new cooking videos have been uploaded. Suppose the new video is "Easy dinner recipes you can make in 30 minutes."

[0062] The server downloads the video and analyzes it using a generative AI model, which extracts the key cooking steps and generates a short video of about five minutes, complete with thumbnails of the finished dish and a brief explanation of each step.

[0063] User A views this short video, quickly grasps the content, and if interested, watches the original 30-minute video. This process allows User A to efficiently select content that interests them and make effective use of their time.

[0064] Based on the above details, the system of the present invention provides an innovative means for users to quickly and effectively grasp video content that interests them and efficiently obtain the information they need.

[0065] The processing flow will be explained below.

[0066] Step 1: User logs in

[0067] The user starts the application on the device and enters authentication information (user name, password, etc.) on the login screen. When the user clicks the "Login" button, the device sends the entered authentication information to the server. The server verifies the authentication information in the database, and if authentication is successful, issues a session ID and returns it to the device.

[0068] Step 2: User subscribes to a video streaming channel

[0069] The user opens the channel subscription UI within the application and enters the URL and name of the video streaming channel they want to subscribe to. When the user clicks the "Subscribe" button, the device sends the entered channel information to the server. The server stores the received channel information in a database and notifies the user that the subscription was successful.

[0070] Step 3: Server detects new video

[0071] The server periodically (for example, every hour) retrieves a list of registered video distribution channels. The server uses the YouTube API to check for new videos on each channel. If a new video is found, it stores the video information (title, URL, posting date, etc.) in the database.

[0072] Step 4: The server downloads the new video

[0073] The server downloads the video data from the new video URL and stores the downloaded video data in a temporary location.

[0074] Step 5: The server analyzes the video using a generative AI model

[0075] The server loads the downloaded video data into the generative AI model as input, which then analyzes the content of the video and extracts important parts (keyframes, text information, audio information, etc.).

[0076] Step 6: The server generates the short video

[0077] The server automatically generates a short video based on the extracted key parts, adding a thumbnail, a text description, and optionally a narration to the short video, and stores the generated short video on the server.

[0078] Step 7: The server generates metadata for the short video

[0079] The server generates metadata about the short video (such as the original video URL, title, and creation date and time) and stores it in a database.

[0080] Step 8: The server notifies the user of the generated short video

[0081] The server notifies the corresponding user that a new short video has been generated, and the terminal receives the notification and displays to the user that a short video is available.

[0082] Step 9: User Views Short Video

[0083] The user clicks on the notification on their device to open the short video playback screen. The device streams or downloads the short video data from the server and plays it. The user watches the short video and gets an overview.

[0084] Step 10: User watches a full-length video

[0085] If a user is interested in the summary of a short video after watching it, they can click on the link to the original full-length video. The device will open the original full-length video page on YouTube via a browser or other device, and the user can watch the full-length video.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] Conventional methods for viewing video content have made it difficult for users to efficiently identify videos that interest them. In particular, there has been a lack of a way to quickly review only the important parts of a large number of video distribution channels or long video content. This has resulted in the problem of requiring users to spend a huge amount of time. There has also been a demand for a method for quickly identifying the content of newly posted videos.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes means for users to register multiple media distribution channels, means for automatically detecting newly posted video data, means for analyzing the content of the new video data using the generated artificial intelligence model and extracting important parts, means for automatically generating short video data based on the extracted important parts, means for generating and saving metadata related to the generated short video data, means for sending a notification of the generated short video data to a user terminal using a push notification system, and means for providing the generated short video data to the user. This allows users to efficiently understand content that interests them and quickly check important parts, significantly reducing the time required for video viewing.

[0091] "User" refers to a person who uses the system, subscribes to a video distribution channel, and views the generated short videos.

[0092] A "media distribution channel" is an online platform that provides video content, such as YouTube or Vimeo.

[0093] "Video Data" means video content held in electronic format, including any combination of still images, audio, and text.

[0094] A "server" is a computer system that receives requests from users and detects, analyzes, stores, and provides video data.

[0095] A "generated artificial intelligence model" is a model that uses a pre-trained machine learning algorithm and is used to analyze video content and extract important parts.

[0096] "Important portion extraction" is the process of identifying and extracting portions of video data that are likely to be of particular interest to the user.

[0097] "Short video data" refers to a short edited video generated based on the important parts, and is created to provide an overview to the user.

[0098] "Metadata" refers to information related to short video data, including title, summary, thumbnail, timestamp, and the like.

[0099] A "push notification system" is a technology or service that allows a server to send notifications to a user terminal in real time.

[0100] The system of the present invention is designed to enable users to efficiently monitor multiple media distribution channels and quickly identify content they are interested in. The system is comprised of a user terminal, a server, and a generative AI model.

[0101] First, a user installs the application on their device and creates a user account. To create an account, they must enter information such as a username, password, and email address. The user then logs in using these credentials. After logging in, the user registers with the system by entering the URL and name of a media distribution channel. For example, this could be a channel like YouTube or Vimeo.

[0102] The server uses a database such as MySQL (registered trademark) or PostgreSQL to store and manage user authentication information and information about registered media distribution channels. The server periodically checks whether new videos have been uploaded to registered channels using the YouTube API or Vimeo API. When a new video is detected, it obtains the video's metadata (title, upload date and time, URL, etc.) and downloads it to the server using YouTube-dl or FFmpeg.

[0103] The server inputs the downloaded video data into a generative AI model (e.g., GPT-3 (registered trademark), BERT). It sends the following prompt to the generative AI model:

[0104] Analyze a video for "Easy Dinner Recipes that Can Be Made in 30 Minutes" and extract the key cooking steps. Create a short video of approximately 5 minutes based on the extracted steps. Use an image of the finished dish as the thumbnail and add a brief description of each step.

[0105] The generative AI model follows these prompts to analyze the video content and extract key parts. Once the key parts are extracted, a short video is generated using software such as OpenCV and MoviePy. The short video may include a thumbnail, subtitles, and narration.

[0106] The metadata of the generated short video data (title, summary, thumbnail, etc.) is saved back to the database. The server uses Firebase Cloud Messaging (FCM) to send a notification of the generated short video data to the user's device. The notification is displayed on the user's device, and a list of new short videos is displayed in the "Notifications" tab in the app. The user can click the notification to play the short video in the app's video player (e.g., ExoPlayer).

[0107] As a concrete example, consider the case where User B is interested in the latest technology news. User B registers multiple technology media distribution channels in the system. The server periodically checks these channels and discovers that a new video has been uploaded with the title "Explaining the latest advances in AI technology." The server downloads this video and analyzes it with a generative AI model. The generative AI model extracts the "explanatory parts about important technologies" and edits them into a short video of about 5 minutes. This short video includes subtitles that provide an overview of the technology and relevant screenshots.

[0108] A notification is sent to User B's device and displayed as a new short video within the app. User B plays the short video and gets a quick overview of the progress of AI technology. Intrigued, User B clicks on the link to the original full-length video to watch in more detail.

[0109] This process allows users to efficiently select content that interests them and make effective use of their time. The system of the present invention provides an innovative means for users to quickly and effectively find video content that interests them and efficiently obtain the information they need.

[0110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0111] Step 1: User registration and login

[0112] Specific behavior:

[0113] Input: A user installs an application on their device and creates an account by entering a username, password, and email address.

[0114] Data processing / calculation: The server receives the entered authentication information and stores it in a database such as MySQL or PostgreSQL. When logging in, the entered authentication information is compared with the information in the database.

[0115] Output: Once the correct credentials are entered, the user can log into their account. After logging in, the user's dashboard will be displayed.

[0116] Step 2: Register your video distribution channel

[0117] Specific behavior:

[0118] Input: After logging in, the user enters the URL and name of the video distribution channel and clicks the "Register" button.

[0119] Data processing / calculation: The server receives the transmitted channel information and stores it in a database.

[0120] Output: The registered channel information is saved in the database and will be checked from next time onwards.

[0121] Step 3: Detect and download new videos

[0122] Specific behavior:

[0123] Input: The server periodically checks for new videos using the YouTube API and / or Vimeo API, based on the channel information stored in the database.

[0124] Data processing / calculation: When a new video is detected, the video's metadata (title, upload date and time, URL, etc.) is obtained, and the video data is downloaded to the server using YouTube-dl or FFmpeg.

[0125] Output: The downloaded video data is saved on the server.

[0126] Step 4: Analyze the video and generate a short video

[0127] Specific behavior:

[0128] Input: Video data stored on the server is fed into a generative AI model (e.g., GPT-3, BERT) using a prompt like this:

[0129] Analyze a video for "Easy Dinner Recipes that Can Be Made in 30 Minutes" and extract the key cooking steps. Create a short video of approximately 5 minutes based on the extracted steps. Use an image of the finished dish as the thumbnail and add a brief description of each step.

[0130] Data processing / computation: The generative AI model analyzes the video data and extracts key parts. Based on the results, short video data is generated using OpenCV and MoviePy. The short video may include subtitles, thumbnails, and narration.

[0131] Output: The generated short video data is saved on the server.

[0132] Step 5: Notification and viewing of short videos

[0133] Specific behavior:

[0134] Input: The metadata of the generated short video data (title, summary, thumbnail, etc.) is stored in a database.

[0135] Data processing / calculation: The server uses Firebase Cloud Messaging (FCM) to send notifications of short video data to the user's device.

[0136] Output: A notification will be displayed on the user's device, and a list of new short videos will be displayed in the "Notifications" tab within the app.

[0137] Step 6: Watch a full-length video

[0138] Specific behavior:

[0139] Input: When the user clicks on the notification, a short video will play in the app's in-app video player (e.g. ExoPlayer).

[0140] Data processing / computation: While playing the short video, a link to the original full-length video is provided.

[0141] Output: If the user is interested, they can click on the link to the original full-length video, which will automatically open a browser and allow them to watch the full-length video.

[0142] The above are the specific processing steps of this system. The operations performed at each step and the accompanying data flow are explained in detail.

[0143] (Application example 1)

[0144] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0145] Conventional video streaming platforms make it difficult for users to efficiently grasp the content that interests them. In particular, there are limited ways to quickly understand the content of long videos before watching them. Furthermore, there is insufficient consideration given to optimizing the viewing experience on smart devices and wearable devices. This makes it difficult for users to make effective use of their time, and there is a demand for a way to quickly obtain the information they need.

[0146] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0147] In this invention, the server includes a means for allowing a user to register multiple video distribution channels, a means for automatically detecting newly posted videos, a means for analyzing the content of the new videos using a generative AI model and extracting important parts, a means for automatically generating short videos based on the extracted important parts, and a means for providing the generated short videos to the user via a smart device, particularly a mobile terminal or a wearable device, thereby enabling the user to quickly understand the content of content that interests them and to watch full-length videos as needed.

[0148] A "user" is an individual or a corporation that uses the system of the present invention to subscribe to a video distribution channel and watch short videos to understand the content of the videos.

[0149] "Video distribution channel" refers to a website or service that provides video content on the Internet and to which users can register content that interests them.

[0150] A "new video" is a video that has been newly uploaded to a video distribution channel and is detected by the system of the present invention.

[0151] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to analyze the content of a video and extract important parts.

[0152] A "mobile terminal" is a portable electronic device such as a smartphone or tablet, which is a device that a user uses to watch short videos.

[0153] A "wearable device" is an electronic device that is worn on the body, such as smart glasses or a smart watch, and is used by users to watch short videos.

[0154] "Short videos" are short videos that are automatically generated by extracting only the important parts of new videos analyzed using a generative AI model.

[0155] The "server" is a central system that manages information about video distribution channels registered by users, detects and analyzes new videos, and generates and provides short videos.

[0156] The system of the present invention allows users to efficiently find new videos from video distribution channels. This system is mainly composed of a server, a user terminal, and a generative AI model.

[0157] The server includes means for users to register multiple video distribution channels, means for automatically detecting newly posted videos, means for analyzing the content of new videos using a generative AI model and extracting important parts, means for automatically generating short videos based on the extracted important parts, and means for providing the generated short videos to users via smart devices, particularly mobile terminals and wearable devices.

[0158] System configuration

[0159] User terminal operation

[0160] 1. User registration and login:

[0161] Users install the application on their smart device (smartphone, tablet, etc.) and create an account. They can then log in by entering their authentication information on the login screen.

[0162] 2. Video streaming channel registration:

[0163] After logging in, users enter the URL and name of the video streaming channel they wish to subscribe to and register through the application.

[0164] 3. View short videos:

[0165] The server notifies the user of the short video, which the user can then view on a smart device or wearable device (such as smart glasses or a smartwatch) by tapping the notification.

[0166] 4. Watching full-length videos:

[0167] Tap the link of the short video that interests you to go to the original full-length video page to watch it.

[0168] Server Operation

[0169] 1. User and channel information management:

[0170] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database (MongoDB).

[0171] 2. New video detection:

[0172] Registered channels are checked periodically and newly uploaded videos are detected using external APIs such as the YouTube Data API.

[0173] 3. Download and analyze the video:

[0174] The detected new videos are downloaded to the server and the video content is analyzed using a generative AI model (OpenAI® GPT).

[0175] 4. Short video generation:

[0176] Based on the key parts captured by the generative AI model, a short video is automatically generated, which includes a summary, thumbnail, and text information.

[0177] 5. Notification and provision of short videos:

[0178] Using Firebase Cloud Messaging (FCM), the user is notified that a short video has been created and can watch it.

[0179] Specific examples

[0180] For example, if a user subscribes to cooking channels, the server periodically checks these channels to detect when new cooking videos have been uploaded. Suppose the new video is an "easy dinner recipe that can be made in 30 minutes." The server downloads this video and analyzes it using a generative AI model. As a result, the important cooking steps are extracted and generated into a short video of about 5 minutes. This short video includes a thumbnail of the finished dish and a brief explanation of each step.

[0181] Examples of prompts include:

[0182] Create a short video by extracting only the most important parts from the following videos:

[0183] 1. Intro

[0184] 2. Ingredient List

[0185] 3.Cooking Steps (Step by Step)

[0186] 4. Final Dish Presentation

[0187] This allows users to view short videos, quickly grasp the content, and then watch the full 30-minute video if they are interested. This process allows users to efficiently select content that interests them and make effective use of their time.

[0188] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0189] Step 1:

[0190] The server manages user registration and authentication information. A user creates an account through a smart device and enters their email address and password. The entered data is stored in the server's MongoDB database. The output is a user ID and an authentication token.

[0191] Step 2:

[0192] After logging in, a user registers multiple video distribution channels. The user inputs the channel URL and name from their device and sends this to the server. The server stores this information in a database. The input is the channel URL and name, and the output is updating the database.

[0193] Step 3:

[0194] The server periodically checks all registered channels to see if new videos have been uploaded. This check is performed using the YouTube Data API. The input is channel information, and the output is metadata for new videos.

[0195] Step 4:

[0196] When a new video is detected, the server downloads it and stores it in the server's local storage using the video's URL. The input is the URL of the new video, and the output is the video file.

[0197] Step 5:

[0198] The server uses a generative AI model to analyze the content of the downloaded video. As a specific example, it generates a prompt sentence and inputs it into the generative AI model. The generative AI model extracts important parts and returns the text analysis results. The input is the video content and the prompt sentence, and the output is the analysis results.

[0199] Step 6:

[0200] The server automatically generates a short video based on the analysis results. Using video editing software, the extracted important parts are cut out and edited. The input is the analysis results and the original video file, and the output is the short video file.

[0201] Step 7:

[0202] The server generates metadata about the generated short video and stores it in a database. The metadata includes the video title, summary, length, URL, etc. The input is the short video file, and the output is the metadata and its storage.

[0203] Step 8:

[0204] The server uses Firebase Cloud Messaging (FCM) to notify the user that the generated short video is available for viewing. The notification includes the title and link of the short video. The input is notification information, and the output is a notification to the user device.

[0205] Step 9:

[0206] Users receive a notification on their smart device or wearable device and watch a short video. They can access the original full-length video by tapping the link in the short video they watched. The input is the notification message, and the output is the viewing of the short video and access to the original full-length video.

[0207] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0208] The system of the present invention efficiently manages video distribution channels and quickly identifies content that users are interested in. Furthermore, by combining it with an emotion engine that recognizes user emotions, it realizes more personalized video delivery.

[0209] System Overview

[0210] The system allows users to register multiple video distribution channels, automatically detects newly posted videos, and analyzes their content to generate short videos. This allows users to quickly get an overview of the video and watch only those videos that interest them. Furthermore, by incorporating an emotion engine, the system can recognize users' emotions in real time and adjust and recommend video content accordingly.

[0211] User terminal operation

[0212] 1. User registration and login:

[0213] A user installs the application, creates an account, and then enters their authentication information on the login screen to log in.

[0214] 2. Video streaming channel registration:

[0215] After logging in, users enter the URL and name of their video distribution channel and register it in the system.

[0216] 3. View short videos:

[0217] Receive notifications of short videos generated by the system and view the short videos.

[0218] 4. Watching full-length videos:

[0219] If you are interested in the short video, click on the link to the original full-length video and watch it.

[0220] Server Operation

[0221] 1. User and channel information management:

[0222] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database.

[0223] 2. New video detection:

[0224] It periodically checks subscribed channels to detect newly uploaded videos. It uses the YouTube API to check for new videos and stores that information in a database.

[0225] 3. Download and analyze the video:

[0226] New videos are downloaded to the server and analyzed using the generative AI model.

[0227] 4. Short video generation:

[0228] A generative AI model analyzes the content of a video, extracts key parts, and automatically generates a short video that includes a thumbnail, text, and narration.

[0229] 5. Metadata management for short videos:

[0230] The metadata of the generated short videos is stored and managed in a database.

[0231] 6. Short Video Notifications:

[0232] The user is notified that a short video has been generated and is made available for viewing.

[0233] Emotion Engine Operation

[0234] 1. User Emotion Recognition:

[0235] While the user is watching a short video, the emotion engine uses cameras and sensors to analyze the user's facial expressions and voice, collecting emotional data in real time.

[0236] 2. Sentiment Data Analysis:

[0237] The server analyzes the collected emotional data to understand the user's emotions while watching, thereby evaluating the user's interest and satisfaction.

[0238] 3. Adjustment of short video content:

[0239] Based on the analysis results of the emotion engine, the server adjusts the content of the short video in real time, specifically by emphasizing elements that attract the user's interest and omitting unnecessary parts.

[0240] 4. Personalized Recommendations:

[0241] Based on past emotional data, the server recommends the most suitable short videos for the user. It analyzes the user's emotional history and prioritizes videos with similar emotional patterns.

[0242] Specific examples

[0243] For example, if user B is interested in music, he / she can register multiple music channels in the system. The server periodically checks these channels and detects when new music videos are uploaded. In this case, the new videos are called "latest hit songs."

[0244] The server downloads the new video and analyzes it with a generative AI model, which extracts key parts of the song and generates a short video of about three minutes, including a thumbnail of the song and a description of the key lyrics.

[0245] User B watches the short video, and the emotion engine analyzes his facial expressions and reactions. If it detects that User B is particularly enjoying the song, other videos related to the song will be recommended based on the emotion engine's analysis. As a result, User B can consistently access content that interests him, improving his viewing experience.

[0246] Based on the above details, the system of the present invention takes into account the user's emotions and provides video content that meets individual needs, thereby realizing an efficient and effective video viewing experience.

[0247] The processing flow will be explained below.

[0248] Step 1: User installs the application on their local device and creates an account

[0249] The user downloads and installs the application on their device. After installation, they enter the required information (name, email address, password, etc.) on the account creation screen to create a new account. When they click the "Register" button, the device sends the entered information to the server, which then saves the account information in a database.

[0250] Step 2: User logs in

[0251] The user starts the application and enters authentication information (email address, password) on the login screen. When the "Login" button is clicked, the device sends the entered authentication information to the server. The server collates the information with the authentication information in the database, and if authentication is successful, returns a session ID to the device.

[0252] Step 3: User subscribes to a video streaming channel

[0253] After logging in, the user opens the "Subscribe to Channel" section in the application menu, enters the URL or name of the video streaming channel they want to subscribe to, and clicks the "Subscribe" button. The device sends the entered channel information to the server, which then stores it in a database.

[0254] Step 4: The server periodically detects new videos

[0255] The server periodically (for example, every hour) retrieves a list of registered video distribution channels from the database. It checks each channel using the YouTube API or similar to see if new videos have been posted. If a new video is found, it saves the video information (title, URL, posting date, etc.) in the database and proceeds to the next step.

[0256] Step 5: The server downloads the new video

[0257] The server downloads the video data using the URL of the new video, and then stores it in a temporary location.

[0258] Step 6: The server analyzes the video using the generated AI model

[0259] The server inputs the temporarily stored video data into the generative AI model, which then analyzes the video content and extracts important parts (keyframes, text, audio information, etc.).

[0260] Step 7: The server generates the short video

[0261] The server uses the extracted key parts to automatically generate a short video, which includes a thumbnail, a text description, and optionally a narration, and stores the generated short video on the server.

[0262] Step 8: Recognize the user's emotions

[0263] While a user is viewing a short video, their facial expressions and voice are captured through the device's camera and microphone. The emotion engine analyzes this data in real time to determine the user's emotional state (e.g., joy, interest, indifference, etc.).

[0264] Step 9: The server collects the emotion data and adjusts the content of the short video.

[0265] The device sends the collected emotional data to a server, which then analyzes the data and adjusts the content of the short video in real time based on the user's reaction. For example, it may expand the parts that the user found interesting and omit the parts that the user did not find interesting.

[0266] Step 10: The server manages the metadata for the short video

[0267] The server generates metadata including the generated short video and adjustment information based on the emotion data, and stores the metadata in a database. This metadata includes the original video URL, title, creation date and time, emotional state, etc.

[0268] Step 11: The server notifies the user of the short video

[0269] The server notifies the user that a new short video has been generated. The device receives the notification and displays to the user that a short video is available.

[0270] Step 12: User views the short video and, if desired, watches the full-length video

[0271] The user clicks on the notification on their device to open the short video playback screen. The device streams or downloads the short video data from the server and plays it. The user watches the short video and gets an overview of it. If they are interested, they click on the link to the original full-length video and watch it on YouTube.

[0272] This series of processes allows users to efficiently select content that interests them and improve their viewing experience.By introducing an emotion engine, the system can provide optimal content according to the user's emotions.

[0273] Example 2

[0274] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0275] Conventional video viewing systems have difficulty efficiently discovering content that interests users and analyzing emotions toward that content to provide personalized recommendations. Furthermore, viewing a full-length video in its entirety takes a long time, and there is a high risk of viewing content that does not match the user's interests. There is a need to address these issues.

[0276] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0277] In this invention, the server includes means for users to register multiple media, means for automatically detecting newly posted data, means for analyzing the content of the new data using the generated artificial intelligence model and extracting important parts, means for automatically generating short data based on the extracted important parts, means for providing the generated short data to the user, means for collecting and analyzing user emotion data, means for adjusting the content of the short data based on the emotion data, and means for making personalized recommendations. This allows users to efficiently discover content that interests them and receive personalized recommendations, thereby improving their viewing experience.

[0278] "User" refers to a person who uses the system to register, view, and rate video content.

[0279] "Media" refers to video distribution channels and content sources.

[0280] "Data" refers to the information that the system processes and analyzes, such as video, metadata, and emotional data.

[0281] "Artificial intelligence model" refers to machine learning algorithms and deep learning models used to analyze and generate content and recognize emotions.

[0282] "Short data" refers to a short video clip created by extracting only the important parts of the generated video.

[0283] "Emotion data" refers to information about emotions acquired by analyzing the user's facial expressions and voice.

[0284] "Personalized recommendations" refers to content that is individually recommended to users based on their interests, past viewing history, and emotional data.

[0285] "Metadata" refers to additional information such as title, summary, and playback time related to the generated short data.

[0286] The system of the present invention allows users to efficiently manage video content and quickly find content they are interested in. The system is implemented as follows.

[0287] User terminal operation

[0288] The user terminal functions as an interface for users to access the system. Users begin using the system by installing the application and creating an account. After logging in, users register video distribution channels that interest them. When a new video is posted to a registered channel, the system generates a short video and notifies the user. Users can watch the short video and, if interested, access the original full-length video.

[0289] Server Operation

[0290] The server is the central part of the system and plays the following roles:

[0291] 1. User and channel information management:

[0292] The server stores and manages the authentication information entered by the user and the registered channel information in a database, using a common database management system (e.g., MySQL).

[0293] 2. New video detection:

[0294] The server periodically checks subscribed channels (e.g., using a scheduler cron job) and uses an API (e.g., YouTube API) to detect newly uploaded videos.

[0295] 3. Download and analyze the video:

[0296] When a new video is detected, the server downloads the video, stores it locally using a Python library (e.g., pytube), and then analyzes the video using the generated AI model.

[0297] 4. Short video generation:

[0298] The generative AI model analyzes the video and extracts key parts. Specifically, it uses OpenCV to analyze the video frames, and then uses a natural language processing library (e.g., NLTK) to generate narration and text, automatically generating a short video.

[0299] 5. Metadata management for short videos:

[0300] Metadata about the generated short videos (e.g., title, summary, and playback time) is stored and managed in a database.

[0301] 6. Short Video Notifications:

[0302] The server uses a push notification service (e.g., Firebase Cloud Messaging) to notify the user that a short video has been generated.

[0303] Emotion Engine Operation

[0304] The emotion engine plays an important role in enhancing the user's viewing experience.

[0305] 1. User Emotion Recognition:

[0306] While the user is watching a short video, facial expression and voice data is collected using the device's camera and microphone, using OpenCV and the Emotion API.

[0307] 2. Sentiment Data Analysis:

[0308] The server analyzes the collected emotion data in real time, using an emotion analysis library (e.g., DeepFace) to identify emotions from the user's facial expressions and stores the results in a database.

[0309] 3. Adjustment of short video content:

[0310] Based on the emotion data, the server sends prompts to the generative AI model to adjust the content of the short video in real time, such as "include more frames in which the user is smiling."

[0311] 4. Personalized Recommendations:

[0312] The server recommends the most suitable short videos to users based on their past emotional data. It uses a machine learning model using Python libraries (e.g., scikit-learn) to analyze users' emotional patterns and provide the most suitable content.

[0313] Specific examples

[0314] For example, if user B is interested in music, he / she registers multiple music channels in the system. The server periodically checks these channels and detects when new music videos are uploaded. In this case, let's say the new videos are "latest hit songs."

[0315] The server downloads this new video and runs it through a generative AI model, which extracts key parts of the song and generates a short video of about three minutes, including a thumbnail of the song and a description of the key lyrics.

[0316] User B watches the short video while the emotion engine analyzes his facial expressions and reactions. If it detects that User B is particularly enjoying the song, other videos related to the song will be recommended based on the emotion engine's analysis. As a result, User B can consistently access content that interests them, improving their viewing experience.

[0317] Prompt Sentence Examples

[0318] "Extract the key parts of the music video 'The Latest Hit Song' and edit them into a short video of approximately 3 minutes. Please include a thumbnail and explanations of the key lyrics."

[0319] This concludes the description of the embodiment of the invention. The system efficiently and effectively manages video content while incorporating user emotional data, making it possible to provide a viewing experience tailored to individual needs.

[0320] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0321] Step 1:

[0322] User Registration and Login

[0323] Input: The user enters information such as a name, email address, or password.

[0324] Processing: Information entered from the user's terminal is sent to the server, which then stores the user information in a database.

[0325] Output: The user account is created and you can log in.

[0326] Specific operation: The user installs the application and presses the "New Registration" button. They enter the required information and click the "Register" button. The server receives the user information and saves it in the database. The user enters their email address and password on the login screen and presses the "Login" button to complete the login.

[0327] Step 2:

[0328] Video streaming channel registration

[0329] Input: The user enters the URL or name of a video streaming channel.

[0330] Processing: The channel information entered from the user terminal is sent to the server, which then stores the channel information in a database.

[0331] Output: Video distribution channels are registered and managed in the system.

[0332] Specific operation: After logging in, the user goes to the "Subscribe to Channel" screen, enters the URL and name of the video distribution channel, and presses the "Subscribe" button. The server receives the channel information and stores it in the database.

[0333] Step 3:

[0334] New video detection

[0335] Input: Information about the registered video streaming channel.

[0336] Processing: The server periodically checks the channel (e.g., using a scheduler cron job) and uses the YouTube API to detect new videos.

[0337] Output: The information of the newly uploaded video is saved in the database.

[0338] Specific operation: The server periodically sends an API request based on the channel information to check for new videos. If a new video is found, the video information is saved in the database.

[0339] Step 4:

[0340] Downloading and analyzing videos

[0341] Input: The URL of the new video.

[0342] Processing: The server downloads the video, analyzes it with the generative AI model, and saves the video locally using a Python library (e.g., pytube).

[0343] Output: Analysis results from the generative AI model.

[0344] Specific operation: The server obtains the URL of the new video and starts downloading it. After the download is complete, the video is input into the generative AI model for analysis.

[0345] Step 5:

[0346] Short video generation

[0347] Input: Analysis results of the generative AI model (important part).

[0348] Processing: The server generates a short video based on key parts obtained from the generative AI model, performs frame analysis using OpenCV, and generates narration and text using a natural language processing library (e.g., NLTK).

[0349] Output: Short video.

[0350] How it works: Based on the analysis results obtained from the generative AI model, the server automatically generates a short video, adding thumbnails, text, and narration as needed.

[0351] Step 6:

[0352] Metadata management for short videos

[0353] Input: Metadata of the generated short video (e.g. title, summary, duration).

[0354] Processing: The server stores the metadata in a database.

[0355] Output: The saved metadata.

[0356] Specific operation: The server obtains the metadata of the generated short video and stores it in a database.

[0357] Step 7:

[0358] Short video notifications

[0359] Input: The generated short video.

[0360] Processing: The server uses a push notification service to send a notification of the short video to the user's device, such as using Firebase Cloud Messaging.

[0361] Output: Notification to the user.

[0362] Specific operation: The server sends a notification via the notification service to inform the user that a short video has been created. A notification icon will appear on the user's device, and the user can click it to watch the short video.

[0363] Step 8:

[0364] User Emotion Recognition

[0365] Input: Facial expressions and voice while the user watches a short video.

[0366] Processing: The user device collects emotion data using a camera and microphone and sends it to the server using OpenCV and the Emotion API.

[0367] Output: Collected emotion data.

[0368] How it works: While a user is watching a short video, the device's camera and microphone collect facial and voice data, which is then sent to a server in real time.

[0369] Step 9:

[0370] Sentiment Data Analysis

[0371] Input: Collected emotion data.

[0372] Processing: The server uses an emotion analysis library (e.g. DeepFace) to identify emotions from the user's facial expressions and voice.

[0373] Output: Analysis results (user sentiment).

[0374] Specific operation: The server analyzes the received emotion data and identifies the emotion the user is feeling while watching the video. The analysis results are stored in a database.

[0375] Step 10:

[0376] Adjustment of short video content

[0377] Input: Analysis results (user sentiment).

[0378] Processing: The server sends prompts to the generative AI model to adjust the content of the short video.

[0379] Output: Adjusted short video.

[0380] How it works: The server generates prompts and sends them to the generative AI model. For example, by issuing instructions such as "include as many frames as possible in which the user is smiling," the model generates short videos tailored to the user's interests.

[0381] Step 11:

[0382] Personalized Recommendations

[0383] Input: Analysis results (user's emotion history).

[0384] Processing: The server uses a machine learning model (e.g., scikit-learn) to recommend the best short videos for the user.

[0385] Output: Recommended short videos.

[0386] How it works: The server analyzes the user's emotional patterns and recommends the most suitable videos based on past emotional data, allowing the system to continue providing users with content that is appealing to them.

[0387] (Application example 2)

[0388] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0389] Conventional content distribution services have the problem that it is difficult for users to efficiently find new videos from a large number of video distribution channels, and it takes a long time to find content that interests them. In addition, personalized video recommendations based on users' emotions and interests are insufficient, leaving a need for an improved viewing experience. To solve these issues, a system is needed that can recognize users' emotions and quickly provide videos that meet their individual needs.

[0390] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0391] In this invention, the server includes means for users to register multiple video distribution channels, means for automatically detecting newly posted videos, means for analyzing the content of the new videos using a generative artificial intelligence model and extracting important parts, means for automatically generating short videos based on the extracted important parts, means for providing the generated short videos to the user, means for recognizing user emotions in real time, and means for personalizing content based on the user emotions. This allows users to efficiently find video content that interests them, significantly improving their viewing experience.

[0392] "User" means an individual who uses a video distribution channel to view and manage content.

[0393] A "video distribution channel" is a medium that provides video content via the Internet.

[0394] "Newly posted videos" are video contents that have recently been uploaded to a video distribution channel.

[0395] A "generative artificial intelligence model" is a machine learning model that analyzes the content of videos based on data and extracts and generates important parts.

[0396] The "important parts" are scenes or information in the video that are expected to be of particular interest to the user.

[0397] A "short video" is a video that extracts important parts from the original video so that the content can be understood in a short amount of time.

[0398] "User emotion" refers to the emotional state shown by the user while viewing, and is inferred from facial expressions and voice.

[0399] "Personalizing content" means providing content that is individually optimized based on a user's emotions and viewing history.

[0400] To implement this invention, a system is required in which a smartphone is used as the main user terminal and which links with a server. Specific processing steps and the hardware and software used for these steps are described below.

[0401] User terminal operation

[0402] 1. User registration and login:

[0403] Users install the application on their smartphone and create an account.

[0404] After creating an account, the user enters their authentication information on the login screen to log in. Firebase Authentication is used for authentication.

[0405] 2. Video streaming channel registration:

[0406] After logging in, users enter the URL and name of their video streaming channel and register with the system through the application. This information is stored in the Firebase Database.

[0407] 3. View short videos:

[0408] The server generates a notification for the short video, and the user can view it on their smartphone. Notifications are sent via Firebase Cloud Messaging.

[0409] 4. Watching full-length videos:

[0410] If a user is interested in a short video, they can click on the link to the original full-length video to watch it.

[0411] Server Operation

[0412] 1. User and channel information management:

[0413] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in the Firebase Database.

[0414] 2. New video detection:

[0415] The server periodically checks registered video streaming channels using the YouTube Data API and Twitch API to detect newly uploaded videos, and stores this information in the Firebase Database.

[0416] 3. Download and analyze the video:

[0417] The server downloads the newly detected video and analyzes it using Google® Cloud AI's Video Intelligence API.

[0418] 4. Short video generation:

[0419] A generative AI model extracts key parts of the video and generates a short video, including a thumbnail and key scenes. The generated short video is stored in Firebase Storage, and its metadata is stored in Firebase Database.

[0420] 5. Short Video Notifications:

[0421] The generated short video is sent to the user's device using Firebase Cloud Messaging.

[0422] 6. User Emotion Recognition:

[0423] The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice in real time, using the OpenCV library and Google Cloud Speech-to-Text API.

[0424] 7. Sentiment Data Analysis and Personalized Recommendations:

[0425] The emotional data collected by the server is analyzed using Firebase ML Kit, and videos are personalized based on the user's viewing history and emotional history.

[0426] Specific examples

[0427] For example, if user D is interested in entertainment-related videos, he / she registers multiple entertainment channels in the app. The server periodically checks these channels to detect new video content. Let's consider the case where a trailer for a new movie has been uploaded as a new video.

[0428] The server downloads new movie trailers and uses a generative AI model to extract key scenes. The resulting trailers are short videos of approximately three minutes, each containing thumbnails of the movie and key scenes.

[0429] While User D is watching this short video, the emotion engine analyzes his facial expressions and voice to recognize his excitement and interest. If User D is determined to be particularly interested, the server will also recommend other related entertainment videos.

[0430] Prompt Sentence Examples

[0431] "Analyze the latest movie trailers, extract key scenes, and generate a short video of less than 3 minutes. Include text information about the main cast and director."

[0432] In this way, users can efficiently find content that interests them, improving their viewing experience.

[0433] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0434] Step 1:

[0435] User registration and login:

[0436] Input: User authentication information (email address, password)

[0437] Output: Session information for the authenticated user

[0438] Specific behavior:

[0439] A user installs a smartphone application and creates an account. They then enter their credentials on the login screen and are authenticated using Firebase Authentication. If authentication is successful, a user session is created and the user is logged in to the application.

[0440] Step 2:

[0441] Subscribe to our video streaming channel:

[0442] Input: URL or name of the video distribution channel

[0443] Output: Registered channel information (channel ID, URL)

[0444] Specific behavior:

[0445] Users can register their video streaming channel by entering its URL and name in the application. The information is stored in the Firebase Database, and the channel ID and URL are associated.

[0446] Step 3:

[0447] New Video Detection:

[0448] Input: Registered video distribution channel information

[0449] Output: New video information (video ID, title, URL)

[0450] Specific behavior:

[0451] The server periodically checks registered video streaming channels using the YouTube Data API and Twitch API, detects newly uploaded videos, and stores that information in the Firebase Database.

[0452] Step 4:

[0453] Video download and analysis:

[0454] Input: New video information (video ID, URL)

[0455] Output: Data with important parts extracted

[0456] Specific behavior:

[0457] The server downloads a new video and analyzes it using Google Cloud AI's Video Intelligence API. A generative AI model is used to analyze the video content and extract key parts (scenes, audio, etc.).

[0458] Step 5:

[0459] Short video generation:

[0460] Input: Data with important parts extracted

[0461] Output: Generated short video file (video ID, short video URL)

[0462] Specific behavior:

[0463] The server generates a short video based on the extracted key parts. The generative AI model is given a prompt: "Analyze the content of the latest video, extract key scenes, and generate a short video of less than 3 minutes." The generated short video is stored in Firebase Storage, and its metadata (video ID, short video URL) is stored in Firebase Database.

[0464] Step 6:

[0465] Short Video Notifications:

[0466] Input: The generated short video file and its metadata

[0467] Output: Push notification to user device

[0468] Specific behavior:

[0469] The generated short video is pushed to the user's device using Firebase Cloud Messaging, and when the user clicks on the notification, the short video starts playing.

[0470] Step 7:

[0471] User Emotion Recognition:

[0472] Input: Facial expression and voice data while the user is watching a short video

[0473] Output: Analyzed user emotion data (excitement, joy, interest, etc.)

[0474] Specific behavior:

[0475] The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice in real time, and uses the OpenCV library and Google Cloud Speech-to-Text API to collect emotional data and estimate the user's emotional state.

[0476] Step 8:

[0477] Sentiment data analysis and personalized recommendations:

[0478] Input: Analyzed user emotion data and viewing history

[0479] Output: A personalized video recommendation list

[0480] Specific behavior:

[0481] The server analyzes the collected emotion data using Firebase ML Kit and provides personalized videos based on the user's viewing and emotion history. If it determines that the user enjoys a particular piece of content, it will also recommend other interesting videos related to that content.

[0482] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0483] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0484] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0485] [Second embodiment]

[0486] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0487] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0488] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0489] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0490] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0491] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0492] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0493] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0494] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0495] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0496] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0497] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0498] The system of the present invention allows users to efficiently monitor multiple video distribution channels and quickly identify content they are interested in. This system is primarily composed of a user terminal, a server, and a generative AI model.

[0499] System Overview

[0500] The basic operation of the system is that users subscribe to video distribution channels, and when new videos are uploaded, the system automatically detects them, analyzes the important parts, and generates short videos. This allows users to quickly get an overview of the video through the short video, and if they are interested, they can watch the original full-length video.

[0501] User terminal operation

[0502] 1. User registration and login:

[0503] A user installs an application and creates a user account.

[0504] You can log in by entering your authentication information on the login screen.

[0505] 2. Video streaming channel registration:

[0506] After logging in, users enter the URL and name of the video distribution channel they wish to register and register it in the system.

[0507] 3. View short videos:

[0508] Receive notifications of short videos generated by the system.

[0509] The user views the short video and gets an overview of it.

[0510] 4. Watching full-length videos:

[0511] Click on the link for the short video that interests you to go to the original full-length video page to watch it.

[0512] Server Operation

[0513] 1. User and channel information management:

[0514] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database.

[0515] 2. New video detection:

[0516] It periodically checks subscribed channels to detect newly uploaded videos, using external APIs such as the YouTube API.

[0517] 3. Download and analyze the video:

[0518] The detected new videos are downloaded to the server and their content is analyzed using a generative AI model.

[0519] 4. Short video generation:

[0520] Based on the key parts captured by the generative AI model, a short video is automatically generated, which includes elements such as a thumbnail, text, and narration.

[0521] 5. Metadata management for short videos:

[0522] Metadata for the generated short videos is generated and stored in a database.

[0523] 6. Short Video Notifications:

[0524] The user is notified that a short video has been generated and is made available for viewing.

[0525] Specific examples

[0526] For example, if user A is interested in cooking, he / she registers multiple cooking channels in the system. The server periodically checks these channels and detects when new cooking videos have been uploaded. Suppose the new video is "Easy dinner recipes you can make in 30 minutes."

[0527] The server downloads the video and analyzes it using a generative AI model, which extracts the key cooking steps and generates a short video of about five minutes, complete with thumbnails of the finished dish and a brief explanation of each step.

[0528] User A views this short video, quickly grasps the content, and if interested, watches the original 30-minute video. This process allows User A to efficiently select content that interests them and make effective use of their time.

[0529] Based on the above details, the system of the present invention provides an innovative means for users to quickly and effectively grasp video content that interests them and efficiently obtain the information they need.

[0530] The processing flow will be explained below.

[0531] Step 1: User logs in

[0532] The user starts the application on the device and enters authentication information (user name, password, etc.) on the login screen. When the user clicks the "Login" button, the device sends the entered authentication information to the server. The server verifies the authentication information in the database, and if authentication is successful, issues a session ID and returns it to the device.

[0533] Step 2: User subscribes to a video streaming channel

[0534] The user opens the channel subscription UI within the application and enters the URL and name of the video streaming channel they want to subscribe to. When the user clicks the "Subscribe" button, the device sends the entered channel information to the server. The server stores the received channel information in a database and notifies the user that the subscription was successful.

[0535] Step 3: Server detects new video

[0536] The server periodically (for example, every hour) retrieves a list of registered video distribution channels. The server uses the YouTube API to check for new videos on each channel. If a new video is found, it stores the video information (title, URL, posting date, etc.) in the database.

[0537] Step 4: The server downloads the new video

[0538] The server downloads the video data from the new video URL and stores the downloaded video data in a temporary location.

[0539] Step 5: The server analyzes the video using a generative AI model

[0540] The server loads the downloaded video data into the generative AI model as input, which then analyzes the content of the video and extracts important parts (keyframes, text information, audio information, etc.).

[0541] Step 6: The server generates the short video

[0542] The server automatically generates a short video based on the extracted key parts, adding a thumbnail, a text description, and optionally a narration to the short video, and stores the generated short video on the server.

[0543] Step 7: The server generates metadata for the short video

[0544] The server generates metadata about the short video (such as the original video URL, title, and creation date and time) and stores it in a database.

[0545] Step 8: The server notifies the user of the generated short video

[0546] The server notifies the corresponding user that a new short video has been generated, and the terminal receives the notification and displays to the user that a short video is available.

[0547] Step 9: User Views Short Video

[0548] The user clicks on the notification on their device to open the short video playback screen. The device streams or downloads the short video data from the server and plays it. The user watches the short video and gets an overview.

[0549] Step 10: User watches a full-length video

[0550] If a user is interested in the summary of a short video after watching it, they can click on the link to the original full-length video. The device will open the original full-length video page on YouTube via a browser or other device, and the user can watch the full-length video.

[0551] Example 1

[0552] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0553] Conventional methods for viewing video content have made it difficult for users to efficiently identify videos that interest them. In particular, there has been a lack of a way to quickly review only the important parts of a large number of video distribution channels or long video content. This has resulted in the problem of requiring users to spend a huge amount of time. There has also been a demand for a method for quickly identifying the content of newly posted videos.

[0554] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0555] In this invention, the server includes means for users to register multiple media distribution channels, means for automatically detecting newly posted video data, means for analyzing the content of the new video data using the generated artificial intelligence model and extracting important parts, means for automatically generating short video data based on the extracted important parts, means for generating and saving metadata related to the generated short video data, means for sending a notification of the generated short video data to a user terminal using a push notification system, and means for providing the generated short video data to the user. This allows users to efficiently understand content that interests them and quickly check important parts, significantly reducing the time required for video viewing.

[0556] "User" refers to a person who uses the system, subscribes to a video distribution channel, and views the generated short videos.

[0557] A "media distribution channel" is an online platform that provides video content, such as YouTube or Vimeo.

[0558] "Video Data" means video content held in electronic format, including any combination of still images, audio, and text.

[0559] A "server" is a computer system that receives requests from users and detects, analyzes, stores, and provides video data.

[0560] A "generated artificial intelligence model" is a model that uses a pre-trained machine learning algorithm and is used to analyze video content and extract important parts.

[0561] "Important portion extraction" is the process of identifying and extracting portions of video data that are likely to be of particular interest to the user.

[0562] "Short video data" refers to a short edited video generated based on the important parts, and is created to provide an overview to the user.

[0563] "Metadata" refers to information related to short video data, including title, summary, thumbnail, timestamp, and the like.

[0564] A "push notification system" is a technology or service that allows a server to send notifications to a user terminal in real time.

[0565] The system of the present invention is designed to enable users to efficiently monitor multiple media distribution channels and quickly identify content they are interested in. The system is comprised of a user terminal, a server, and a generative AI model.

[0566] First, a user installs the application on their device and creates a user account. To create an account, they must enter information such as a username, password, and email address. The user then logs in using these credentials. After logging in, the user registers with the system by entering the URL and name of a media distribution channel. For example, this could be a channel like YouTube or Vimeo.

[0567] The server uses a database such as MySQL or PostgreSQL to store and manage user authentication information and information about registered media distribution channels. The server periodically checks whether new videos have been uploaded to registered channels using the YouTube API or Vimeo API. When a new video is detected, it obtains the video's metadata (title, upload date and time, URL, etc.) and downloads it to the server using YouTube-dl or FFmpeg.

[0568] The server inputs the downloaded video data into a generative AI model (e.g., GPT-3, BERT). It sends the following prompt to the generative AI model:

[0569] Analyze a video for "Easy Dinner Recipes that Can Be Made in 30 Minutes" and extract the key cooking steps. Create a short video of approximately 5 minutes based on the extracted steps. Use an image of the finished dish as the thumbnail and add a brief description of each step.

[0570] The generative AI model follows these prompts to analyze the video content and extract key parts. Once the key parts are extracted, a short video is generated using software such as OpenCV and MoviePy. The short video may include a thumbnail, subtitles, and narration.

[0571] The metadata of the generated short video data (title, summary, thumbnail, etc.) is saved back to the database. The server uses Firebase Cloud Messaging (FCM) to send a notification of the generated short video data to the user's device. The notification is displayed on the user's device, and a list of new short videos is displayed in the "Notifications" tab in the app. The user can click the notification to play the short video in the app's video player (e.g., ExoPlayer).

[0572] As a concrete example, consider the case where User B is interested in the latest technology news. User B registers multiple technology media distribution channels in the system. The server periodically checks these channels and discovers that a new video has been uploaded with the title "Explaining the latest advances in AI technology." The server downloads this video and analyzes it with a generative AI model. The generative AI model extracts the "explanatory parts about important technologies" and edits them into a short video of about 5 minutes. This short video includes subtitles that provide an overview of the technology and relevant screenshots.

[0573] A notification is sent to User B's device and displayed as a new short video within the app. User B plays the short video and gets a quick overview of the progress of AI technology. Intrigued, User B clicks on the link to the original full-length video to watch in more detail.

[0574] This process allows users to efficiently select content that interests them and make effective use of their time. The system of the present invention provides an innovative means for users to quickly and effectively find video content that interests them and efficiently obtain the information they need.

[0575] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0576] Step 1: User registration and login

[0577] Specific behavior:

[0578] Input: A user installs an application on their device and creates an account by entering a username, password, and email address.

[0579] Data processing / calculation: The server receives the entered authentication information and stores it in a database such as MySQL or PostgreSQL. When logging in, the entered authentication information is compared with the information in the database.

[0580] Output: Once the correct credentials are entered, the user can log into their account. After logging in, the user's dashboard will be displayed.

[0581] Step 2: Register your video distribution channel

[0582] Specific behavior:

[0583] Input: After logging in, the user enters the URL and name of the video distribution channel and clicks the "Register" button.

[0584] Data processing / calculation: The server receives the transmitted channel information and stores it in a database.

[0585] Output: The registered channel information is saved in the database and will be checked from next time onwards.

[0586] Step 3: Detect and download new videos

[0587] Specific behavior:

[0588] Input: The server periodically checks for new videos using the YouTube API and / or Vimeo API, based on the channel information stored in the database.

[0589] Data processing / calculation: When a new video is detected, the video's metadata (title, upload date and time, URL, etc.) is obtained, and the video data is downloaded to the server using YouTube-dl or FFmpeg.

[0590] Output: The downloaded video data is saved on the server.

[0591] Step 4: Analyze the video and generate a short video

[0592] Specific behavior:

[0593] Input: Video data stored on the server is fed into a generative AI model (e.g., GPT-3, BERT) using a prompt like this:

[0594] Analyze a video for "Easy Dinner Recipes that Can Be Made in 30 Minutes" and extract the key cooking steps. Create a short video of approximately 5 minutes based on the extracted steps. Use an image of the finished dish as the thumbnail and add a brief description of each step.

[0595] Data processing / computation: The generative AI model analyzes the video data and extracts key parts. Based on the results, short video data is generated using OpenCV and MoviePy. The short video may include subtitles, thumbnails, and narration.

[0596] Output: The generated short video data is saved on the server.

[0597] Step 5: Notification and viewing of short videos

[0598] Specific behavior:

[0599] Input: The metadata of the generated short video data (title, summary, thumbnail, etc.) is stored in a database.

[0600] Data processing / calculation: The server uses Firebase Cloud Messaging (FCM) to send notifications of short video data to the user's device.

[0601] Output: A notification will be displayed on the user's device, and a list of new short videos will be displayed in the "Notifications" tab within the app.

[0602] Step 6: Watch a full-length video

[0603] Specific behavior:

[0604] Input: When the user clicks on the notification, a short video will play in the app's in-app video player (e.g. ExoPlayer).

[0605] Data processing / computation: While playing the short video, a link to the original full-length video is provided.

[0606] Output: If the user is interested, they can click on the link to the original full-length video, which will automatically open a browser and allow them to watch the full-length video.

[0607] The above are the specific processing steps of this system. The operations performed at each step and the accompanying data flow are explained in detail.

[0608] (Application example 1)

[0609] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0610] Conventional video streaming platforms make it difficult for users to efficiently grasp the content that interests them. In particular, there are limited ways to quickly understand the content of long videos before watching them. Furthermore, there is insufficient consideration given to optimizing the viewing experience on smart devices and wearable devices. This makes it difficult for users to make effective use of their time, and there is a demand for a way to quickly obtain the information they need.

[0611] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0612] In this invention, the server includes a means for allowing a user to register multiple video distribution channels, a means for automatically detecting newly posted videos, a means for analyzing the content of the new videos using a generative AI model and extracting important parts, a means for automatically generating short videos based on the extracted important parts, and a means for providing the generated short videos to the user via a smart device, particularly a mobile terminal or a wearable device, thereby enabling the user to quickly understand the content of content that interests them and to watch full-length videos as needed.

[0613] A "user" is an individual or a corporation that uses the system of the present invention to subscribe to a video distribution channel and watch short videos to understand the content of the videos.

[0614] "Video distribution channel" refers to a website or service that provides video content on the Internet and to which users can register content that interests them.

[0615] A "new video" is a video that has been newly uploaded to a video distribution channel and is detected by the system of the present invention.

[0616] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to analyze the content of a video and extract important parts.

[0617] A "mobile terminal" is a portable electronic device such as a smartphone or tablet, which is a device that a user uses to watch short videos.

[0618] A "wearable device" is an electronic device that is worn on the body, such as smart glasses or a smart watch, and is used by users to watch short videos.

[0619] "Short videos" are short videos that are automatically generated by extracting only the important parts of new videos analyzed using a generative AI model.

[0620] The "server" is a central system that manages information about video distribution channels registered by users, detects and analyzes new videos, and generates and provides short videos.

[0621] The system of the present invention allows users to efficiently find new videos from video distribution channels. This system is mainly composed of a server, a user terminal, and a generative AI model.

[0622] The server includes means for users to register multiple video distribution channels, means for automatically detecting newly posted videos, means for analyzing the content of new videos using a generative AI model and extracting important parts, means for automatically generating short videos based on the extracted important parts, and means for providing the generated short videos to users via smart devices, particularly mobile terminals and wearable devices.

[0623] System configuration

[0624] User terminal operation

[0625] 1. User registration and login:

[0626] Users install the application on their smart device (smartphone, tablet, etc.) and create an account. They can then log in by entering their authentication information on the login screen.

[0627] 2. Video streaming channel registration:

[0628] After logging in, users enter the URL and name of the video streaming channel they wish to subscribe to and register through the application.

[0629] 3. View short videos:

[0630] The server notifies the user of the short video, which the user can then view on a smart device or wearable device (such as smart glasses or a smartwatch) by tapping the notification.

[0631] 4. Watching full-length videos:

[0632] Tap the link of the short video that interests you to go to the original full-length video page to watch it.

[0633] Server Operation

[0634] 1. User and channel information management:

[0635] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database (MongoDB).

[0636] 2. New video detection:

[0637] Registered channels are checked periodically and newly uploaded videos are detected using external APIs such as the YouTube Data API.

[0638] 3. Download and analyze the video:

[0639] The detected new videos are downloaded to the server and the content of the videos is analyzed using a generative AI model (OpenAI GPT).

[0640] 4. Short video generation:

[0641] Based on the key parts captured by the generative AI model, a short video is automatically generated, which includes a summary, thumbnail, and text information.

[0642] 5. Notification and provision of short videos:

[0643] Using Firebase Cloud Messaging (FCM), the user is notified that a short video has been created and can watch it.

[0644] Specific examples

[0645] For example, if a user subscribes to cooking channels, the server periodically checks these channels to detect when new cooking videos have been uploaded. Suppose the new video is an "easy dinner recipe that can be made in 30 minutes." The server downloads this video and analyzes it using a generative AI model. As a result, the important cooking steps are extracted and generated into a short video of about 5 minutes. This short video includes a thumbnail of the finished dish and a brief explanation of each step.

[0646] Examples of prompts include:

[0647] Create a short video by extracting only the most important parts from the following videos:

[0648] 1. Intro

[0649] 2. Ingredient List

[0650] 3.Cooking Steps (Step by Step)

[0651] 4. Final Dish Presentation

[0652] This allows users to view short videos, quickly grasp the content, and then watch the full 30-minute video if they are interested. This process allows users to efficiently select content that interests them and make effective use of their time.

[0653] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0654] Step 1:

[0655] The server manages user registration and authentication information. A user creates an account through a smart device and enters their email address and password. The entered data is stored in the server's MongoDB database. The output is a user ID and an authentication token.

[0656] Step 2:

[0657] After logging in, a user registers multiple video distribution channels. The user inputs the channel URL and name from their device and sends this to the server. The server stores this information in a database. The input is the channel URL and name, and the output is updating the database.

[0658] Step 3:

[0659] The server periodically checks all registered channels to see if new videos have been uploaded. This check is performed using the YouTube Data API. The input is channel information, and the output is metadata for new videos.

[0660] Step 4:

[0661] When a new video is detected, the server downloads it and stores it in the server's local storage using the video's URL. The input is the URL of the new video, and the output is the video file.

[0662] Step 5:

[0663] The server uses a generative AI model to analyze the content of the downloaded video. As a specific example, it generates a prompt sentence and inputs it into the generative AI model. The generative AI model extracts important parts and returns the text analysis results. The input is the video content and the prompt sentence, and the output is the analysis results.

[0664] Step 6:

[0665] The server automatically generates a short video based on the analysis results. Using video editing software, the extracted important parts are cut out and edited. The input is the analysis results and the original video file, and the output is the short video file.

[0666] Step 7:

[0667] The server generates metadata about the generated short video and stores it in a database. The metadata includes the video title, summary, length, URL, etc. The input is the short video file, and the output is the metadata and its storage.

[0668] Step 8:

[0669] The server uses Firebase Cloud Messaging (FCM) to notify the user that the generated short video is available for viewing. The notification includes the title and link of the short video. The input is notification information, and the output is a notification to the user device.

[0670] Step 9:

[0671] Users receive a notification on their smart device or wearable device and watch a short video. They can access the original full-length video by tapping the link in the short video they watched. The input is the notification message, and the output is the viewing of the short video and access to the original full-length video.

[0672] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0673] The system of the present invention efficiently manages video distribution channels and quickly identifies content that users are interested in. Furthermore, by combining it with an emotion engine that recognizes user emotions, it realizes more personalized video delivery.

[0674] System Overview

[0675] The system allows users to register multiple video distribution channels, automatically detects newly posted videos, and analyzes their content to generate short videos. This allows users to quickly get an overview of the video and watch only those videos that interest them. Furthermore, by incorporating an emotion engine, the system can recognize users' emotions in real time and adjust and recommend video content accordingly.

[0676] User terminal operation

[0677] 1. User registration and login:

[0678] A user installs the application, creates an account, and then enters their authentication information on the login screen to log in.

[0679] 2. Video streaming channel registration:

[0680] After logging in, users enter the URL and name of their video distribution channel and register it in the system.

[0681] 3. View short videos:

[0682] Receive notifications of short videos generated by the system and view the short videos.

[0683] 4. Watching full-length videos:

[0684] If you are interested in the short video, click on the link to the original full-length video and watch it.

[0685] Server Operation

[0686] 1. User and channel information management:

[0687] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database.

[0688] 2. New video detection:

[0689] It periodically checks subscribed channels to detect newly uploaded videos. It uses the YouTube API to check for new videos and stores that information in a database.

[0690] 3. Download and analyze the video:

[0691] New videos are downloaded to the server and analyzed using the generative AI model.

[0692] 4. Short video generation:

[0693] A generative AI model analyzes the content of a video, extracts key parts, and automatically generates a short video that includes a thumbnail, text, and narration.

[0694] 5. Metadata management for short videos:

[0695] The metadata of the generated short videos is stored and managed in a database.

[0696] 6. Short Video Notifications:

[0697] The user is notified that a short video has been generated and is made available for viewing.

[0698] Emotion Engine Operation

[0699] 1. User Emotion Recognition:

[0700] While the user is watching a short video, the emotion engine uses cameras and sensors to analyze the user's facial expressions and voice, collecting emotional data in real time.

[0701] 2. Sentiment Data Analysis:

[0702] The server analyzes the collected emotional data to understand the user's emotions while watching, thereby evaluating the user's interest and satisfaction.

[0703] 3. Adjustment of short video content:

[0704] Based on the analysis results of the emotion engine, the server adjusts the content of the short video in real time, specifically by emphasizing elements that attract the user's interest and omitting unnecessary parts.

[0705] 4. Personalized Recommendations:

[0706] Based on past emotional data, the server recommends the most suitable short videos for the user. It analyzes the user's emotional history and prioritizes videos with similar emotional patterns.

[0707] Specific examples

[0708] For example, if user B is interested in music, he / she can register multiple music channels in the system. The server periodically checks these channels and detects when new music videos are uploaded. In this case, the new videos are called "latest hit songs."

[0709] The server downloads the new video and analyzes it with a generative AI model, which extracts key parts of the song and generates a short video of about three minutes, including a thumbnail of the song and a description of the key lyrics.

[0710] User B watches the short video, and the emotion engine analyzes his facial expressions and reactions. If it detects that User B is particularly enjoying the song, other videos related to the song will be recommended based on the emotion engine's analysis. As a result, User B can consistently access content that interests him, improving his viewing experience.

[0711] Based on the above details, the system of the present invention takes into account the user's emotions and provides video content that meets individual needs, thereby realizing an efficient and effective video viewing experience.

[0712] The processing flow will be explained below.

[0713] Step 1: User installs the application on their local device and creates an account

[0714] The user downloads and installs the application on their device. After installation, they enter the required information (name, email address, password, etc.) on the account creation screen to create a new account. When they click the "Register" button, the device sends the entered information to the server, which then saves the account information in a database.

[0715] Step 2: User logs in

[0716] The user starts the application and enters authentication information (email address, password) on the login screen. When the "Login" button is clicked, the device sends the entered authentication information to the server. The server collates the information with the authentication information in the database, and if authentication is successful, returns a session ID to the device.

[0717] Step 3: User subscribes to a video streaming channel

[0718] After logging in, the user opens the "Subscribe to Channel" section in the application menu, enters the URL or name of the video streaming channel they want to subscribe to, and clicks the "Subscribe" button. The device sends the entered channel information to the server, which then stores it in a database.

[0719] Step 4: The server periodically detects new videos

[0720] The server periodically (for example, every hour) retrieves a list of registered video distribution channels from the database. It checks each channel using the YouTube API or similar to see if new videos have been posted. If a new video is found, it saves the video information (title, URL, posting date, etc.) in the database and proceeds to the next step.

[0721] Step 5: The server downloads the new video

[0722] The server downloads the video data using the URL of the new video, and then stores it in a temporary location.

[0723] Step 6: The server analyzes the video using the generated AI model

[0724] The server inputs the temporarily stored video data into the generative AI model, which then analyzes the video content and extracts important parts (keyframes, text, audio information, etc.).

[0725] Step 7: The server generates the short video

[0726] The server uses the extracted key parts to automatically generate a short video, which includes a thumbnail, a text description, and optionally a narration, and stores the generated short video on the server.

[0727] Step 8: Recognize the user's emotions

[0728] While a user is viewing a short video, their facial expressions and voice are captured through the device's camera and microphone. The emotion engine analyzes this data in real time to determine the user's emotional state (e.g., joy, interest, indifference, etc.).

[0729] Step 9: The server collects the emotion data and adjusts the content of the short video.

[0730] The device sends the collected emotional data to a server, which then analyzes the data and adjusts the content of the short video in real time based on the user's reaction. For example, it may expand the parts that the user found interesting and omit the parts that the user did not find interesting.

[0731] Step 10: The server manages the metadata for the short video

[0732] The server generates metadata including the generated short video and adjustment information based on the emotion data, and stores the metadata in a database. This metadata includes the original video URL, title, creation date and time, emotional state, etc.

[0733] Step 11: The server notifies the user of the short video

[0734] The server notifies the user that a new short video has been generated. The device receives the notification and displays to the user that a short video is available.

[0735] Step 12: User views the short video and, if desired, watches the full-length video

[0736] The user clicks on the notification on their device to open the short video playback screen. The device streams or downloads the short video data from the server and plays it. The user watches the short video and gets an overview of it. If they are interested, they click on the link to the original full-length video and watch it on YouTube.

[0737] This series of processes allows users to efficiently select content that interests them and improve their viewing experience.By introducing an emotion engine, the system can provide optimal content according to the user's emotions.

[0738] Example 2

[0739] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0740] Conventional video viewing systems have difficulty efficiently discovering content that interests users and analyzing emotions toward that content to provide personalized recommendations. Furthermore, viewing a full-length video in its entirety takes a long time, and there is a high risk of viewing content that does not match the user's interests. There is a need to address these issues.

[0741] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0742] In this invention, the server includes means for users to register multiple media, means for automatically detecting newly posted data, means for analyzing the content of the new data using the generated artificial intelligence model and extracting important parts, means for automatically generating short data based on the extracted important parts, means for providing the generated short data to the user, means for collecting and analyzing user emotion data, means for adjusting the content of the short data based on the emotion data, and means for making personalized recommendations. This allows users to efficiently discover content that interests them and receive personalized recommendations, thereby improving their viewing experience.

[0743] "User" refers to a person who uses the system to register, view, and rate video content.

[0744] "Media" refers to video distribution channels and content sources.

[0745] "Data" refers to the information that the system processes and analyzes, such as video, metadata, and emotional data.

[0746] "Artificial intelligence model" refers to machine learning algorithms and deep learning models used to analyze and generate content and recognize emotions.

[0747] "Short data" refers to a short video clip created by extracting only the important parts of the generated video.

[0748] "Emotion data" refers to information about emotions acquired by analyzing the user's facial expressions and voice.

[0749] "Personalized recommendations" refers to content that is individually recommended to users based on their interests, past viewing history, and emotional data.

[0750] "Metadata" refers to additional information such as title, summary, and playback time related to the generated short data.

[0751] The system of the present invention allows users to efficiently manage video content and quickly find content they are interested in. The system is implemented as follows.

[0752] User terminal operation

[0753] The user terminal functions as an interface for users to access the system. Users begin using the system by installing the application and creating an account. After logging in, users register video distribution channels that interest them. When a new video is posted to a registered channel, the system generates a short video and notifies the user. Users can watch the short video and, if interested, access the original full-length video.

[0754] Server Operation

[0755] The server is the central part of the system and plays the following roles:

[0756] 1. User and channel information management:

[0757] The server stores and manages the authentication information entered by the user and the registered channel information in a database, using a common database management system (e.g., MySQL).

[0758] 2. New video detection:

[0759] The server periodically checks subscribed channels (e.g., using a scheduler cron job) and uses an API (e.g., YouTube API) to detect newly uploaded videos.

[0760] 3. Download and analyze the video:

[0761] When a new video is detected, the server downloads the video, stores it locally using a Python library (e.g., pytube), and then analyzes the video using the generated AI model.

[0762] 4. Short video generation:

[0763] The generative AI model analyzes the video and extracts key parts. Specifically, it uses OpenCV to analyze the video frames, and then uses a natural language processing library (e.g., NLTK) to generate narration and text, automatically generating a short video.

[0764] 5. Metadata management for short videos:

[0765] Metadata about the generated short videos (e.g., title, summary, and playback time) is stored and managed in a database.

[0766] 6. Short Video Notifications:

[0767] The server uses a push notification service (e.g., Firebase Cloud Messaging) to notify the user that a short video has been generated.

[0768] Emotion Engine Operation

[0769] The emotion engine plays an important role in enhancing the user's viewing experience.

[0770] 1. User Emotion Recognition:

[0771] While the user is watching a short video, facial expression and voice data is collected using the device's camera and microphone, using OpenCV and the Emotion API.

[0772] 2. Sentiment Data Analysis:

[0773] The server analyzes the collected emotion data in real time, using an emotion analysis library (e.g., DeepFace) to identify emotions from the user's facial expressions and stores the results in a database.

[0774] 3. Adjustment of short video content:

[0775] Based on the emotion data, the server sends prompts to the generative AI model to adjust the content of the short video in real time, such as "include more frames in which the user is smiling."

[0776] 4. Personalized Recommendations:

[0777] The server recommends the most suitable short videos to users based on their past emotional data. It uses a machine learning model using Python libraries (e.g., scikit-learn) to analyze users' emotional patterns and provide the most suitable content.

[0778] Specific examples

[0779] For example, if user B is interested in music, he / she registers multiple music channels in the system. The server periodically checks these channels and detects when new music videos are uploaded. In this case, let's say the new videos are "latest hit songs."

[0780] The server downloads this new video and runs it through a generative AI model, which extracts key parts of the song and generates a short video of about three minutes, including a thumbnail of the song and a description of the key lyrics.

[0781] User B watches the short video while the emotion engine analyzes his facial expressions and reactions. If it detects that User B is particularly enjoying the song, other videos related to the song will be recommended based on the emotion engine's analysis. As a result, User B can consistently access content that interests them, improving their viewing experience.

[0782] Prompt Sentence Examples

[0783] "Extract the key parts of the music video 'The Latest Hit Song' and edit them into a short video of approximately 3 minutes. Please include a thumbnail and explanations of the key lyrics."

[0784] This concludes the description of the embodiment of the invention. The system efficiently and effectively manages video content while incorporating user emotional data, making it possible to provide a viewing experience tailored to individual needs.

[0785] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0786] Step 1:

[0787] User Registration and Login

[0788] Input: The user enters information such as a name, email address, or password.

[0789] Processing: Information entered from the user's terminal is sent to the server, which then stores the user information in a database.

[0790] Output: The user account is created and you can log in.

[0791] Specific operation: The user installs the application and presses the "New Registration" button. They enter the required information and click the "Register" button. The server receives the user information and saves it in the database. The user enters their email address and password on the login screen and presses the "Login" button to complete the login.

[0792] Step 2:

[0793] Video streaming channel registration

[0794] Input: The user enters the URL or name of a video streaming channel.

[0795] Processing: The channel information entered from the user terminal is sent to the server, which then stores the channel information in a database.

[0796] Output: Video distribution channels are registered and managed in the system.

[0797] Specific operation: After logging in, the user goes to the "Subscribe to Channel" screen, enters the URL and name of the video distribution channel, and presses the "Subscribe" button. The server receives the channel information and stores it in the database.

[0798] Step 3:

[0799] New video detection

[0800] Input: Information about the registered video streaming channel.

[0801] Processing: The server periodically checks the channel (e.g., using a scheduler cron job) and uses the YouTube API to detect new videos.

[0802] Output: The information of the newly uploaded video is saved in the database.

[0803] Specific operation: The server periodically sends an API request based on the channel information to check for new videos. If a new video is found, the video information is saved in the database.

[0804] Step 4:

[0805] Downloading and analyzing videos

[0806] Input: The URL of the new video.

[0807] Processing: The server downloads the video, analyzes it with the generative AI model, and saves the video locally using a Python library (e.g., pytube).

[0808] Output: Analysis results from the generative AI model.

[0809] Specific operation: The server obtains the URL of the new video and starts downloading it. After the download is complete, the video is input into the generative AI model for analysis.

[0810] Step 5:

[0811] Short video generation

[0812] Input: Analysis results of the generative AI model (important part).

[0813] Processing: The server generates a short video based on key parts obtained from the generative AI model, performs frame analysis using OpenCV, and generates narration and text using a natural language processing library (e.g., NLTK).

[0814] Output: Short video.

[0815] How it works: Based on the analysis results obtained from the generative AI model, the server automatically generates a short video, adding thumbnails, text, and narration as needed.

[0816] Step 6:

[0817] Metadata management for short videos

[0818] Input: Metadata of the generated short video (e.g. title, summary, duration).

[0819] Processing: The server stores the metadata in a database.

[0820] Output: The saved metadata.

[0821] Specific operation: The server obtains the metadata of the generated short video and stores it in a database.

[0822] Step 7:

[0823] Short video notifications

[0824] Input: The generated short video.

[0825] Processing: The server uses a push notification service to send a notification of the short video to the user's device, such as using Firebase Cloud Messaging.

[0826] Output: Notification to the user.

[0827] Specific operation: The server sends a notification via the notification service to inform the user that a short video has been created. A notification icon will appear on the user's device, and the user can click it to watch the short video.

[0828] Step 8:

[0829] User Emotion Recognition

[0830] Input: Facial expressions and voice while the user watches a short video.

[0831] Processing: The user device collects emotion data using a camera and microphone and sends it to the server using OpenCV and the Emotion API.

[0832] Output: Collected emotion data.

[0833] How it works: While a user is watching a short video, the device's camera and microphone collect facial and voice data, which is then sent to a server in real time.

[0834] Step 9:

[0835] Sentiment Data Analysis

[0836] Input: Collected emotion data.

[0837] Processing: The server uses an emotion analysis library (e.g. DeepFace) to identify emotions from the user's facial expressions and voice.

[0838] Output: Analysis results (user sentiment).

[0839] Specific operation: The server analyzes the received emotion data and identifies the emotion the user is feeling while watching the video. The analysis results are stored in a database.

[0840] Step 10:

[0841] Adjustment of short video content

[0842] Input: Analysis results (user sentiment).

[0843] Processing: The server sends prompts to the generative AI model to adjust the content of the short video.

[0844] Output: Adjusted short video.

[0845] How it works: The server generates prompts and sends them to the generative AI model. For example, by issuing instructions such as "include as many frames as possible in which the user is smiling," the model generates short videos tailored to the user's interests.

[0846] Step 11:

[0847] Personalized Recommendations

[0848] Input: Analysis results (user's emotion history).

[0849] Processing: The server uses a machine learning model (e.g., scikit-learn) to recommend the best short videos for the user.

[0850] Output: Recommended short videos.

[0851] How it works: The server analyzes the user's emotional patterns and recommends the most suitable videos based on past emotional data, allowing the system to continue providing users with content that is appealing to them.

[0852] (Application example 2)

[0853] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0854] Conventional content distribution services have the problem that it is difficult for users to efficiently find new videos from a large number of video distribution channels, and it takes a long time to find content that interests them. In addition, personalized video recommendations based on users' emotions and interests are insufficient, leaving a need for an improved viewing experience. To solve these issues, a system is needed that can recognize users' emotions and quickly provide videos that meet their individual needs.

[0855] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0856] In this invention, the server includes means for users to register multiple video distribution channels, means for automatically detecting newly posted videos, means for analyzing the content of the new videos using a generative artificial intelligence model and extracting important parts, means for automatically generating short videos based on the extracted important parts, means for providing the generated short videos to the user, means for recognizing user emotions in real time, and means for personalizing content based on the user emotions. This allows users to efficiently find video content that interests them, significantly improving their viewing experience.

[0857] "User" means an individual who uses a video distribution channel to view and manage content.

[0858] A "video distribution channel" is a medium that provides video content via the Internet.

[0859] "Newly posted videos" are video contents that have recently been uploaded to a video distribution channel.

[0860] A "generative artificial intelligence model" is a machine learning model that analyzes the content of videos based on data and extracts and generates important parts.

[0861] The "important parts" are scenes or information in the video that are expected to be of particular interest to the user.

[0862] A "short video" is a video that extracts important parts from the original video so that the content can be understood in a short amount of time.

[0863] "User emotion" refers to the emotional state shown by the user while viewing, and is inferred from facial expressions and voice.

[0864] "Personalizing content" means providing content that is individually optimized based on a user's emotions and viewing history.

[0865] To implement this invention, a system is required in which a smartphone is used as the main user terminal and which links with a server. Specific processing steps and the hardware and software used for these steps are described below.

[0866] User terminal operation

[0867] 1. User registration and login:

[0868] Users install the application on their smartphone and create an account.

[0869] After creating an account, the user enters their authentication information on the login screen to log in. Firebase Authentication is used for authentication.

[0870] 2. Video streaming channel registration:

[0871] After logging in, users enter the URL and name of their video streaming channel and register with the system through the application. This information is stored in the Firebase Database.

[0872] 3. View short videos:

[0873] The server generates a notification for the short video, and the user can view it on their smartphone. Notifications are sent via Firebase Cloud Messaging.

[0874] 4. Watching full-length videos:

[0875] If a user is interested in a short video, they can click on the link to the original full-length video to watch it.

[0876] Server Operation

[0877] 1. User and channel information management:

[0878] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in the Firebase Database.

[0879] 2. New video detection:

[0880] The server periodically checks registered video streaming channels using the YouTube Data API and Twitch API to detect newly uploaded videos, and stores this information in the Firebase Database.

[0881] 3. Download and analyze the video:

[0882] The server downloads the newly detected video and analyzes it using Google Cloud AI's Video Intelligence API.

[0883] 4. Short video generation:

[0884] A generative AI model extracts key parts of the video and generates a short video, including a thumbnail and key scenes. The generated short video is stored in Firebase Storage, and its metadata is stored in Firebase Database.

[0885] 5. Short Video Notifications:

[0886] The generated short video is sent to the user's device using Firebase Cloud Messaging.

[0887] 6. User Emotion Recognition:

[0888] The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice in real time, using the OpenCV library and Google Cloud Speech-to-Text API.

[0889] 7. Sentiment Data Analysis and Personalized Recommendations:

[0890] The emotional data collected by the server is analyzed using Firebase ML Kit, and videos are personalized based on the user's viewing history and emotional history.

[0891] Specific examples

[0892] For example, if user D is interested in entertainment-related videos, he / she registers multiple entertainment channels in the app. The server periodically checks these channels to detect new video content. Let's consider the case where a trailer for a new movie has been uploaded as a new video.

[0893] The server downloads new movie trailers and uses a generative AI model to extract key scenes. The resulting trailers are short videos of approximately three minutes, each containing thumbnails of the movie and key scenes.

[0894] While User D is watching this short video, the emotion engine analyzes his facial expressions and voice to recognize his excitement and interest. If User D is determined to be particularly interested, the server will also recommend other related entertainment videos.

[0895] Prompt Sentence Examples

[0896] "Analyze the latest movie trailers, extract key scenes, and generate a short video of less than 3 minutes. Include text information about the main cast and director."

[0897] In this way, users can efficiently find content that interests them, improving their viewing experience.

[0898] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0899] Step 1:

[0900] User registration and login:

[0901] Input: User authentication information (email address, password)

[0902] Output: Session information for the authenticated user

[0903] Specific behavior:

[0904] A user installs a smartphone application and creates an account. They then enter their credentials on the login screen and are authenticated using Firebase Authentication. If authentication is successful, a user session is created and the user is logged in to the application.

[0905] Step 2:

[0906] Subscribe to our video streaming channel:

[0907] Input: URL or name of the video distribution channel

[0908] Output: Registered channel information (channel ID, URL)

[0909] Specific behavior:

[0910] Users can register their video streaming channel by entering its URL and name in the application. The information is stored in the Firebase Database, and the channel ID and URL are associated.

[0911] Step 3:

[0912] New Video Detection:

[0913] Input: Registered video distribution channel information

[0914] Output: New video information (video ID, title, URL)

[0915] Specific behavior:

[0916] The server periodically checks registered video streaming channels using the YouTube Data API and Twitch API, detects newly uploaded videos, and stores that information in the Firebase Database.

[0917] Step 4:

[0918] Video download and analysis:

[0919] Input: New video information (video ID, URL)

[0920] Output: Data with important parts extracted

[0921] Specific behavior:

[0922] The server downloads a new video and analyzes it using Google Cloud AI's Video Intelligence API. A generative AI model is used to analyze the video content and extract key parts (scenes, audio, etc.).

[0923] Step 5:

[0924] Short video generation:

[0925] Input: Data with important parts extracted

[0926] Output: Generated short video file (video ID, short video URL)

[0927] Specific behavior:

[0928] The server generates a short video based on the extracted key parts. The generative AI model is given a prompt: "Analyze the content of the latest video, extract key scenes, and generate a short video of less than 3 minutes." The generated short video is stored in Firebase Storage, and its metadata (video ID, short video URL) is stored in Firebase Database.

[0929] Step 6:

[0930] Short Video Notifications:

[0931] Input: The generated short video file and its metadata

[0932] Output: Push notification to user device

[0933] Specific behavior:

[0934] The generated short video is pushed to the user's device using Firebase Cloud Messaging, and when the user clicks on the notification, the short video starts playing.

[0935] Step 7:

[0936] User Emotion Recognition:

[0937] Input: Facial expression and voice data while the user is watching a short video

[0938] Output: Analyzed user emotion data (excitement, joy, interest, etc.)

[0939] Specific behavior:

[0940] The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice in real time, and uses the OpenCV library and Google Cloud Speech-to-Text API to collect emotional data and estimate the user's emotional state.

[0941] Step 8:

[0942] Sentiment data analysis and personalized recommendations:

[0943] Input: Analyzed user emotion data and viewing history

[0944] Output: A personalized video recommendation list

[0945] Specific behavior:

[0946] The server analyzes the collected emotion data using Firebase ML Kit and provides personalized videos based on the user's viewing and emotion history. If it determines that the user enjoys a particular piece of content, it will also recommend other interesting videos related to that content.

[0947] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0948] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0949] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0950] [Third embodiment]

[0951] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0952] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0953] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0954] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0955] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0956] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0957] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0958] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0959] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0960] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0961] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0962] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0963] The system of the present invention allows users to efficiently monitor multiple video distribution channels and quickly identify content they are interested in. This system is primarily composed of a user terminal, a server, and a generative AI model.

[0964] System Overview

[0965] The basic operation of the system is that users subscribe to video distribution channels, and when new videos are uploaded, the system automatically detects them, analyzes the important parts, and generates short videos. This allows users to quickly get an overview of the video through the short video, and if they are interested, they can watch the original full-length video.

[0966] User terminal operation

[0967] 1. User registration and login:

[0968] A user installs an application and creates a user account.

[0969] You can log in by entering your authentication information on the login screen.

[0970] 2. Video streaming channel registration:

[0971] After logging in, users enter the URL and name of the video distribution channel they wish to register and register it in the system.

[0972] 3. View short videos:

[0973] Receive notifications of short videos generated by the system.

[0974] The user views the short video and gets an overview of it.

[0975] 4. Watching full-length videos:

[0976] Click on the link for the short video that interests you to go to the original full-length video page to watch it.

[0977] Server Operation

[0978] 1. User and channel information management:

[0979] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database.

[0980] 2. New video detection:

[0981] It periodically checks subscribed channels to detect newly uploaded videos, using external APIs such as the YouTube API.

[0982] 3. Download and analyze the video:

[0983] The detected new videos are downloaded to the server and their content is analyzed using a generative AI model.

[0984] 4. Short video generation:

[0985] Based on the key parts captured by the generative AI model, a short video is automatically generated, which includes elements such as a thumbnail, text, and narration.

[0986] 5. Metadata management for short videos:

[0987] Metadata for the generated short videos is generated and stored in a database.

[0988] 6. Short Video Notifications:

[0989] The user is notified that a short video has been generated and is made available for viewing.

[0990] Specific examples

[0991] For example, if user A is interested in cooking, he / she registers multiple cooking channels in the system. The server periodically checks these channels and detects when new cooking videos have been uploaded. Suppose the new video is "Easy dinner recipes you can make in 30 minutes."

[0992] The server downloads the video and analyzes it using a generative AI model, which extracts the key cooking steps and generates a short video of about five minutes, complete with thumbnails of the finished dish and a brief explanation of each step.

[0993] User A views this short video, quickly grasps the content, and if interested, watches the original 30-minute video. This process allows User A to efficiently select content that interests them and make effective use of their time.

[0994] Based on the above details, the system of the present invention provides an innovative means for users to quickly and effectively grasp video content that interests them and efficiently obtain the information they need.

[0995] The processing flow will be explained below.

[0996] Step 1: User logs in

[0997] The user starts the application on the device and enters authentication information (user name, password, etc.) on the login screen. When the user clicks the "Login" button, the device sends the entered authentication information to the server. The server verifies the authentication information in the database, and if authentication is successful, issues a session ID and returns it to the device.

[0998] Step 2: User subscribes to a video streaming channel

[0999] The user opens the channel subscription UI within the application and enters the URL and name of the video streaming channel they want to subscribe to. When the user clicks the "Subscribe" button, the device sends the entered channel information to the server. The server stores the received channel information in a database and notifies the user that the subscription was successful.

[1000] Step 3: Server detects new video

[1001] The server periodically (for example, every hour) retrieves a list of registered video distribution channels. The server uses the YouTube API to check for new videos on each channel. If a new video is found, it stores the video information (title, URL, posting date, etc.) in the database.

[1002] Step 4: The server downloads the new video

[1003] The server downloads the video data from the new video URL and stores the downloaded video data in a temporary location.

[1004] Step 5: The server analyzes the video using a generative AI model

[1005] The server loads the downloaded video data into the generative AI model as input, which then analyzes the content of the video and extracts important parts (keyframes, text information, audio information, etc.).

[1006] Step 6: The server generates the short video

[1007] The server automatically generates a short video based on the extracted key parts, adding a thumbnail, a text description, and optionally a narration to the short video, and stores the generated short video on the server.

[1008] Step 7: The server generates metadata for the short video

[1009] The server generates metadata about the short video (such as the original video URL, title, and creation date and time) and stores it in a database.

[1010] Step 8: The server notifies the user of the generated short video

[1011] The server notifies the corresponding user that a new short video has been generated, and the terminal receives the notification and displays to the user that a short video is available.

[1012] Step 9: User Views Short Video

[1013] The user clicks on the notification on their device to open the short video playback screen. The device streams or downloads the short video data from the server and plays it. The user watches the short video and gets an overview.

[1014] Step 10: User watches a full-length video

[1015] If a user is interested in the summary of a short video after watching it, they can click on the link to the original full-length video. The device will open the original full-length video page on YouTube via a browser or other device, and the user can watch the full-length video.

[1016] Example 1

[1017] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1018] Conventional methods for viewing video content have made it difficult for users to efficiently identify videos that interest them. In particular, there has been a lack of a way to quickly review only the important parts of a large number of video distribution channels or long video content. This has resulted in the problem of requiring users to spend a huge amount of time. There has also been a demand for a method for quickly identifying the content of newly posted videos.

[1019] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1020] In this invention, the server includes means for users to register multiple media distribution channels, means for automatically detecting newly posted video data, means for analyzing the content of the new video data using the generated artificial intelligence model and extracting important parts, means for automatically generating short video data based on the extracted important parts, means for generating and saving metadata related to the generated short video data, means for sending a notification of the generated short video data to a user terminal using a push notification system, and means for providing the generated short video data to the user. This allows users to efficiently understand content that interests them and quickly check important parts, significantly reducing the time required for video viewing.

[1021] "User" refers to a person who uses the system, subscribes to a video distribution channel, and views the generated short videos.

[1022] A "media distribution channel" is an online platform that provides video content, such as YouTube or Vimeo.

[1023] "Video Data" means video content held in electronic format, including any combination of still images, audio, and text.

[1024] A "server" is a computer system that receives requests from users and detects, analyzes, stores, and provides video data.

[1025] A "generated artificial intelligence model" is a model that uses a pre-trained machine learning algorithm and is used to analyze video content and extract important parts.

[1026] "Important portion extraction" is the process of identifying and extracting portions of video data that are likely to be of particular interest to the user.

[1027] "Short video data" refers to a short edited video generated based on the important parts, and is created to provide an overview to the user.

[1028] "Metadata" refers to information related to short video data, including title, summary, thumbnail, timestamp, and the like.

[1029] A "push notification system" is a technology or service that allows a server to send notifications to a user terminal in real time.

[1030] The system of the present invention is designed to enable users to efficiently monitor multiple media distribution channels and quickly identify content they are interested in. The system is comprised of a user terminal, a server, and a generative AI model.

[1031] First, a user installs the application on their device and creates a user account. To create an account, they must enter information such as a username, password, and email address. The user then logs in using these credentials. After logging in, the user registers with the system by entering the URL and name of a media distribution channel. For example, this could be a channel like YouTube or Vimeo.

[1032] The server uses a database such as MySQL or PostgreSQL to store and manage user authentication information and information about registered media distribution channels. The server periodically checks whether new videos have been uploaded to registered channels using the YouTube API or Vimeo API. When a new video is detected, it obtains the video's metadata (title, upload date and time, URL, etc.) and downloads it to the server using YouTube-dl or FFmpeg.

[1033] The server inputs the downloaded video data into a generative AI model (e.g., GPT-3, BERT). It sends the following prompt to the generative AI model:

[1034] Analyze a video for "Easy Dinner Recipes that Can Be Made in 30 Minutes" and extract the key cooking steps. Create a short video of approximately 5 minutes based on the extracted steps. Use an image of the finished dish as the thumbnail and add a brief description of each step.

[1035] The generative AI model follows these prompts to analyze the video content and extract key parts. Once the key parts are extracted, a short video is generated using software such as OpenCV and MoviePy. The short video may include a thumbnail, subtitles, and narration.

[1036] The metadata of the generated short video data (title, summary, thumbnail, etc.) is saved back to the database. The server uses Firebase Cloud Messaging (FCM) to send a notification of the generated short video data to the user's device. The notification is displayed on the user's device, and a list of new short videos is displayed in the "Notifications" tab in the app. The user can click the notification to play the short video in the app's video player (e.g., ExoPlayer).

[1037] As a concrete example, consider the case where User B is interested in the latest technology news. User B registers multiple technology media distribution channels in the system. The server periodically checks these channels and discovers that a new video has been uploaded with the title "Explaining the latest advances in AI technology." The server downloads this video and analyzes it with a generative AI model. The generative AI model extracts the "explanatory parts about important technologies" and edits them into a short video of about 5 minutes. This short video includes subtitles that provide an overview of the technology and relevant screenshots.

[1038] A notification is sent to User B's device and displayed as a new short video within the app. User B plays the short video and gets a quick overview of the progress of AI technology. Intrigued, User B clicks on the link to the original full-length video to watch in more detail.

[1039] This process allows users to efficiently select content that interests them and make effective use of their time. The system of the present invention provides an innovative means for users to quickly and effectively find video content that interests them and efficiently obtain the information they need.

[1040] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1041] Step 1: User registration and login

[1042] Specific behavior:

[1043] Input: A user installs an application on their device and creates an account by entering a username, password, and email address.

[1044] Data processing / calculation: The server receives the entered authentication information and stores it in a database such as MySQL or PostgreSQL. When logging in, the entered authentication information is compared with the information in the database.

[1045] Output: Once the correct credentials are entered, the user can log into their account. After logging in, the user's dashboard will be displayed.

[1046] Step 2: Register your video distribution channel

[1047] Specific behavior:

[1048] Input: After logging in, the user enters the URL and name of the video distribution channel and clicks the "Register" button.

[1049] Data processing / calculation: The server receives the transmitted channel information and stores it in a database.

[1050] Output: The registered channel information is saved in the database and will be checked from next time onwards.

[1051] Step 3: Detect and download new videos

[1052] Specific behavior:

[1053] Input: The server periodically checks for new videos using the YouTube API and / or Vimeo API, based on the channel information stored in the database.

[1054] Data processing / calculation: When a new video is detected, the video's metadata (title, upload date and time, URL, etc.) is obtained, and the video data is downloaded to the server using YouTube-dl or FFmpeg.

[1055] Output: The downloaded video data is saved on the server.

[1056] Step 4: Analyze the video and generate a short video

[1057] Specific behavior:

[1058] Input: Video data stored on the server is fed into a generative AI model (e.g., GPT-3, BERT) using a prompt like this:

[1059] Analyze a video for "Easy Dinner Recipes that Can Be Made in 30 Minutes" and extract the key cooking steps. Create a short video of approximately 5 minutes based on the extracted steps. Use an image of the finished dish as the thumbnail and add a brief description of each step.

[1060] Data processing / computation: The generative AI model analyzes the video data and extracts key parts. Based on the results, short video data is generated using OpenCV and MoviePy. The short video may include subtitles, thumbnails, and narration.

[1061] Output: The generated short video data is saved on the server.

[1062] Step 5: Notification and viewing of short videos

[1063] Specific behavior:

[1064] Input: The metadata of the generated short video data (title, summary, thumbnail, etc.) is stored in a database.

[1065] Data processing / calculation: The server uses Firebase Cloud Messaging (FCM) to send notifications of short video data to the user's device.

[1066] Output: A notification will be displayed on the user's device, and a list of new short videos will be displayed in the "Notifications" tab within the app.

[1067] Step 6: Watch a full-length video

[1068] Specific behavior:

[1069] Input: When the user clicks on the notification, a short video will play in the app's in-app video player (e.g. ExoPlayer).

[1070] Data processing / computation: While playing the short video, a link to the original full-length video is provided.

[1071] Output: If the user is interested, they can click on the link to the original full-length video, which will automatically open a browser and allow them to watch the full-length video.

[1072] The above are the specific processing steps of this system. The operations performed at each step and the accompanying data flow are explained in detail.

[1073] (Application example 1)

[1074] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1075] Conventional video streaming platforms make it difficult for users to efficiently grasp the content that interests them. In particular, there are limited ways to quickly understand the content of long videos before watching them. Furthermore, there is insufficient consideration given to optimizing the viewing experience on smart devices and wearable devices. This makes it difficult for users to make effective use of their time, and there is a demand for a way to quickly obtain the information they need.

[1076] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1077] In this invention, the server includes a means for allowing a user to register multiple video distribution channels, a means for automatically detecting newly posted videos, a means for analyzing the content of the new videos using a generative AI model and extracting important parts, a means for automatically generating short videos based on the extracted important parts, and a means for providing the generated short videos to the user via a smart device, particularly a mobile terminal or a wearable device, thereby enabling the user to quickly understand the content of content that interests them and to watch full-length videos as needed.

[1078] A "user" is an individual or a corporation that uses the system of the present invention to subscribe to a video distribution channel and watch short videos to understand the content of the videos.

[1079] "Video distribution channel" refers to a website or service that provides video content on the Internet and to which users can register content that interests them.

[1080] A "new video" is a video that has been newly uploaded to a video distribution channel and is detected by the system of the present invention.

[1081] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to analyze the content of a video and extract important parts.

[1082] A "mobile terminal" is a portable electronic device such as a smartphone or tablet, which is a device that a user uses to watch short videos.

[1083] A "wearable device" is an electronic device that is worn on the body, such as smart glasses or a smart watch, and is used by users to watch short videos.

[1084] "Short videos" are short videos that are automatically generated by extracting only the important parts of new videos analyzed using a generative AI model.

[1085] The "server" is a central system that manages information about video distribution channels registered by users, detects and analyzes new videos, and generates and provides short videos.

[1086] The system of the present invention allows users to efficiently find new videos from video distribution channels. This system is mainly composed of a server, a user terminal, and a generative AI model.

[1087] The server includes means for users to register multiple video distribution channels, means for automatically detecting newly posted videos, means for analyzing the content of new videos using a generative AI model and extracting important parts, means for automatically generating short videos based on the extracted important parts, and means for providing the generated short videos to users via smart devices, particularly mobile terminals and wearable devices.

[1088] System configuration

[1089] User terminal operation

[1090] 1. User registration and login:

[1091] Users install the application on their smart device (smartphone, tablet, etc.) and create an account. They can then log in by entering their authentication information on the login screen.

[1092] 2. Video streaming channel registration:

[1093] After logging in, users enter the URL and name of the video streaming channel they wish to subscribe to and register through the application.

[1094] 3. View short videos:

[1095] The server notifies the user of the short video, which the user can then view on a smart device or wearable device (such as smart glasses or a smartwatch) by tapping the notification.

[1096] 4. Watching full-length videos:

[1097] Tap the link of the short video that interests you to go to the original full-length video page to watch it.

[1098] Server Operation

[1099] 1. User and channel information management:

[1100] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database (MongoDB).

[1101] 2. New video detection:

[1102] Registered channels are checked periodically and newly uploaded videos are detected using external APIs such as the YouTube Data API.

[1103] 3. Download and analyze the video:

[1104] The detected new videos are downloaded to the server and the content of the videos is analyzed using a generative AI model (OpenAI GPT).

[1105] 4. Short video generation:

[1106] Based on the key parts captured by the generative AI model, a short video is automatically generated, which includes a summary, thumbnail, and text information.

[1107] 5. Notification and provision of short videos:

[1108] Using Firebase Cloud Messaging (FCM), the user is notified that a short video has been created and can watch it.

[1109] Specific examples

[1110] For example, if a user subscribes to cooking channels, the server periodically checks these channels to detect when new cooking videos have been uploaded. Suppose the new video is an "easy dinner recipe that can be made in 30 minutes." The server downloads this video and analyzes it using a generative AI model. As a result, the important cooking steps are extracted and generated into a short video of about 5 minutes. This short video includes a thumbnail of the finished dish and a brief explanation of each step.

[1111] Examples of prompts include:

[1112] Create a short video by extracting only the most important parts from the following videos:

[1113] 1. Intro

[1114] 2. Ingredient List

[1115] 3.Cooking Steps (Step by Step)

[1116] 4. Final Dish Presentation

[1117] This allows users to view short videos, quickly grasp the content, and then watch the full 30-minute video if they are interested. This process allows users to efficiently select content that interests them and make effective use of their time.

[1118] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1119] Step 1:

[1120] The server manages user registration and authentication information. A user creates an account through a smart device and enters their email address and password. The entered data is stored in the server's MongoDB database. The output is a user ID and an authentication token.

[1121] Step 2:

[1122] After logging in, a user registers multiple video distribution channels. The user inputs the channel URL and name from their device and sends this to the server. The server stores this information in a database. The input is the channel URL and name, and the output is updating the database.

[1123] Step 3:

[1124] The server periodically checks all registered channels to see if new videos have been uploaded. This check is performed using the YouTube Data API. The input is channel information, and the output is metadata for new videos.

[1125] Step 4:

[1126] When a new video is detected, the server downloads it and stores it in the server's local storage using the video's URL. The input is the URL of the new video, and the output is the video file.

[1127] Step 5:

[1128] The server uses a generative AI model to analyze the content of the downloaded video. As a specific example, it generates a prompt sentence and inputs it into the generative AI model. The generative AI model extracts important parts and returns the text analysis results. The input is the video content and the prompt sentence, and the output is the analysis results.

[1129] Step 6:

[1130] The server automatically generates a short video based on the analysis results. Using video editing software, the extracted important parts are cut out and edited. The input is the analysis results and the original video file, and the output is the short video file.

[1131] Step 7:

[1132] The server generates metadata about the generated short video and stores it in a database. The metadata includes the video title, summary, length, URL, etc. The input is the short video file, and the output is the metadata and its storage.

[1133] Step 8:

[1134] The server uses Firebase Cloud Messaging (FCM) to notify the user that the generated short video is available for viewing. The notification includes the title and link of the short video. The input is notification information, and the output is a notification to the user device.

[1135] Step 9:

[1136] Users receive a notification on their smart device or wearable device and watch a short video. They can access the original full-length video by tapping the link in the short video they watched. The input is the notification message, and the output is the viewing of the short video and access to the original full-length video.

[1137] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1138] The system of the present invention efficiently manages video distribution channels and quickly identifies content that users are interested in. Furthermore, by combining it with an emotion engine that recognizes user emotions, it realizes more personalized video delivery.

[1139] System Overview

[1140] The system allows users to register multiple video distribution channels, automatically detects newly posted videos, and analyzes their content to generate short videos. This allows users to quickly get an overview of the video and watch only those videos that interest them. Furthermore, by incorporating an emotion engine, the system can recognize users' emotions in real time and adjust and recommend video content accordingly.

[1141] User terminal operation

[1142] 1. User registration and login:

[1143] A user installs the application, creates an account, and then enters their authentication information on the login screen to log in.

[1144] 2. Video streaming channel registration:

[1145] After logging in, users enter the URL and name of their video distribution channel and register it in the system.

[1146] 3. View short videos:

[1147] Receive notifications of short videos generated by the system and view the short videos.

[1148] 4. Watching full-length videos:

[1149] If you are interested in the short video, click on the link to the original full-length video and watch it.

[1150] Server Operation

[1151] 1. User and channel information management:

[1152] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database.

[1153] 2. New video detection:

[1154] It periodically checks subscribed channels to detect newly uploaded videos. It uses the YouTube API to check for new videos and stores that information in a database.

[1155] 3. Download and analyze the video:

[1156] New videos are downloaded to the server and analyzed using the generative AI model.

[1157] 4. Short video generation:

[1158] A generative AI model analyzes the content of a video, extracts key parts, and automatically generates a short video that includes a thumbnail, text, and narration.

[1159] 5. Metadata management for short videos:

[1160] The metadata of the generated short videos is stored and managed in a database.

[1161] 6. Short Video Notifications:

[1162] The user is notified that a short video has been generated and is made available for viewing.

[1163] Emotion Engine Operation

[1164] 1. User Emotion Recognition:

[1165] While the user is watching a short video, the emotion engine uses cameras and sensors to analyze the user's facial expressions and voice, collecting emotional data in real time.

[1166] 2. Sentiment Data Analysis:

[1167] The server analyzes the collected emotional data to understand the user's emotions while watching, thereby evaluating the user's interest and satisfaction.

[1168] 3. Adjustment of short video content:

[1169] Based on the analysis results of the emotion engine, the server adjusts the content of the short video in real time, specifically by emphasizing elements that attract the user's interest and omitting unnecessary parts.

[1170] 4. Personalized Recommendations:

[1171] Based on past emotional data, the server recommends the most suitable short videos for the user. It analyzes the user's emotional history and prioritizes videos with similar emotional patterns.

[1172] Specific examples

[1173] For example, if user B is interested in music, he / she can register multiple music channels in the system. The server periodically checks these channels and detects when new music videos are uploaded. In this case, the new videos are called "latest hit songs."

[1174] The server downloads the new video and analyzes it with a generative AI model, which extracts key parts of the song and generates a short video of about three minutes, including a thumbnail of the song and a description of the key lyrics.

[1175] User B watches the short video, and the emotion engine analyzes his facial expressions and reactions. If it detects that User B is particularly enjoying the song, other videos related to the song will be recommended based on the emotion engine's analysis. As a result, User B can consistently access content that interests him, improving his viewing experience.

[1176] Based on the above details, the system of the present invention takes into account the user's emotions and provides video content that meets individual needs, thereby realizing an efficient and effective video viewing experience.

[1177] The processing flow will be explained below.

[1178] Step 1: User installs the application on their local device and creates an account

[1179] The user downloads and installs the application on their device. After installation, they enter the required information (name, email address, password, etc.) on the account creation screen to create a new account. When they click the "Register" button, the device sends the entered information to the server, which then saves the account information in a database.

[1180] Step 2: User logs in

[1181] The user starts the application and enters authentication information (email address, password) on the login screen. When the "Login" button is clicked, the device sends the entered authentication information to the server. The server collates the information with the authentication information in the database, and if authentication is successful, returns a session ID to the device.

[1182] Step 3: User subscribes to a video streaming channel

[1183] After logging in, the user opens the "Subscribe to Channel" section in the application menu, enters the URL or name of the video streaming channel they want to subscribe to, and clicks the "Subscribe" button. The device sends the entered channel information to the server, which then stores it in a database.

[1184] Step 4: The server periodically detects new videos

[1185] The server periodically (for example, every hour) retrieves a list of registered video distribution channels from the database. It checks each channel using the YouTube API or similar to see if new videos have been posted. If a new video is found, it saves the video information (title, URL, posting date, etc.) in the database and proceeds to the next step.

[1186] Step 5: The server downloads the new video

[1187] The server downloads the video data using the URL of the new video, and then stores it in a temporary location.

[1188] Step 6: The server analyzes the video using the generated AI model

[1189] The server inputs the temporarily stored video data into the generative AI model, which then analyzes the video content and extracts important parts (keyframes, text, audio information, etc.).

[1190] Step 7: The server generates the short video

[1191] The server uses the extracted key parts to automatically generate a short video, which includes a thumbnail, a text description, and optionally a narration, and stores the generated short video on the server.

[1192] Step 8: Recognize the user's emotions

[1193] While a user is viewing a short video, their facial expressions and voice are captured through the device's camera and microphone. The emotion engine analyzes this data in real time to determine the user's emotional state (e.g., joy, interest, indifference, etc.).

[1194] Step 9: The server collects the emotion data and adjusts the content of the short video.

[1195] The device sends the collected emotional data to a server, which then analyzes the data and adjusts the content of the short video in real time based on the user's reaction. For example, it may expand the parts that the user found interesting and omit the parts that the user did not find interesting.

[1196] Step 10: The server manages the metadata for the short video

[1197] The server generates metadata including the generated short video and adjustment information based on the emotion data, and stores the metadata in a database. This metadata includes the original video URL, title, creation date and time, emotional state, etc.

[1198] Step 11: The server notifies the user of the short video

[1199] The server notifies the user that a new short video has been generated. The device receives the notification and displays to the user that a short video is available.

[1200] Step 12: User views the short video and, if desired, watches the full-length video

[1201] The user clicks on the notification on their device to open the short video playback screen. The device streams or downloads the short video data from the server and plays it. The user watches the short video and gets an overview of it. If they are interested, they click on the link to the original full-length video and watch it on YouTube.

[1202] This series of processes allows users to efficiently select content that interests them and improve their viewing experience.By introducing an emotion engine, the system can provide optimal content according to the user's emotions.

[1203] Example 2

[1204] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1205] Conventional video viewing systems have difficulty efficiently discovering content that interests users and analyzing emotions toward that content to provide personalized recommendations. Furthermore, viewing a full-length video in its entirety takes a long time, and there is a high risk of viewing content that does not match the user's interests. There is a need to address these issues.

[1206] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1207] In this invention, the server includes means for users to register multiple media, means for automatically detecting newly posted data, means for analyzing the content of the new data using the generated artificial intelligence model and extracting important parts, means for automatically generating short data based on the extracted important parts, means for providing the generated short data to the user, means for collecting and analyzing user emotion data, means for adjusting the content of the short data based on the emotion data, and means for making personalized recommendations. This allows users to efficiently discover content that interests them and receive personalized recommendations, thereby improving their viewing experience.

[1208] "User" refers to a person who uses the system to register, view, and rate video content.

[1209] "Media" refers to video distribution channels and content sources.

[1210] "Data" refers to the information that the system processes and analyzes, such as video, metadata, and emotional data.

[1211] "Artificial intelligence model" refers to machine learning algorithms and deep learning models used to analyze and generate content and recognize emotions.

[1212] "Short data" refers to a short video clip created by extracting only the important parts of the generated video.

[1213] "Emotion data" refers to information about emotions acquired by analyzing the user's facial expressions and voice.

[1214] "Personalized recommendations" refers to content that is individually recommended to users based on their interests, past viewing history, and emotional data.

[1215] "Metadata" refers to additional information such as title, summary, and playback time related to the generated short data.

[1216] The system of the present invention allows users to efficiently manage video content and quickly find content they are interested in. The system is implemented as follows.

[1217] User terminal operation

[1218] The user terminal functions as an interface for users to access the system. Users begin using the system by installing the application and creating an account. After logging in, users register video distribution channels that interest them. When a new video is posted to a registered channel, the system generates a short video and notifies the user. Users can watch the short video and, if interested, access the original full-length video.

[1219] Server Operation

[1220] The server is the central part of the system and plays the following roles:

[1221] 1. User and channel information management:

[1222] The server stores and manages the authentication information entered by the user and the registered channel information in a database, using a common database management system (e.g., MySQL).

[1223] 2. New video detection:

[1224] The server periodically checks subscribed channels (e.g., using a scheduler cron job) and uses an API (e.g., YouTube API) to detect newly uploaded videos.

[1225] 3. Download and analyze the video:

[1226] When a new video is detected, the server downloads the video, stores it locally using a Python library (e.g., pytube), and then analyzes the video using the generated AI model.

[1227] 4. Short video generation:

[1228] The generative AI model analyzes the video and extracts key parts. Specifically, it uses OpenCV to analyze the video frames, and then uses a natural language processing library (e.g., NLTK) to generate narration and text, automatically generating a short video.

[1229] 5. Metadata management for short videos:

[1230] Metadata about the generated short videos (e.g., title, summary, and playback time) is stored and managed in a database.

[1231] 6. Short Video Notifications:

[1232] The server uses a push notification service (e.g., Firebase Cloud Messaging) to notify the user that a short video has been generated.

[1233] Emotion Engine Operation

[1234] The emotion engine plays an important role in enhancing the user's viewing experience.

[1235] 1. User Emotion Recognition:

[1236] While the user is watching a short video, facial expression and voice data is collected using the device's camera and microphone, using OpenCV and the Emotion API.

[1237] 2. Sentiment Data Analysis:

[1238] The server analyzes the collected emotion data in real time, using an emotion analysis library (e.g., DeepFace) to identify emotions from the user's facial expressions and stores the results in a database.

[1239] 3. Adjustment of short video content:

[1240] Based on the emotion data, the server sends prompts to the generative AI model to adjust the content of the short video in real time, such as "include more frames in which the user is smiling."

[1241] 4. Personalized Recommendations:

[1242] The server recommends the most suitable short videos to users based on their past emotional data. It uses a machine learning model using Python libraries (e.g., scikit-learn) to analyze users' emotional patterns and provide the most suitable content.

[1243] Specific examples

[1244] For example, if user B is interested in music, he / she registers multiple music channels in the system. The server periodically checks these channels and detects when new music videos are uploaded. In this case, let's say the new videos are "latest hit songs."

[1245] The server downloads this new video and runs it through a generative AI model, which extracts key parts of the song and generates a short video of about three minutes, including a thumbnail of the song and a description of the key lyrics.

[1246] User B watches the short video while the emotion engine analyzes his facial expressions and reactions. If it detects that User B is particularly enjoying the song, other videos related to the song will be recommended based on the emotion engine's analysis. As a result, User B can consistently access content that interests them, improving their viewing experience.

[1247] Prompt Sentence Examples

[1248] "Extract the key parts of the music video 'The Latest Hit Song' and edit them into a short video of approximately 3 minutes. Please include a thumbnail and explanations of the key lyrics."

[1249] This concludes the description of the embodiment of the invention. The system efficiently and effectively manages video content while incorporating user emotional data, making it possible to provide a viewing experience tailored to individual needs.

[1250] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1251] Step 1:

[1252] User Registration and Login

[1253] Input: The user enters information such as a name, email address, or password.

[1254] Processing: Information entered from the user's terminal is sent to the server, which then stores the user information in a database.

[1255] Output: The user account is created and you can log in.

[1256] Specific operation: The user installs the application and presses the "New Registration" button. They enter the required information and click the "Register" button. The server receives the user information and saves it in the database. The user enters their email address and password on the login screen and presses the "Login" button to complete the login.

[1257] Step 2:

[1258] Video streaming channel registration

[1259] Input: The user enters the URL or name of a video streaming channel.

[1260] Processing: The channel information entered from the user terminal is sent to the server, which then stores the channel information in a database.

[1261] Output: Video distribution channels are registered and managed in the system.

[1262] Specific operation: After logging in, the user goes to the "Subscribe to Channel" screen, enters the URL and name of the video distribution channel, and presses the "Subscribe" button. The server receives the channel information and stores it in the database.

[1263] Step 3:

[1264] New video detection

[1265] Input: Information about the registered video streaming channel.

[1266] Processing: The server periodically checks the channel (e.g., using a scheduler cron job) and uses the YouTube API to detect new videos.

[1267] Output: The information of the newly uploaded video is saved in the database.

[1268] Specific operation: The server periodically sends an API request based on the channel information to check for new videos. If a new video is found, the video information is saved in the database.

[1269] Step 4:

[1270] Downloading and analyzing videos

[1271] Input: The URL of the new video.

[1272] Processing: The server downloads the video, analyzes it with the generative AI model, and saves the video locally using a Python library (e.g., pytube).

[1273] Output: Analysis results from the generative AI model.

[1274] Specific operation: The server obtains the URL of the new video and starts downloading it. After the download is complete, the video is input into the generative AI model for analysis.

[1275] Step 5:

[1276] Short video generation

[1277] Input: Analysis results of the generative AI model (important part).

[1278] Processing: The server generates a short video based on key parts obtained from the generative AI model, performs frame analysis using OpenCV, and generates narration and text using a natural language processing library (e.g., NLTK).

[1279] Output: Short video.

[1280] How it works: Based on the analysis results obtained from the generative AI model, the server automatically generates a short video, adding thumbnails, text, and narration as needed.

[1281] Step 6:

[1282] Metadata management for short videos

[1283] Input: Metadata of the generated short video (e.g. title, summary, duration).

[1284] Processing: The server stores the metadata in a database.

[1285] Output: The saved metadata.

[1286] Specific operation: The server obtains the metadata of the generated short video and stores it in a database.

[1287] Step 7:

[1288] Short video notifications

[1289] Input: The generated short video.

[1290] Processing: The server uses a push notification service to send a notification of the short video to the user's device, such as using Firebase Cloud Messaging.

[1291] Output: Notification to the user.

[1292] Specific operation: The server sends a notification via the notification service to inform the user that a short video has been created. A notification icon will appear on the user's device, and the user can click it to watch the short video.

[1293] Step 8:

[1294] User Emotion Recognition

[1295] Input: Facial expressions and voice while the user watches a short video.

[1296] Processing: The user device collects emotion data using a camera and microphone and sends it to the server using OpenCV and the Emotion API.

[1297] Output: Collected emotion data.

[1298] How it works: While a user is watching a short video, the device's camera and microphone collect facial and voice data, which is then sent to a server in real time.

[1299] Step 9:

[1300] Sentiment Data Analysis

[1301] Input: Collected emotion data.

[1302] Processing: The server uses an emotion analysis library (e.g. DeepFace) to identify emotions from the user's facial expressions and voice.

[1303] Output: Analysis results (user sentiment).

[1304] Specific operation: The server analyzes the received emotion data and identifies the emotion the user is feeling while watching the video. The analysis results are stored in a database.

[1305] Step 10:

[1306] Adjustment of short video content

[1307] Input: Analysis results (user sentiment).

[1308] Processing: The server sends prompts to the generative AI model to adjust the content of the short video.

[1309] Output: Adjusted short video.

[1310] How it works: The server generates prompts and sends them to the generative AI model. For example, by issuing instructions such as "include as many frames as possible in which the user is smiling," the model generates short videos tailored to the user's interests.

[1311] Step 11:

[1312] Personalized Recommendations

[1313] Input: Analysis results (user's emotion history).

[1314] Processing: The server uses a machine learning model (e.g., scikit-learn) to recommend the best short videos for the user.

[1315] Output: Recommended short videos.

[1316] How it works: The server analyzes the user's emotional patterns and recommends the most suitable videos based on past emotional data, allowing the system to continue providing users with content that is appealing to them.

[1317] (Application example 2)

[1318] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1319] Conventional content distribution services have the problem that it is difficult for users to efficiently find new videos from a large number of video distribution channels, and it takes a long time to find content that interests them. In addition, personalized video recommendations based on users' emotions and interests are insufficient, leaving a need for an improved viewing experience. To solve these issues, a system is needed that can recognize users' emotions and quickly provide videos that meet their individual needs.

[1320] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1321] In this invention, the server includes means for users to register multiple video distribution channels, means for automatically detecting newly posted videos, means for analyzing the content of the new videos using a generative artificial intelligence model and extracting important parts, means for automatically generating short videos based on the extracted important parts, means for providing the generated short videos to the user, means for recognizing user emotions in real time, and means for personalizing content based on the user emotions. This allows users to efficiently find video content that interests them, significantly improving their viewing experience.

[1322] "User" means an individual who uses a video distribution channel to view and manage content.

[1323] A "video distribution channel" is a medium that provides video content via the Internet.

[1324] "Newly posted videos" are video contents that have recently been uploaded to a video distribution channel.

[1325] A "generative artificial intelligence model" is a machine learning model that analyzes the content of videos based on data and extracts and generates important parts.

[1326] The "important parts" are scenes or information in the video that are expected to be of particular interest to the user.

[1327] A "short video" is a video that extracts important parts from the original video so that the content can be understood in a short amount of time.

[1328] "User emotion" refers to the emotional state shown by the user while viewing, and is inferred from facial expressions and voice.

[1329] "Personalizing content" means providing content that is individually optimized based on a user's emotions and viewing history.

[1330] To implement this invention, a system is required in which a smartphone is used as the main user terminal and which links with a server. Specific processing steps and the hardware and software used for these steps are described below.

[1331] User terminal operation

[1332] 1. User registration and login:

[1333] Users install the application on their smartphone and create an account.

[1334] After creating an account, the user enters their authentication information on the login screen to log in. Firebase Authentication is used for authentication.

[1335] 2. Video streaming channel registration:

[1336] After logging in, users enter the URL and name of their video streaming channel and register with the system through the application. This information is stored in the Firebase Database.

[1337] 3. View short videos:

[1338] The server generates a notification for the short video, and the user can view it on their smartphone. Notifications are sent via Firebase Cloud Messaging.

[1339] 4. Watching full-length videos:

[1340] If a user is interested in a short video, they can click on the link to the original full-length video to watch it.

[1341] Server Operation

[1342] 1. User and channel information management:

[1343] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in the Firebase Database.

[1344] 2. New video detection:

[1345] The server periodically checks registered video streaming channels using the YouTube Data API and Twitch API to detect newly uploaded videos, and stores this information in the Firebase Database.

[1346] 3. Download and analyze the video:

[1347] The server downloads the newly detected video and analyzes it using Google Cloud AI's Video Intelligence API.

[1348] 4. Short video generation:

[1349] A generative AI model extracts key parts of the video and generates a short video, including a thumbnail and key scenes. The generated short video is stored in Firebase Storage, and its metadata is stored in Firebase Database.

[1350] 5. Short Video Notifications:

[1351] The generated short video is sent to the user's device using Firebase Cloud Messaging.

[1352] 6. User Emotion Recognition:

[1353] The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice in real time, using the OpenCV library and Google Cloud Speech-to-Text API.

[1354] 7. Sentiment Data Analysis and Personalized Recommendations:

[1355] The emotional data collected by the server is analyzed using Firebase ML Kit, and videos are personalized based on the user's viewing history and emotional history.

[1356] Specific examples

[1357] For example, if user D is interested in entertainment-related videos, he / she registers multiple entertainment channels in the app. The server periodically checks these channels to detect new video content. Let's consider the case where a trailer for a new movie has been uploaded as a new video.

[1358] The server downloads new movie trailers and uses a generative AI model to extract key scenes. The resulting trailers are short videos of approximately three minutes, each containing thumbnails of the movie and key scenes.

[1359] While User D is watching this short video, the emotion engine analyzes his facial expressions and voice to recognize his excitement and interest. If User D is determined to be particularly interested, the server will also recommend other related entertainment videos.

[1360] Prompt Sentence Examples

[1361] "Analyze the latest movie trailers, extract key scenes, and generate a short video of less than 3 minutes. Include text information about the main cast and director."

[1362] In this way, users can efficiently find content that interests them, improving their viewing experience.

[1363] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1364] Step 1:

[1365] User registration and login:

[1366] Input: User authentication information (email address, password)

[1367] Output: Session information for the authenticated user

[1368] Specific behavior:

[1369] A user installs a smartphone application and creates an account. They then enter their credentials on the login screen and are authenticated using Firebase Authentication. If authentication is successful, a user session is created and the user is logged in to the application.

[1370] Step 2:

[1371] Subscribe to our video streaming channel:

[1372] Input: URL or name of the video distribution channel

[1373] Output: Registered channel information (channel ID, URL)

[1374] Specific behavior:

[1375] Users can register their video streaming channel by entering its URL and name in the application. The information is stored in the Firebase Database, and the channel ID and URL are associated.

[1376] Step 3:

[1377] New Video Detection:

[1378] Input: Registered video distribution channel information

[1379] Output: New video information (video ID, title, URL)

[1380] Specific behavior:

[1381] The server periodically checks registered video streaming channels using the YouTube Data API and Twitch API, detects newly uploaded videos, and stores that information in the Firebase Database.

[1382] Step 4:

[1383] Video download and analysis:

[1384] Input: New video information (video ID, URL)

[1385] Output: Data with important parts extracted

[1386] Specific behavior:

[1387] The server downloads a new video and analyzes it using Google Cloud AI's Video Intelligence API. A generative AI model is used to analyze the video content and extract key parts (scenes, audio, etc.).

[1388] Step 5:

[1389] Short video generation:

[1390] Input: Data with important parts extracted

[1391] Output: Generated short video file (video ID, short video URL)

[1392] Specific behavior:

[1393] The server generates a short video based on the extracted key parts. The generative AI model is given a prompt: "Analyze the content of the latest video, extract key scenes, and generate a short video of less than 3 minutes." The generated short video is stored in Firebase Storage, and its metadata (video ID, short video URL) is stored in Firebase Database.

[1394] Step 6:

[1395] Short Video Notifications:

[1396] Input: The generated short video file and its metadata

[1397] Output: Push notification to user device

[1398] Specific behavior:

[1399] The generated short video is pushed to the user's device using Firebase Cloud Messaging, and when the user clicks on the notification, the short video starts playing.

[1400] Step 7:

[1401] User Emotion Recognition:

[1402] Input: Facial expression and voice data while the user is watching a short video

[1403] Output: Analyzed user emotion data (excitement, joy, interest, etc.)

[1404] Specific behavior:

[1405] The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice in real time, and uses the OpenCV library and Google Cloud Speech-to-Text API to collect emotional data and estimate the user's emotional state.

[1406] Step 8:

[1407] Sentiment data analysis and personalized recommendations:

[1408] Input: Analyzed user emotion data and viewing history

[1409] Output: A personalized video recommendation list

[1410] Specific behavior:

[1411] The server analyzes the collected emotion data using Firebase ML Kit and provides personalized videos based on the user's viewing and emotion history. If it determines that the user enjoys a particular piece of content, it will also recommend other interesting videos related to that content.

[1412] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1413] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1414] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1415] [Fourth embodiment]

[1416] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1417] 7, a 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.

[1418] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1419] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1420] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1421] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1422] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1423] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1424] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1425] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1426] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1427] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1428] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1429] The system of the present invention allows users to efficiently monitor multiple video distribution channels and quickly identify content they are interested in. This system is primarily composed of a user terminal, a server, and a generative AI model.

[1430] System Overview

[1431] The basic operation of the system is that users subscribe to video distribution channels, and when new videos are uploaded, the system automatically detects them, analyzes the important parts, and generates short videos. This allows users to quickly get an overview of the video through the short video, and if they are interested, they can watch the original full-length video.

[1432] User terminal operation

[1433] 1. User registration and login:

[1434] A user installs an application and creates a user account.

[1435] You can log in by entering your authentication information on the login screen.

[1436] 2. Video streaming channel registration:

[1437] After logging in, users enter the URL and name of the video distribution channel they wish to register and register it in the system.

[1438] 3. View short videos:

[1439] Receive notifications of short videos generated by the system.

[1440] The user views the short video and gets an overview of it.

[1441] 4. Watching full-length videos:

[1442] Click on the link for the short video that interests you to go to the original full-length video page to watch it.

[1443] Server Operation

[1444] 1. User and channel information management:

[1445] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database.

[1446] 2. New video detection:

[1447] It periodically checks subscribed channels to detect newly uploaded videos, using external APIs such as the YouTube API.

[1448] 3. Download and analyze the video:

[1449] The detected new videos are downloaded to the server and their content is analyzed using a generative AI model.

[1450] 4. Short video generation:

[1451] Based on the key parts captured by the generative AI model, a short video is automatically generated, which includes elements such as a thumbnail, text, and narration.

[1452] 5. Metadata management for short videos:

[1453] Metadata for the generated short videos is generated and stored in a database.

[1454] 6. Short Video Notifications:

[1455] The user is notified that a short video has been generated and is made available for viewing.

[1456] Specific examples

[1457] For example, if user A is interested in cooking, he / she registers multiple cooking channels in the system. The server periodically checks these channels and detects when new cooking videos have been uploaded. Suppose the new video is "Easy dinner recipes you can make in 30 minutes."

[1458] The server downloads the video and analyzes it using a generative AI model, which extracts the key cooking steps and generates a short video of about five minutes, complete with thumbnails of the finished dish and a brief explanation of each step.

[1459] User A views this short video, quickly grasps the content, and if interested, watches the original 30-minute video. This process allows User A to efficiently select content that interests them and make effective use of their time.

[1460] Based on the above details, the system of the present invention provides an innovative means for users to quickly and effectively grasp video content that interests them and efficiently obtain the information they need.

[1461] The processing flow will be explained below.

[1462] Step 1: User logs in

[1463] The user starts the application on the device and enters authentication information (user name, password, etc.) on the login screen. When the user clicks the "Login" button, the device sends the entered authentication information to the server. The server verifies the authentication information in the database, and if authentication is successful, issues a session ID and returns it to the device.

[1464] Step 2: User subscribes to a video streaming channel

[1465] The user opens the channel subscription UI within the application and enters the URL and name of the video streaming channel they want to subscribe to. When the user clicks the "Subscribe" button, the device sends the entered channel information to the server. The server stores the received channel information in a database and notifies the user that the subscription was successful.

[1466] Step 3: Server detects new video

[1467] The server periodically (for example, every hour) retrieves a list of registered video distribution channels. The server uses the YouTube API to check for new videos on each channel. If a new video is found, it stores the video information (title, URL, posting date, etc.) in the database.

[1468] Step 4: The server downloads the new video

[1469] The server downloads the video data from the new video URL and stores the downloaded video data in a temporary location.

[1470] Step 5: The server analyzes the video using a generative AI model

[1471] The server loads the downloaded video data into the generative AI model as input, which then analyzes the content of the video and extracts important parts (keyframes, text information, audio information, etc.).

[1472] Step 6: The server generates the short video

[1473] The server automatically generates a short video based on the extracted key parts, adding a thumbnail, a text description, and optionally a narration to the short video, and stores the generated short video on the server.

[1474] Step 7: The server generates metadata for the short video

[1475] The server generates metadata about the short video (such as the original video URL, title, and creation date and time) and stores it in a database.

[1476] Step 8: The server notifies the user of the generated short video

[1477] The server notifies the corresponding user that a new short video has been generated, and the terminal receives the notification and displays to the user that a short video is available.

[1478] Step 9: User Views Short Video

[1479] The user clicks on the notification on their device to open the short video playback screen. The device streams or downloads the short video data from the server and plays it. The user watches the short video and gets an overview.

[1480] Step 10: User watches a full-length video

[1481] If a user is interested in the summary of a short video after watching it, they can click on the link to the original full-length video. The device will open the original full-length video page on YouTube via a browser or other device, and the user can watch the full-length video.

[1482] Example 1

[1483] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1484] Conventional methods for viewing video content have made it difficult for users to efficiently identify videos that interest them. In particular, there has been a lack of a way to quickly review only the important parts of a large number of video distribution channels or long video content. This has resulted in the problem of requiring users to spend a huge amount of time. There has also been a demand for a method for quickly identifying the content of newly posted videos.

[1485] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1486] In this invention, the server includes means for users to register multiple media distribution channels, means for automatically detecting newly posted video data, means for analyzing the content of the new video data using the generated artificial intelligence model and extracting important parts, means for automatically generating short video data based on the extracted important parts, means for generating and saving metadata related to the generated short video data, means for sending a notification of the generated short video data to a user terminal using a push notification system, and means for providing the generated short video data to the user. This allows users to efficiently understand content that interests them and quickly check important parts, significantly reducing the time required for video viewing.

[1487] "User" refers to a person who uses the system, subscribes to a video distribution channel, and views the generated short videos.

[1488] A "media distribution channel" is an online platform that provides video content, such as YouTube or Vimeo.

[1489] "Video Data" means video content held in electronic format, including any combination of still images, audio, and text.

[1490] A "server" is a computer system that receives requests from users and detects, analyzes, stores, and provides video data.

[1491] A "generated artificial intelligence model" is a model that uses a pre-trained machine learning algorithm and is used to analyze video content and extract important parts.

[1492] "Important portion extraction" is the process of identifying and extracting portions of video data that are likely to be of particular interest to the user.

[1493] "Short video data" refers to a short edited video generated based on the important parts, and is created to provide an overview to the user.

[1494] "Metadata" refers to information related to short video data, including title, summary, thumbnail, timestamp, and the like.

[1495] A "push notification system" is a technology or service that allows a server to send notifications to a user terminal in real time.

[1496] The system of the present invention is designed to enable users to efficiently monitor multiple media distribution channels and quickly identify content they are interested in. The system is comprised of a user terminal, a server, and a generative AI model.

[1497] First, a user installs the application on their device and creates a user account. To create an account, they must enter information such as a username, password, and email address. The user then logs in using these credentials. After logging in, the user registers with the system by entering the URL and name of a media distribution channel. For example, this could be a channel like YouTube or Vimeo.

[1498] The server uses a database such as MySQL or PostgreSQL to store and manage user authentication information and information about registered media distribution channels. The server periodically checks whether new videos have been uploaded to registered channels using the YouTube API or Vimeo API. When a new video is detected, it obtains the video's metadata (title, upload date and time, URL, etc.) and downloads it to the server using YouTube-dl or FFmpeg.

[1499] The server inputs the downloaded video data into a generative AI model (e.g., GPT-3, BERT). It sends the following prompt to the generative AI model:

[1500] Analyze a video for "Easy Dinner Recipes that Can Be Made in 30 Minutes" and extract the key cooking steps. Create a short video of approximately 5 minutes based on the extracted steps. Use an image of the finished dish as the thumbnail and add a brief description of each step.

[1501] The generative AI model follows these prompts to analyze the video content and extract key parts. Once the key parts are extracted, a short video is generated using software such as OpenCV and MoviePy. The short video may include a thumbnail, subtitles, and narration.

[1502] The metadata of the generated short video data (title, summary, thumbnail, etc.) is saved back to the database. The server uses Firebase Cloud Messaging (FCM) to send a notification of the generated short video data to the user's device. The notification is displayed on the user's device, and a list of new short videos is displayed in the "Notifications" tab in the app. The user can click the notification to play the short video in the app's video player (e.g., ExoPlayer).

[1503] As a concrete example, consider the case where User B is interested in the latest technology news. User B registers multiple technology media distribution channels in the system. The server periodically checks these channels and discovers that a new video has been uploaded with the title "Explaining the latest advances in AI technology." The server downloads this video and analyzes it with a generative AI model. The generative AI model extracts the "explanatory parts about important technologies" and edits them into a short video of about 5 minutes. This short video includes subtitles that provide an overview of the technology and relevant screenshots.

[1504] A notification is sent to User B's device and displayed as a new short video within the app. User B plays the short video and gets a quick overview of the progress of AI technology. Intrigued, User B clicks on the link to the original full-length video to watch in more detail.

[1505] This process allows users to efficiently select content that interests them and make effective use of their time. The system of the present invention provides an innovative means for users to quickly and effectively find video content that interests them and efficiently obtain the information they need.

[1506] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1507] Step 1: User registration and login

[1508] Specific behavior:

[1509] Input: A user installs an application on their device and creates an account by entering a username, password, and email address.

[1510] Data processing / calculation: The server receives the entered authentication information and stores it in a database such as MySQL or PostgreSQL. When logging in, the entered authentication information is compared with the information in the database.

[1511] Output: Once the correct credentials are entered, the user can log into their account. After logging in, the user's dashboard will be displayed.

[1512] Step 2: Register your video distribution channel

[1513] Specific behavior:

[1514] Input: After logging in, the user enters the URL and name of the video distribution channel and clicks the "Register" button.

[1515] Data processing / calculation: The server receives the transmitted channel information and stores it in a database.

[1516] Output: The registered channel information is saved in the database and will be checked from next time onwards.

[1517] Step 3: Detect and download new videos

[1518] Specific behavior:

[1519] Input: The server periodically checks for new videos using the YouTube API and / or Vimeo API, based on the channel information stored in the database.

[1520] Data processing / calculation: When a new video is detected, the video's metadata (title, upload date and time, URL, etc.) is obtained, and the video data is downloaded to the server using YouTube-dl or FFmpeg.

[1521] Output: The downloaded video data is saved on the server.

[1522] Step 4: Analyze the video and generate a short video

[1523] Specific behavior:

[1524] Input: Video data stored on the server is fed into a generative AI model (e.g., GPT-3, BERT) using a prompt like this:

[1525] Analyze a video for "Easy Dinner Recipes that Can Be Made in 30 Minutes" and extract the key cooking steps. Create a short video of approximately 5 minutes based on the extracted steps. Use an image of the finished dish as the thumbnail and add a brief description of each step.

[1526] Data processing / computation: The generative AI model analyzes the video data and extracts key parts. Based on the results, short video data is generated using OpenCV and MoviePy. The short video may include subtitles, thumbnails, and narration.

[1527] Output: The generated short video data is saved on the server.

[1528] Step 5: Notification and viewing of short videos

[1529] Specific behavior:

[1530] Input: The metadata of the generated short video data (title, summary, thumbnail, etc.) is stored in a database.

[1531] Data processing / calculation: The server uses Firebase Cloud Messaging (FCM) to send notifications of short video data to the user's device.

[1532] Output: A notification will be displayed on the user's device, and a list of new short videos will be displayed in the "Notifications" tab within the app.

[1533] Step 6: Watch a full-length video

[1534] Specific behavior:

[1535] Input: When the user clicks on the notification, a short video will play in the app's in-app video player (e.g. ExoPlayer).

[1536] Data processing / computation: While playing the short video, a link to the original full-length video is provided.

[1537] Output: If the user is interested, they can click on the link to the original full-length video, which will automatically open a browser and allow them to watch the full-length video.

[1538] The above are the specific processing steps of this system. The operations performed at each step and the accompanying data flow are explained in detail.

[1539] (Application example 1)

[1540] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1541] Conventional video streaming platforms make it difficult for users to efficiently grasp the content that interests them. In particular, there are limited ways to quickly understand the content of long videos before watching them. Furthermore, there is insufficient consideration given to optimizing the viewing experience on smart devices and wearable devices. This makes it difficult for users to make effective use of their time, and there is a demand for a way to quickly obtain the information they need.

[1542] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1543] In this invention, the server includes a means for allowing a user to register multiple video distribution channels, a means for automatically detecting newly posted videos, a means for analyzing the content of the new videos using a generative AI model and extracting important parts, a means for automatically generating short videos based on the extracted important parts, and a means for providing the generated short videos to the user via a smart device, particularly a mobile terminal or a wearable device, thereby enabling the user to quickly understand the content of content that interests them and to watch full-length videos as needed.

[1544] A "user" is an individual or a corporation that uses the system of the present invention to subscribe to a video distribution channel and watch short videos to understand the content of the videos.

[1545] "Video distribution channel" refers to a website or service that provides video content on the Internet and to which users can register content that interests them.

[1546] A "new video" is a video that has been newly uploaded to a video distribution channel and is detected by the system of the present invention.

[1547] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to analyze the content of a video and extract important parts.

[1548] A "mobile terminal" is a portable electronic device such as a smartphone or tablet, which is a device that a user uses to watch short videos.

[1549] A "wearable device" is an electronic device that is worn on the body, such as smart glasses or a smart watch, and is used by users to watch short videos.

[1550] "Short videos" are short videos that are automatically generated by extracting only the important parts of new videos analyzed using a generative AI model.

[1551] The "server" is a central system that manages information about video distribution channels registered by users, detects and analyzes new videos, and generates and provides short videos.

[1552] The system of the present invention allows users to efficiently find new videos from video distribution channels. This system is mainly composed of a server, a user terminal, and a generative AI model.

[1553] The server includes means for users to register multiple video distribution channels, means for automatically detecting newly posted videos, means for analyzing the content of new videos using a generative AI model and extracting important parts, means for automatically generating short videos based on the extracted important parts, and means for providing the generated short videos to users via smart devices, particularly mobile terminals and wearable devices.

[1554] System configuration

[1555] User terminal operation

[1556] 1. User registration and login:

[1557] Users install the application on their smart device (smartphone, tablet, etc.) and create an account. They can then log in by entering their authentication information on the login screen.

[1558] 2. Video streaming channel registration:

[1559] After logging in, users enter the URL and name of the video streaming channel they wish to subscribe to and register through the application.

[1560] 3. View short videos:

[1561] The server notifies the user of the short video, which the user can then view on a smart device or wearable device (such as smart glasses or a smartwatch) by tapping the notification.

[1562] 4. Watching full-length videos:

[1563] Tap the link of the short video that interests you to go to the original full-length video page to watch it.

[1564] Server Operation

[1565] 1. User and channel information management:

[1566] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database (MongoDB).

[1567] 2. New video detection:

[1568] Registered channels are checked periodically and newly uploaded videos are detected using external APIs such as the YouTube Data API.

[1569] 3. Download and analyze the video:

[1570] The detected new videos are downloaded to the server and the content of the videos is analyzed using a generative AI model (OpenAI GPT).

[1571] 4. Short video generation:

[1572] Based on the key parts captured by the generative AI model, a short video is automatically generated, which includes a summary, thumbnail, and text information.

[1573] 5. Notification and provision of short videos:

[1574] Using Firebase Cloud Messaging (FCM), the user is notified that a short video has been created and can watch it.

[1575] Specific examples

[1576] For example, if a user subscribes to cooking channels, the server periodically checks these channels to detect when new cooking videos have been uploaded. Suppose the new video is an "easy dinner recipe that can be made in 30 minutes." The server downloads this video and analyzes it using a generative AI model. As a result, the important cooking steps are extracted and generated into a short video of about 5 minutes. This short video includes a thumbnail of the finished dish and a brief explanation of each step.

[1577] Examples of prompts include:

[1578] Create a short video by extracting only the most important parts from the following videos:

[1579] 1. Intro

[1580] 2. Ingredient List

[1581] 3.Cooking Steps (Step by Step)

[1582] 4. Final Dish Presentation

[1583] This allows users to view short videos, quickly grasp the content, and then watch the full 30-minute video if they are interested. This process allows users to efficiently select content that interests them and make effective use of their time.

[1584] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1585] Step 1:

[1586] The server manages user registration and authentication information. A user creates an account through a smart device and enters their email address and password. The entered data is stored in the server's MongoDB database. The output is a user ID and an authentication token.

[1587] Step 2:

[1588] After logging in, a user registers multiple video distribution channels. The user inputs the channel URL and name from their device and sends this to the server. The server stores this information in a database. The input is the channel URL and name, and the output is updating the database.

[1589] Step 3:

[1590] The server periodically checks all registered channels to see if new videos have been uploaded. This check is performed using the YouTube Data API. The input is channel information, and the output is metadata for new videos.

[1591] Step 4:

[1592] When a new video is detected, the server downloads it and stores it in the server's local storage using the video's URL. The input is the URL of the new video, and the output is the video file.

[1593] Step 5:

[1594] The server uses a generative AI model to analyze the content of the downloaded video. As a specific example, it generates a prompt sentence and inputs it into the generative AI model. The generative AI model extracts important parts and returns the text analysis results. The input is the video content and the prompt sentence, and the output is the analysis results.

[1595] Step 6:

[1596] The server automatically generates a short video based on the analysis results. Using video editing software, the extracted important parts are cut out and edited. The input is the analysis results and the original video file, and the output is the short video file.

[1597] Step 7:

[1598] The server generates metadata about the generated short video and stores it in a database. The metadata includes the video title, summary, length, URL, etc. The input is the short video file, and the output is the metadata and its storage.

[1599] Step 8:

[1600] The server uses Firebase Cloud Messaging (FCM) to notify the user that the generated short video is available for viewing. The notification includes the title and link of the short video. The input is notification information, and the output is a notification to the user device.

[1601] Step 9:

[1602] Users receive a notification on their smart device or wearable device and watch a short video. They can access the original full-length video by tapping the link in the short video they watched. The input is the notification message, and the output is the viewing of the short video and access to the original full-length video.

[1603] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1604] The system of the present invention efficiently manages video distribution channels and quickly identifies content that users are interested in. Furthermore, by combining it with an emotion engine that recognizes user emotions, it realizes more personalized video delivery.

[1605] System Overview

[1606] The system allows users to register multiple video distribution channels, automatically detects newly posted videos, and analyzes their content to generate short videos. This allows users to quickly get an overview of the video and watch only those videos that interest them. Furthermore, by incorporating an emotion engine, the system can recognize users' emotions in real time and adjust and recommend video content accordingly.

[1607] User terminal operation

[1608] 1. User registration and login:

[1609] A user installs the application, creates an account, and then enters their authentication information on the login screen to log in.

[1610] 2. Video streaming channel registration:

[1611] After logging in, users enter the URL and name of their video distribution channel and register it in the system.

[1612] 3. View short videos:

[1613] Receive notifications of short videos generated by the system and view the short videos.

[1614] 4. Watching full-length videos:

[1615] If you are interested in the short video, click on the link to the original full-length video and watch it.

[1616] Server Operation

[1617] 1. User and channel information management:

[1618] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in a database.

[1619] 2. New video detection:

[1620] It periodically checks subscribed channels to detect newly uploaded videos. It uses the YouTube API to check for new videos and stores that information in a database.

[1621] 3. Download and analyze the video:

[1622] New videos are downloaded to the server and analyzed using the generative AI model.

[1623] 4. Short video generation:

[1624] A generative AI model analyzes the content of a video, extracts key parts, and automatically generates a short video that includes a thumbnail, text, and narration.

[1625] 5. Metadata management for short videos:

[1626] The metadata of the generated short videos is stored and managed in a database.

[1627] 6. Short Video Notifications:

[1628] The user is notified that a short video has been generated and is made available for viewing.

[1629] Emotion Engine Operation

[1630] 1. User Emotion Recognition:

[1631] While the user is watching a short video, the emotion engine uses cameras and sensors to analyze the user's facial expressions and voice, collecting emotional data in real time.

[1632] 2. Sentiment Data Analysis:

[1633] The server analyzes the collected emotional data to understand the user's emotions while watching, thereby evaluating the user's interest and satisfaction.

[1634] 3. Adjustment of short video content:

[1635] Based on the analysis results of the emotion engine, the server adjusts the content of the short video in real time, specifically by emphasizing elements that attract the user's interest and omitting unnecessary parts.

[1636] 4. Personalized Recommendations:

[1637] Based on past emotional data, the server recommends the most suitable short videos for the user. It analyzes the user's emotional history and prioritizes videos with similar emotional patterns.

[1638] Specific examples

[1639] For example, if user B is interested in music, he / she can register multiple music channels in the system. The server periodically checks these channels and detects when new music videos are uploaded. In this case, the new videos are called "latest hit songs."

[1640] The server downloads the new video and analyzes it with a generative AI model, which extracts key parts of the song and generates a short video of about three minutes, including a thumbnail of the song and a description of the key lyrics.

[1641] User B watches the short video, and the emotion engine analyzes his facial expressions and reactions. If it detects that User B is particularly enjoying the song, other videos related to the song will be recommended based on the emotion engine's analysis. As a result, User B can consistently access content that interests him, improving his viewing experience.

[1642] Based on the above details, the system of the present invention takes into account the user's emotions and provides video content that meets individual needs, thereby realizing an efficient and effective video viewing experience.

[1643] The processing flow will be explained below.

[1644] Step 1: User installs the application on their local device and creates an account

[1645] The user downloads and installs the application on their device. After installation, they enter the required information (name, email address, password, etc.) on the account creation screen to create a new account. When they click the "Register" button, the device sends the entered information to the server, which then saves the account information in a database.

[1646] Step 2: User logs in

[1647] The user starts the application and enters authentication information (email address, password) on the login screen. When the "Login" button is clicked, the device sends the entered authentication information to the server. The server collates the information with the authentication information in the database, and if authentication is successful, returns a session ID to the device.

[1648] Step 3: User subscribes to a video streaming channel

[1649] After logging in, the user opens the "Subscribe to Channel" section in the application menu, enters the URL or name of the video streaming channel they want to subscribe to, and clicks the "Subscribe" button. The device sends the entered channel information to the server, which then stores it in a database.

[1650] Step 4: The server periodically detects new videos

[1651] The server periodically (for example, every hour) retrieves a list of registered video distribution channels from the database. It checks each channel using the YouTube API or similar to see if new videos have been posted. If a new video is found, it saves the video information (title, URL, posting date, etc.) in the database and proceeds to the next step.

[1652] Step 5: The server downloads the new video

[1653] The server downloads the video data using the URL of the new video, and then stores it in a temporary location.

[1654] Step 6: The server analyzes the video using the generated AI model

[1655] The server inputs the temporarily stored video data into the generative AI model, which then analyzes the video content and extracts important parts (keyframes, text, audio information, etc.).

[1656] Step 7: The server generates the short video

[1657] The server uses the extracted key parts to automatically generate a short video, which includes a thumbnail, a text description, and optionally a narration, and stores the generated short video on the server.

[1658] Step 8: Recognize the user's emotions

[1659] While a user is viewing a short video, their facial expressions and voice are captured through the device's camera and microphone. The emotion engine analyzes this data in real time to determine the user's emotional state (e.g., joy, interest, indifference, etc.).

[1660] Step 9: The server collects the emotion data and adjusts the content of the short video.

[1661] The device sends the collected emotional data to a server, which then analyzes the data and adjusts the content of the short video in real time based on the user's reaction. For example, it may expand the parts that the user found interesting and omit the parts that the user did not find interesting.

[1662] Step 10: The server manages the metadata for the short video

[1663] The server generates metadata including the generated short video and adjustment information based on the emotion data, and stores the metadata in a database. This metadata includes the original video URL, title, creation date and time, emotional state, etc.

[1664] Step 11: The server notifies the user of the short video

[1665] The server notifies the user that a new short video has been generated. The device receives the notification and displays to the user that a short video is available.

[1666] Step 12: User views the short video and, if desired, watches the full-length video

[1667] The user clicks on the notification on their device to open the short video playback screen. The device streams or downloads the short video data from the server and plays it. The user watches the short video and gets an overview of it. If they are interested, they click on the link to the original full-length video and watch it on YouTube.

[1668] This series of processes allows users to efficiently select content that interests them and improve their viewing experience.By introducing an emotion engine, the system can provide optimal content according to the user's emotions.

[1669] Example 2

[1670] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1671] Conventional video viewing systems have difficulty efficiently discovering content that interests users and analyzing emotions toward that content to provide personalized recommendations. Furthermore, viewing a full-length video in its entirety takes a long time, and there is a high risk of viewing content that does not match the user's interests. There is a need to address these issues.

[1672] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1673] In this invention, the server includes means for users to register multiple media, means for automatically detecting newly posted data, means for analyzing the content of the new data using the generated artificial intelligence model and extracting important parts, means for automatically generating short data based on the extracted important parts, means for providing the generated short data to the user, means for collecting and analyzing user emotion data, means for adjusting the content of the short data based on the emotion data, and means for making personalized recommendations. This allows users to efficiently discover content that interests them and receive personalized recommendations, thereby improving their viewing experience.

[1674] "User" refers to a person who uses the system to register, view, and rate video content.

[1675] "Media" refers to video distribution channels and content sources.

[1676] "Data" refers to the information that the system processes and analyzes, such as video, metadata, and emotional data.

[1677] "Artificial intelligence model" refers to machine learning algorithms and deep learning models used to analyze and generate content and recognize emotions.

[1678] "Short data" refers to a short video clip created by extracting only the important parts of the generated video.

[1679] "Emotion data" refers to information about emotions acquired by analyzing the user's facial expressions and voice.

[1680] "Personalized recommendations" refers to content that is individually recommended to users based on their interests, past viewing history, and emotional data.

[1681] "Metadata" refers to additional information such as title, summary, and playback time related to the generated short data.

[1682] The system of the present invention allows users to efficiently manage video content and quickly find content they are interested in. The system is implemented as follows.

[1683] User terminal operation

[1684] The user terminal functions as an interface for users to access the system. Users begin using the system by installing the application and creating an account. After logging in, users register video distribution channels that interest them. When a new video is posted to a registered channel, the system generates a short video and notifies the user. Users can watch the short video and, if interested, access the original full-length video.

[1685] Server Operation

[1686] The server is the central part of the system and plays the following roles:

[1687] 1. User and channel information management:

[1688] The server stores and manages the authentication information entered by the user and the registered channel information in a database, using a common database management system (e.g., MySQL).

[1689] 2. New video detection:

[1690] The server periodically checks subscribed channels (e.g., using a scheduler cron job) and uses an API (e.g., YouTube API) to detect newly uploaded videos.

[1691] 3. Download and analyze the video:

[1692] When a new video is detected, the server downloads the video, stores it locally using a Python library (e.g., pytube), and then analyzes the video using the generated AI model.

[1693] 4. Short video generation:

[1694] The generative AI model analyzes the video and extracts key parts. Specifically, it uses OpenCV to analyze the video frames, and then uses a natural language processing library (e.g., NLTK) to generate narration and text, automatically generating a short video.

[1695] 5. Metadata management for short videos:

[1696] Metadata about the generated short videos (e.g., title, summary, and playback time) is stored and managed in a database.

[1697] 6. Short Video Notifications:

[1698] The server uses a push notification service (e.g., Firebase Cloud Messaging) to notify the user that a short video has been generated.

[1699] Emotion Engine Operation

[1700] The emotion engine plays an important role in enhancing the user's viewing experience.

[1701] 1. User Emotion Recognition:

[1702] While the user is watching a short video, facial expression and voice data is collected using the device's camera and microphone, using OpenCV and the Emotion API.

[1703] 2. Sentiment Data Analysis:

[1704] The server analyzes the collected emotion data in real time, using an emotion analysis library (e.g., DeepFace) to identify emotions from the user's facial expressions and stores the results in a database.

[1705] 3. Adjustment of short video content:

[1706] Based on the emotion data, the server sends prompts to the generative AI model to adjust the content of the short video in real time, such as "include more frames in which the user is smiling."

[1707] 4. Personalized Recommendations:

[1708] The server recommends the most suitable short videos to users based on their past emotional data. It uses a machine learning model using Python libraries (e.g., scikit-learn) to analyze users' emotional patterns and provide the most suitable content.

[1709] Specific examples

[1710] For example, if user B is interested in music, he / she registers multiple music channels in the system. The server periodically checks these channels and detects when new music videos are uploaded. In this case, let's say the new videos are "latest hit songs."

[1711] The server downloads this new video and runs it through a generative AI model, which extracts key parts of the song and generates a short video of about three minutes, including a thumbnail of the song and a description of the key lyrics.

[1712] User B watches the short video while the emotion engine analyzes his facial expressions and reactions. If it detects that User B is particularly enjoying the song, other videos related to the song will be recommended based on the emotion engine's analysis. As a result, User B can consistently access content that interests them, improving their viewing experience.

[1713] Prompt Sentence Examples

[1714] "Extract the key parts of the music video 'The Latest Hit Song' and edit them into a short video of approximately 3 minutes. Please include a thumbnail and explanations of the key lyrics."

[1715] This concludes the description of the embodiment of the invention. The system efficiently and effectively manages video content while incorporating user emotional data, making it possible to provide a viewing experience tailored to individual needs.

[1716] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1717] Step 1:

[1718] User Registration and Login

[1719] Input: The user enters information such as a name, email address, or password.

[1720] Processing: Information entered from the user's terminal is sent to the server, which then stores the user information in a database.

[1721] Output: The user account is created and you can log in.

[1722] Specific operation: The user installs the application and presses the "New Registration" button. They enter the required information and click the "Register" button. The server receives the user information and saves it in the database. The user enters their email address and password on the login screen and presses the "Login" button to complete the login.

[1723] Step 2:

[1724] Video streaming channel registration

[1725] Input: The user enters the URL or name of a video streaming channel.

[1726] Processing: The channel information entered from the user terminal is sent to the server, which then stores the channel information in a database.

[1727] Output: Video distribution channels are registered and managed in the system.

[1728] Specific operation: After logging in, the user goes to the "Subscribe to Channel" screen, enters the URL and name of the video distribution channel, and presses the "Subscribe" button. The server receives the channel information and stores it in the database.

[1729] Step 3:

[1730] New video detection

[1731] Input: Information about the registered video streaming channel.

[1732] Processing: The server periodically checks the channel (e.g., using a scheduler cron job) and uses the YouTube API to detect new videos.

[1733] Output: The information of the newly uploaded video is saved in the database.

[1734] Specific operation: The server periodically sends an API request based on the channel information to check for new videos. If a new video is found, the video information is saved in the database.

[1735] Step 4:

[1736] Downloading and analyzing videos

[1737] Input: The URL of the new video.

[1738] Processing: The server downloads the video, analyzes it with the generative AI model, and saves the video locally using a Python library (e.g., pytube).

[1739] Output: Analysis results from the generative AI model.

[1740] Specific operation: The server obtains the URL of the new video and starts downloading it. After the download is complete, the video is input into the generative AI model for analysis.

[1741] Step 5:

[1742] Short video generation

[1743] Input: Analysis results of the generative AI model (important part).

[1744] Processing: The server generates a short video based on key parts obtained from the generative AI model, performs frame analysis using OpenCV, and generates narration and text using a natural language processing library (e.g., NLTK).

[1745] Output: Short video.

[1746] How it works: Based on the analysis results obtained from the generative AI model, the server automatically generates a short video, adding thumbnails, text, and narration as needed.

[1747] Step 6:

[1748] Metadata management for short videos

[1749] Input: Metadata of the generated short video (e.g. title, summary, duration).

[1750] Processing: The server stores the metadata in a database.

[1751] Output: The saved metadata.

[1752] Specific operation: The server obtains the metadata of the generated short video and stores it in a database.

[1753] Step 7:

[1754] Short video notifications

[1755] Input: The generated short video.

[1756] Processing: The server uses a push notification service to send a notification of the short video to the user's device, such as using Firebase Cloud Messaging.

[1757] Output: Notification to the user.

[1758] Specific operation: The server sends a notification via the notification service to inform the user that a short video has been created. A notification icon will appear on the user's device, and the user can click it to watch the short video.

[1759] Step 8:

[1760] User Emotion Recognition

[1761] Input: Facial expressions and voice while the user watches a short video.

[1762] Processing: The user device collects emotion data using a camera and microphone and sends it to the server using OpenCV and the Emotion API.

[1763] Output: Collected emotion data.

[1764] How it works: While a user is watching a short video, the device's camera and microphone collect facial and voice data, which is then sent to a server in real time.

[1765] Step 9:

[1766] Sentiment Data Analysis

[1767] Input: Collected emotion data.

[1768] Processing: The server uses an emotion analysis library (e.g. DeepFace) to identify emotions from the user's facial expressions and voice.

[1769] Output: Analysis results (user sentiment).

[1770] Specific operation: The server analyzes the received emotion data and identifies the emotion the user is feeling while watching the video. The analysis results are stored in a database.

[1771] Step 10:

[1772] Adjustment of short video content

[1773] Input: Analysis results (user sentiment).

[1774] Processing: The server sends prompts to the generative AI model to adjust the content of the short video.

[1775] Output: Adjusted short video.

[1776] How it works: The server generates prompts and sends them to the generative AI model. For example, by issuing instructions such as "include as many frames as possible in which the user is smiling," the model generates short videos tailored to the user's interests.

[1777] Step 11:

[1778] Personalized Recommendations

[1779] Input: Analysis results (user's emotion history).

[1780] Processing: The server uses a machine learning model (e.g., scikit-learn) to recommend the best short videos for the user.

[1781] Output: Recommended short videos.

[1782] How it works: The server analyzes the user's emotional patterns and recommends the most suitable videos based on past emotional data, allowing the system to continue providing users with content that is appealing to them.

[1783] (Application example 2)

[1784] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1785] Conventional content distribution services have the problem that it is difficult for users to efficiently find new videos from a large number of video distribution channels, and it takes a long time to find content that interests them. In addition, personalized video recommendations based on users' emotions and interests are insufficient, leaving a need for an improved viewing experience. To solve these issues, a system is needed that can recognize users' emotions and quickly provide videos that meet their individual needs.

[1786] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1787] In this invention, the server includes means for users to register multiple video distribution channels, means for automatically detecting newly posted videos, means for analyzing the content of the new videos using a generative artificial intelligence model and extracting important parts, means for automatically generating short videos based on the extracted important parts, means for providing the generated short videos to the user, means for recognizing user emotions in real time, and means for personalizing content based on the user emotions. This allows users to efficiently find video content that interests them, significantly improving their viewing experience.

[1788] "User" means an individual who uses a video distribution channel to view and manage content.

[1789] A "video distribution channel" is a medium that provides video content via the Internet.

[1790] "Newly posted videos" are video contents that have recently been uploaded to a video distribution channel.

[1791] A "generative artificial intelligence model" is a machine learning model that analyzes the content of videos based on data and extracts and generates important parts.

[1792] The "important parts" are scenes or information in the video that are expected to be of particular interest to the user.

[1793] A "short video" is a video that extracts important parts from the original video so that the content can be understood in a short amount of time.

[1794] "User emotion" refers to the emotional state shown by the user while viewing, and is inferred from facial expressions and voice.

[1795] "Personalizing content" means providing content that is individually optimized based on a user's emotions and viewing history.

[1796] To implement this invention, a system is required in which a smartphone is used as the main user terminal and which links with a server. Specific processing steps and the hardware and software used for these steps are described below.

[1797] User terminal operation

[1798] 1. User registration and login:

[1799] Users install the application on their smartphone and create an account.

[1800] After creating an account, the user enters their authentication information on the login screen to log in. Firebase Authentication is used for authentication.

[1801] 2. Video streaming channel registration:

[1802] After logging in, users enter the URL and name of their video streaming channel and register with the system through the application. This information is stored in the Firebase Database.

[1803] 3. View short videos:

[1804] The server generates a notification for the short video, and the user can view it on their smartphone. Notifications are sent via Firebase Cloud Messaging.

[1805] 4. Watching full-length videos:

[1806] If a user is interested in a short video, they can click on the link to the original full-length video to watch it.

[1807] Server Operation

[1808] 1. User and channel information management:

[1809] The server stores and manages the authentication information entered by the user and information about the registered video distribution channel in the Firebase Database.

[1810] 2. New video detection:

[1811] The server periodically checks registered video streaming channels using the YouTube Data API and Twitch API to detect newly uploaded videos, and stores this information in the Firebase Database.

[1812] 3. Download and analyze the video:

[1813] The server downloads the newly detected video and analyzes it using Google Cloud AI's Video Intelligence API.

[1814] 4. Short video generation:

[1815] A generative AI model extracts key parts of the video and generates a short video, including a thumbnail and key scenes. The generated short video is stored in Firebase Storage, and its metadata is stored in Firebase Database.

[1816] 5. Short Video Notifications:

[1817] The generated short video is sent to the user's device using Firebase Cloud Messaging.

[1818] 6. User Emotion Recognition:

[1819] The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice in real time, using the OpenCV library and Google Cloud Speech-to-Text API.

[1820] 7. Sentiment Data Analysis and Personalized Recommendations:

[1821] The emotional data collected by the server is analyzed using Firebase ML Kit, and videos are personalized based on the user's viewing history and emotional history.

[1822] Specific examples

[1823] For example, if user D is interested in entertainment-related videos, he / she registers multiple entertainment channels in the app. The server periodically checks these channels to detect new video content. Let's consider the case where a trailer for a new movie has been uploaded as a new video.

[1824] The server downloads new movie trailers and uses a generative AI model to extract key scenes. The resulting trailers are short videos of approximately three minutes, each containing thumbnails of the movie and key scenes.

[1825] While User D is watching this short video, the emotion engine analyzes his facial expressions and voice to recognize his excitement and interest. If User D is determined to be particularly interested, the server will also recommend other related entertainment videos.

[1826] Prompt Sentence Examples

[1827] "Analyze the latest movie trailers, extract key scenes, and generate a short video of less than 3 minutes. Include text information about the main cast and director."

[1828] In this way, users can efficiently find content that interests them, improving their viewing experience.

[1829] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1830] Step 1:

[1831] User registration and login:

[1832] Input: User authentication information (email address, password)

[1833] Output: Session information for the authenticated user

[1834] Specific behavior:

[1835] A user installs a smartphone application and creates an account. They then enter their credentials on the login screen and are authenticated using Firebase Authentication. If authentication is successful, a user session is created and the user is logged in to the application.

[1836] Step 2:

[1837] Subscribe to our video streaming channel:

[1838] Input: URL or name of the video distribution channel

[1839] Output: Registered channel information (channel ID, URL)

[1840] Specific behavior:

[1841] Users can register their video streaming channel by entering its URL and name in the application. The information is stored in the Firebase Database, and the channel ID and URL are associated.

[1842] Step 3:

[1843] New Video Detection:

[1844] Input: Registered video distribution channel information

[1845] Output: New video information (video ID, title, URL)

[1846] Specific behavior:

[1847] The server periodically checks registered video streaming channels using the YouTube Data API and Twitch API, detects newly uploaded videos, and stores that information in the Firebase Database.

[1848] Step 4:

[1849] Video download and analysis:

[1850] Input: New video information (video ID, URL)

[1851] Output: Data with important parts extracted

[1852] Specific behavior:

[1853] The server downloads a new video and analyzes it using Google Cloud AI's Video Intelligence API. A generative AI model is used to analyze the video content and extract key parts (scenes, audio, etc.).

[1854] Step 5:

[1855] Short video generation:

[1856] Input: Data with important parts extracted

[1857] Output: Generated short video file (video ID, short video URL)

[1858] Specific behavior:

[1859] The server generates a short video based on the extracted key parts. The generative AI model is given a prompt: "Analyze the content of the latest video, extract key scenes, and generate a short video of less than 3 minutes." The generated short video is stored in Firebase Storage, and its metadata (video ID, short video URL) is stored in Firebase Database.

[1860] Step 6:

[1861] Short Video Notifications:

[1862] Input: The generated short video file and its metadata

[1863] Output: Push notification to user device

[1864] Specific behavior:

[1865] The generated short video is pushed to the user's device using Firebase Cloud Messaging, and when the user clicks on the notification, the short video starts playing.

[1866] Step 7:

[1867] User Emotion Recognition:

[1868] Input: Facial expression and voice data while the user is watching a short video

[1869] Output: Analyzed user emotion data (excitement, joy, interest, etc.)

[1870] Specific behavior:

[1871] The emotion engine uses the smartphone's camera and microphone to analyze the user's facial expressions and voice in real time, and uses the OpenCV library and Google Cloud Speech-to-Text API to collect emotional data and estimate the user's emotional state.

[1872] Step 8:

[1873] Sentiment data analysis and personalized recommendations:

[1874] Input: Analyzed user emotion data and viewing history

[1875] Output: A personalized video recommendation list

[1876] Specific behavior:

[1877] The server analyzes the collected emotion data using Firebase ML Kit and provides personalized videos based on the user's viewing and emotion history. If it determines that the user enjoys a particular piece of content, it will also recommend other interesting videos related to that content.

[1878] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1879] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1880] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1881] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1882] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1883] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1884] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1885] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1886] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1887] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1888] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1889] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1890] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1891] 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.

[1892] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1893] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1894] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1895] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1896] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1897] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1898] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1899] The following is further disclosed regarding the above embodiment.

[1900] (Claim 1)

[1901] A means for a user to subscribe to multiple video distribution channels;

[1902] A means to automatically detect newly posted videos,

[1903] A means for analyzing the content of new videos using a generative artificial intelligence model and extracting important parts;

[1904] A means for automatically generating short videos based on the extracted important parts;

[1905] means for providing the generated short video to a user;

[1906] A system including:

[1907] (Claim 2)

[1908] 10. The system of claim 1, wherein new video detection is performed periodically.

[1909] (Claim 3)

[1910] 10. The system of claim 1, further comprising means for generating and storing metadata about the generated short video.

[1911] "Example 1"

[1912] (Claim 1)

[1913] means for a user to register for multiple media distribution channels;

[1914] A means for automatically detecting newly posted video data;

[1915] A means for analyzing the content of new video data using the generated artificial intelligence model and extracting important parts;

[1916] A means for automatically generating short video data based on the extracted important parts;

[1917] means for generating and storing metadata relating to the generated short video data;

[1918] A means for providing the generated short video data to a user;

[1919] A system including:

[1920] (Claim 2)

[1921] 2. The system of claim 1, wherein detection of new video data is performed periodically.

[1922] (Claim 3)

[1923] 10. The system of claim 1, further comprising means for sending a notification of the generated short video data to the user terminal using a push notification system.

[1924] "Application Example 1"

[1925] (Claim 1)

[1926] A means for a user to subscribe to multiple video distribution channels;

[1927] A means to automatically detect newly posted videos,

[1928] A means for analyzing the content of new videos using a generative artificial intelligence model and extracting important parts;

[1929] A means for automatically generating short videos based on the extracted important parts;

[1930] A means for providing a user with short videos generated via a smart device, particularly a mobile terminal and a wearable device;

[1931] A system including:

[1932] (Claim 2)

[1933] 10. The system of claim 1, wherein new video detection is performed periodically.

[1934] (Claim 3)

[1935] 10. The system of claim 1, further comprising means for generating and storing metadata about the generated short video.

[1936] "Example 2: Combining Emotion Engines"

[1937] (Claim 1)

[1938] a means for a user to register multiple media;

[1939] A means of automatically detecting newly posted data;

[1940] A means for analyzing the content of new data using the generated artificial intelligence model and extracting important parts;

[1941] A means for automatically generating short story data based on the extracted important parts;

[1942] means for providing the generated short data to a user;

[1943] means for collecting and analyzing user emotion data;

[1944] means for adjusting the content of the short story data based on the emotion data;

[1945] a means for making personalized recommendations;

[1946] A system including:

[1947] (Claim 2)

[1948] 10. The system of claim 1, wherein the detection of new data occurs periodically.

[1949] (Claim 3)

[1950] 10. The system of claim 1, further comprising means for generating and storing metadata about the generated short-form data.

[1951] "Application example 2 when combining emotion engines"

[1952] (Claim 1)

[1953] A means for a user to subscribe to multiple video distribution channels;

[1954] A means to automatically detect newly posted videos,

[1955] A means for analyzing the content of new videos using a generative artificial intelligence model and extracting important parts;

[1956] A means for automatically generating short videos based on the extracted important parts;

[1957] means for providing the generated short video to a user;

[1958] means for recognizing user emotions in real time;

[1959] a means for personalizing content based on user sentiment;

[1960] A system including:

[1961] (Claim 2)

[1962] 10. The system of claim 1, wherein new video detection is performed periodically.

[1963] (Claim 3)

[1964] 10. The system of claim 1, further comprising means for generating and storing metadata about the generated short video. [Explanation of symbols]

[1965] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a user to subscribe to multiple video distribution channels; A means to automatically detect newly posted videos, A means for analyzing the content of new videos using a generative artificial intelligence model and extracting important parts; A means for automatically generating short videos based on the extracted important parts; means for providing the generated short video to a user; A system including:

2. The system of claim 1 , wherein new video detection is performed periodically.

3. The system of claim 1 , further comprising: means for generating and storing metadata about the generated short video.

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