System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining user data; means for analyzing the obtained user data; means for automatically generating a plurality of video contents based on an analysis result; means for allowing a user to select from the plurality of generated video contents; and means for posting the selected video content to an online platform.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] Currently, generating video content and posting it to online platforms requires a lot of time and effort. Furthermore, users often lack the skills and knowledge to create effective content, resulting in poor-quality content. This makes content creation stressful and burdensome for users, making it difficult for them to actively share content. To solve this problem, a system is needed that can automatically generate high-quality video content based on user data and easily post it. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring user data, a means for analyzing the acquired user data, a means for automatically generating multiple video content items based on the analysis results, a means for allowing a user to select from the generated multiple video content items, and a means for posting the selected video content item to an online platform. This system allows users to receive suggestions for optimal video content items by simply providing the necessary data, enabling them to easily post high-quality content. This reduces the burden on users and promotes active information dissemination.

[0006] "User data" refers to information such as message data, image data, and video data provided by the user.

[0007] An "acquisition means" is a method or system for receiving data from a user, storing it, and making it available for analysis.

[0008] The "analyzing means" refers to a method or system for analyzing acquired user data, identifying user interests and preferences, and extracting new information.

[0009] "Means for automatically generating multiple video content" refers to a method or system that automatically creates various patterns of video using an algorithm or generation engine based on the analysis results.

[0010] A "means for selection" is an interface or system that allows a user to select the most suitable video from multiple videos generated.

[0011] A "posting means" is a method or system that allows a user to seamlessly upload selected videos to an online platform.

[0012] "Online Platform" refers to a website or application for sharing user-generated content.

[0013] "Natural language processing technology" is a computer technology for analyzing human language and understanding its meaning.

[0014] "Image recognition technology" is a computer technology for analyzing image data and extracting information such as the objects and scenes contained therein. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

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

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

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

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Hereinafter, an embodiment of the system will be described with reference to a specific example.

[0037] Retrieving User Data

[0038] The user first consents to access the online platform. After obtaining consent, the device collects data such as chat data, photos, and videos from the user. This data is then sent to a server and stored in a database.

[0039] Data analysis

[0040] The server launches a generative AI module to analyze the stored user data. This analysis uses natural language processing and image recognition technologies. For example, text information contained in chat data is analyzed to extract the user's interests. Images and videos are also identified using image recognition algorithms, which are then used to identify the user's preferences.

[0041] Video content generation

[0042] Based on the analysis results, the AI ​​generates multiple patterns of video content that are optimal for the user. For example, if a user has a lot of travel-related data, the AI ​​will create multiple patterns of travel-related video content. The generated videos are based on a storyboard and are automatically edited using existing photos and video clips.

[0043] Creating and providing preview data

[0044] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[0045] Submission support

[0046] When the user presses the post button for the selected video, the device sends the selection information to the server, which converts the selected video into a posting format for the online platform and posts the video to the specified account.

[0047] Specific examples

[0048] For example, consider the case where User A uses LINE to send many messages about travel and cooking. User A also has many photos and videos taken at his travel destinations. When User A uses this system, he first authorizes access to LINE VOOM and uploads his travel photos and videos from his device. The server analyzes this data and generates multiple travel videos based on travel-related keywords and images.

[0049] Next, previews of the generated travel videos are sent to the device, and User A can review and select them. Once selection is complete, the selected video is posted to LINE VOOM. In this way, User A can easily post high-quality travel videos.

[0050] This system allows users to significantly reduce the time and effort required to create and edit video content. It also improves the quality of posted content, which is expected to increase user engagement. In this way, it is possible to support users in continuously posting content.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The user consents to access the online platform. The device obtains this consent and sends the authentication information to the server.

[0054] Step 2:

[0055] The device collects chat data, photos, and videos specified by the user and uploads them to the server, which then stores the uploaded data in a database.

[0056] Step 3:

[0057] The server starts the generation AI module, retrieves the saved user data from the database, and begins analysis.

[0058] Step 4:

[0059] Generative AI uses natural language processing technology to analyze chat data and extract user interests and preferences, while image recognition technology is used to analyze photos and videos and identify the objects and scenes they contain.

[0060] Step 5:

[0061] Based on the analysis results, the generative AI designs a storyboard of video content that is optimal for the user. Multiple video patterns are automatically generated based on this storyboard. Effects and music can also be added to the generated videos.

[0062] Step 6:

[0063] The server converts the generated video content into a preview format and transmits it to the terminal, which displays multiple previews to the user.

[0064] Step 7:

[0065] The user selects the video they like best from the previewed videos and presses the submit button, and the device sends this selection information to the server.

[0066] Step 8:

[0067] The server converts the selected video content into a posting format for the online platform and posts the video to the specified account.

[0068] Step 9:

[0069] The server collects engagement data (e.g., number of likes and comments) after posting, along with user feedback, which helps improve the accuracy of the generation AI.

[0070] Example 1

[0071] 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."

[0072] Modern social media platforms require users to post high-quality video content, but video editing and creation requires technical knowledge and a significant amount of time. This makes it difficult for average users to easily create and post high-quality video content. Automatic generation of personalized content based on user interests is also a challenging task.

[0073] 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.

[0074] In this invention, the server includes means for transmitting user data to the server using a security protocol and storing it in a database, means for activating a generation AI module to analyze the stored user data, means for using natural language processing technology and image recognition technology for the analysis to extract keywords, emotions, and interests from text information, means for creating prompts based on the analysis results and automatically generating multiple video content items using the generation AI, means for creating preview data of the generated multiple video content items and sending it to a terminal, means for a user to check and select the preview data on the terminal, and means for converting the selected video content items into a posting format for an online platform and posting the video items to a specified account. This enables users to easily generate and edit high-quality video content items and post them to an online platform without any special technical skills.

[0075] "User data" is a general term for information including text data, image data, and video data related to a user.

[0076] A "security protocol" is a communication protocol for encrypting and protecting data during transmission.

[0077] A "server" is a sophisticated computer system for analyzing, storing, and processing data.

[0078] A "database" is a system for efficiently storing, retrieving, and managing collected user data.

[0079] A "generative AI module" is a software module that uses artificial intelligence techniques to analyze data and generate new content. Examples include GPT-4 and DALL·E.

[0080] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human-spoken language. Examples include GPT for text analysis.

[0081] "Image recognition technology" is a technology that allows a computer to analyze the content of images and videos and recognize specific objects or scenes. Examples include OpenCV.

[0082] A "prompt" is an input sentence that conveys specific instructions or requests to the generating AI.

[0083] A "storyboard" is a diagram used in video production that illustrates the development of footage.

[0084] "Preview data" refers to samples or sample data of multiple video contents that are generated so that the user can check them.

[0085] An "online platform" is an internet service that allows users to post, share, and view content.

[0086] A "post format" is a format or standard for publishing content on an online platform.

[0087] This invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Hereinafter, an embodiment of this system will be described with specific examples.

[0088] Retrieving User Data

[0089] First, the user must consent to access to a specific online platform (e.g., a social media site). Once the user consents, the device collects the user's message data, image data, and video data. The collected data is transmitted to a server using a security protocol, and the server stores it in a database.

[0090] Data analysis

[0091] The server launches a generative AI module to analyze the user data stored in the database. This analysis uses natural language processing technology (e.g., GPT-4) and image recognition technology (e.g., OpenCV). Specifically, the server uses GPT-4 to extract keywords, emotions, and interests from text information, and OpenCV to analyze image and video content. This allows the server to identify the user's preferences and interests.

[0092] Video content generation

[0093] The server creates prompts based on the analysis results and automatically generates multiple video contents using generative AI (for example, a video generation version of DALL·E). The generated video contents are edited based on a storyboard and seamlessly combined with existing photos and video clips.

[0094] Creating and providing preview data

[0095] A preview of the generated video content is created by the server and sent to the device. The user can view multiple previews on the device and select the video pattern they think is best. The preview also includes effects and background music, so the user can check the overall atmosphere of the completed video.

[0096] Submission support

[0097] When a user selects a video and presses the "Post" button, the device sends the selection information to the server. The server then converts the selected video into the posting format for the specified online platform and automatically posts it to the specified account. The user can then view the final post on the device.

[0098] Specific examples

[0099] For example, consider the case where User A sends many messages about travel and cooking using a messaging app. User A also has many photos and videos taken at his travel destinations. When User A uses this system, he first consents to access a specific online platform (e.g., a travel sharing site) and uploads his travel photos and videos from his device. The server analyzes this data and generates multiple travel videos based on keywords and images related to "travel" and "cooking."

[0100] Next, previews of the generated travel videos are sent to the device, and User A can review and select them. Once the selection is complete, the selected videos are posted to the travel sharing site. In this way, User A can easily post high-quality travel videos.

[0101] Prompt Sentence Examples

[0102] "Given the following travel photo and message data, generate a travel video to post on a social media site. Use GPT-4 for text analysis and OpenCV for image recognition."

[0103] This system allows users to create and edit high-quality video content without any technical skills and easily post it to online platforms. It also enables the generation of personalized content based on users' interests, which is expected to increase engagement.

[0104] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0105] Step 1: Get User Data

[0106] The user consents to access the online platform. With the user's consent, the device collects message data, image data, and video data. The collected data is encrypted using a security protocol and sent to the server. The input is the user's consent and various data, and the output is the transmission of encrypted user data to the server.

[0107] Step 2: Save your data

[0108] The server receives the encrypted data sent from the terminal and stores it in a database. Specific operations include decrypting the data and writing it to the database early. The input is the encrypted user data, and the output is the user data stored in the database.

[0109] Step 3: Launching the analysis module

[0110] The server launches the generation AI module to analyze the stored user data. The input is the user data stored in the database, and the output is the launch of the analysis module. Specifically, the analysis module is initialized and the data is loaded.

[0111] Step 4: Analyzing the text data

[0112] The server uses GPT-4 to analyze user message data and extract keywords, emotions, and interests. The input is the user message data, and the output is the extracted keywords, emotions, and interests. Specific data processing includes tokenization, emotion scoring, and keyword extraction.

[0113] Step 5: Image and video data analysis

[0114] The server uses OpenCV to analyze the user's image and video data and identify scenes and objects. The input is the user's image and video data, and the output is a list of identified scenes and objects. Specifically, processing such as edge detection, object recognition, and scene classification is performed.

[0115] Step 6: Create a prompt

[0116] Based on the analysis results, the server creates a prompt to be passed to the generation AI. The input is the analysis results (keywords, emotions, interests, and a list of scenes and objects), and the output is the prompt text. Specifically, the text is generated by inserting values ​​into the appropriate template.

[0117] Step 7: Auto-generate videos

[0118] The generation AI automatically generates video content based on prompts. The input is the prompt text and the user's image and video data, and the output is an automatically generated video. The generation AI performs editing tasks such as arranging scenes, applying effects, and adding text.

[0119] Step 8: Creating Preview Data

[0120] The server creates preview data from automatically generated video content. The input is the automatically generated video, and the output is the preview data. Specifically, sample clips of the video and thumbnails are generated.

[0121] Step 9: Send preview data

[0122] The server sends the preview data to the terminal. The input is the preview data, and the output is the preview data sent to the terminal. This includes data compression and transmission.

[0123] Step 10: Preview and select

[0124] The user checks the preview data on the device and selects the most suitable video pattern. The input is the preview data displayed on the device, and the output is the user's selection information. Specific actions include rating each video and pressing the selection button.

[0125] Step 11: Submit your selections

[0126] The terminal sends the user's selection information to the server. The input is the user's selection information, and the output is the selection information sent to the server. This involves packaging the selection information and using a transmission protocol.

[0127] Step 12: Post your video

[0128] The server converts the selected video into a posting format for the online platform and posts the video to the specified account. The input is the selected video and posting format information, and the output is the video posted on the online platform. Specifically, this process includes format conversion, calling the platform API, and confirming that posting is complete.

[0129] (Application example 1)

[0130] 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."

[0131] The conventional content creation and posting process is laborious and time-consuming for users. It is also difficult to automatically generate high-quality video content that accurately reflects users' interests. Furthermore, there is a lack of a mechanism for quickly and easily posting the generated content to a platform. The objective of this invention is to solve these problems and provide a system that allows users to easily create and post high-quality video content.

[0132] 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.

[0133] In this invention, the server includes means for acquiring user data, means for analyzing the acquired user data, means for automatically generating multiple video contents based on the analysis results, means for allowing a user to select from the generated video contents, means for providing a preview of the generated video contents, and means for allowing a user to easily post the selected video, thereby enabling a user to quickly and easily generate and post video contents that suit their own interests.

[0134] Below are definitions of key terms based on the patent claims, rewritten to fit the application.

[0135] "User data" is a general term for information such as message data, image data, and video data that is generated or held by a user.

[0136] "Analysis" is the process of extracting useful information from collected user data and determining the user's hobbies and interests.

[0137] "Video content" refers to digital content in video format that is generated using technologies such as generative AI and is viewable by users.

[0138] "Preview" refers to a shortened display or trial viewing that allows you to check the overall picture and atmosphere of the generated video content in advance.

[0139] An "online platform" is an internet service or website where video content is published and users can view and share it.

[0140] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0141] "Image recognition technology" is a computer vision technology for identifying and analyzing useful information from image data.

[0142] "Generative AI" is an artificial intelligence technology that automatically generates new content based on large amounts of data.

[0143] "Automatic video generation" is the process of generating video content using pre-defined algorithms or AI models based on user data.

[0144] "Posting support" is a function that automatically posts the generated video content to the online platform specified by the user.

[0145] This invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Specific embodiments for realizing this system are described below.

[0146] Retrieving User Data

[0147] The user first consents to access the online platform. After obtaining consent, the device collects user data, such as message data, image data, and video data. This data is sent to a server and stored in a database.

[0148] Data analysis

[0149] The server launches a generative AI module to analyze the stored user data. Natural language processing and image recognition technologies are used for the analysis. For example, text information contained in message data is analyzed to extract the user's interests. Image and video data are also identified using image recognition algorithms, and this is also used as information about the user's preferences.

[0150] Video content generation

[0151] Based on the analysis results, the server generates multiple patterns of video content that are optimal for the user. The generation AI automatically edits the storyboard using existing image data and video data clips. This generates high-quality video content that is in line with the user's interests.

[0152] Creating and providing preview data

[0153] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[0154] Submission support

[0155] When a user presses the post button for the selected video, the device sends the selection information to the server, which then converts the selected video into the online platform's posting format and posts the video to the specified account, allowing users to easily share high-quality video content on the online platform.

[0156] Hardware and software used

[0157] The system is implemented using the following hardware and software:

[0158] Hardware: Smartphones, smart glasses

[0159] software:

[0160] OpenAI API (natural language processing)

[0161] MoviePy (video editing)

[0162] requests (data acquisition and transmission)

[0163] cv2 (image processing)

[0164] Specific examples

[0165] For example, if a user has a lot of travel-related data, they can input the following prompt sentence into the generative AI model:

[0166] Analyze user travel data and generate a high-quality travel video. Photos to be used include beaches, mountains, cityscapes, etc. The storyboard sequence should be arrival, sightseeing, dining, departure.

[0167] Based on this prompt, the generative AI automatically generates a travel video, which users can then preview and easily post to an online platform.

[0168] This invention enables users to quickly and easily create and post high-quality video content that matches their interests.

[0169] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0170] Step 1:

[0171] The user consents to access to the online platform. After obtaining the user's consent, the device collects user data such as message data, image data, and video data. The collected data is sent to a server and stored in a database. The input is user data, and the output is the user data stored in the database.

[0172] Step 2:

[0173] The server launches a generative AI module to analyze the stored user data. The input is the user data stored in the database, and the output is the analyzed data. Specifically, it uses natural language processing technology to analyze message data and extract user interests. It also uses image recognition technology to identify the content of image data and video data and obtain information about the user's preferences.

[0174] Step 3:

[0175] The server automatically generates multiple patterns of video content based on the analysis results. The input is the analysis results, and the output is multiple generated video content. Based on the storyboard, the generation AI automatically edits existing image data and video data clips to generate high-quality video content that is optimal for the user.

[0176] Step 4:

[0177] The server creates a preview of the generated video content and sends it to the terminal. The input is the generated video content, and the output is the preview data sent to the terminal. Specifically, a shortened version of the video for preview is generated and sent to the user's terminal.

[0178] Step 5:

[0179] The user checks the preview data and selects the most suitable pattern. The input is the preview data sent from the server, and the output is the selected video content. Specifically, the user watches the preview and checks the overall atmosphere, including effects and background music.

[0180] Step 6:

[0181] When a user presses the post button for the selected video, the device sends the selection information to the server. The input is the selected video content, and the output is the transmission of the selection information to the server. Specifically, clicking the post button sends the selection information to the server.

[0182] Step 7:

[0183] The server converts the selected video into the posting format of the online platform and posts the video to the specified account. The input is the selection information, and the output is the completion of posting to the online platform. The specific operation is to convert the video into the appropriate format and automatically process the posting.

[0184] 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.

[0185] This invention is a system that automatically generates multiple patterns of optimal video content based on user data, enables users to easily post the content to an online platform, and further combines it with an emotion engine that recognizes the user's emotions. Below, an embodiment of the system will be described based on a specific example.

[0186] Retrieving User Data

[0187] The user first consents to access the online platform. After obtaining consent, the device collects data such as chat data, photos, and videos from the user. This data is then sent to a server and stored in a database.

[0188] Data analysis

[0189] The server launches a generative AI module and emotion engine to analyze the stored user data. Natural language processing and image recognition technologies are used for the analysis. For example, text information contained in chat data is analyzed to extract not only the user's interests but also their emotional state. Image recognition algorithms are also used to identify the content of photos and videos, which are then used as information on the user's preferences and emotions.

[0190] Use of emotion engine

[0191] The emotion engine analyzes emotions from the acquired user data. This engine analyzes the user's text and facial expressions in videos to determine the user's emotional state. For example, if the engine detects from the chat data that the user is "having fun," it will use more cheerful content and brighter effects in the video content.

[0192] Video content generation

[0193] Based on the analysis results, the generative AI designs a storyboard of video content that is optimal for the user. Multiple video patterns are automatically generated based on this storyboard. Effects and music based on the results of the emotion engine are also added to the generated videos. For example, if the user is having fun, cheerful music and bright effects are used.

[0194] Creating and providing preview data

[0195] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[0196] Submission support

[0197] When the user presses the post button for the selected video, the device sends the selection information to the server, which converts the selected video into a posting format for the online platform and posts the video to the specified account.

[0198] Specific examples

[0199] For example, consider the case where User B frequently shares fun topics about their daily life on LINE. User B often posts smiling photos and videos. When User B uses this system, they first authorize access to LINE VOOM and upload photos and videos of their daily life from their device. The server analyzes this data, and the emotion engine detects the emotion of enjoyment. Based on the analysis results, the generation AI generates multiple fun video patterns.

[0200] Next, previews of the generated fun videos are sent to User B's device, and User B can review and select them. Once the selection is complete, the selected video is posted to LINE VOOM. In this way, User B can easily post high-quality, fun videos.

[0201] This system enables the generation of video content that reflects the user's emotions, further enriching the user's digital experience. Furthermore, emotion-based customization enables the provision of more personalized content. In this way, it is possible to support users in actively sharing information on an ongoing basis.

[0202] The processing flow will be explained below.

[0203] Step 1:

[0204] The user consents to access the online platform. The device obtains this consent and sends the authentication information to the server.

[0205] Step 2:

[0206] The device collects chat data, photos, and videos specified by the user and uploads them to the server, which then stores the uploaded data in a database.

[0207] Step 3:

[0208] The server starts the generative AI module and emotion engine, retrieves the saved user data from the database, and begins analysis.

[0209] Step 4:

[0210] The generative AI uses natural language processing technology to analyze the conversation data and extract the user's interests and emotions. For example, it identifies emotional keywords such as "fun" and "stressful" from the conversation data.

[0211] Step 5:

[0212] Generative AI uses image recognition technology to analyze photos and videos, identifying objects and scenes contained in them and simultaneously analyzing emotions from people's facial expressions.

[0213] Step 6:

[0214] The emotion engine analyzes the user's emotional state from chat data and image / video data. For example, if a user has many photos and videos of themselves having fun, it will identify their emotional state as "having fun."

[0215] Step 7:

[0216] Based on the analysis results, the generative AI designs a storyboard for video content that reflects the user's preferences and emotional state. Based on the results of the emotion engine, multiple video patterns are automatically generated, using fun music and bright effects, for example.

[0217] Step 8:

[0218] The server creates a preview of the generated video content and sends it to the device, which displays multiple previews to the user.

[0219] Step 9:

[0220] The user selects the video they like best from the previewed videos and presses the submit button, and the device sends this selection information to the server.

[0221] Step 10:

[0222] The server converts the selected video content into a posting format for the online platform and posts the video to the specified account.

[0223] Step 11:

[0224] The server collects engagement data (e.g., number of likes and comments) after posting, along with user feedback, which helps improve the accuracy of the generation AI.

[0225] Example 2

[0226] 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."

[0227] Conventional systems have the problem that the process for users to create effective video content is complicated, time-consuming, and laborious. In addition, it is difficult to generate personalized content based on emotions, and it has not been possible to automatically generate video content that is directly linked to the user's interests and emotions.

[0228] 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.

[0229] In this invention, the server includes means for acquiring user data, means for transmitting the acquired user data to the server and storing it in a database, means for activating a generation AI module and an emotion engine to analyze the stored user data and extract the user's interests and emotional state using natural language processing technology and image recognition technology, means for designing a storyboard of video content optimal for the user based on the analysis results and automatically generating multiple video patterns, means for transmitting previews of the generated multiple video content to the user's device so that the user can select one, and means for posting the selected video content to an online platform. This enables users to easily and quickly generate and post personalized video content based on emotions.

[0230] "User data" refers to information including text data, image data, and video data provided by a user.

[0231] A "server" is a system of electronic devices and software for receiving, storing, and analyzing user data.

[0232] A "database" is a system for managing and storing user data stored on a server.

[0233] The "generative AI module" is a module that uses artificial intelligence technology to analyze user data and generate optimal video content.

[0234] An "emotion engine" is a system for analyzing a user's emotional state from their text data and image data.

[0235] "Natural language processing technology" is a technology for analyzing a user's text data and understanding its meaning and emotions.

[0236] "Image recognition technology" is a technology that analyzes a user's image data and video data and recognizes their content and emotions.

[0237] A "storyboard" is a blueprint that visually represents the structure and flow of scenes in video content.

[0238] An "online platform" is a website or application for posting and sharing user-generated video content.

[0239] A "preview" is a video that is temporarily displayed to allow the user to check the generated video content.

[0240] This invention is a system that automatically generates multiple patterns of optimal video content based on user data and allows users to easily post them to online platforms. It also combines an emotion engine that recognizes the user's emotions.

[0241] First, a user consents to access the online platform to begin using the system. After obtaining the user's consent, the device collects data such as the user's chat data, photos, and videos. This data is sent to a server and stored in a database. The database is a system for managing and storing user data stored on the server.

[0242] The server then launches a generative AI module and an emotion engine to analyze the stored user data. The generative AI module uses natural language processing technology to analyze the text information in the chat data and extract the user's interests and emotional state. The emotion engine uses image recognition technology to identify the content of photos and videos and determine the user's emotional state.

[0243] For example, if the AI ​​detects that the user is enjoying the content, it will use the analysis results to design a storyboard for the optimal video content for the user and automatically generate multiple video patterns. The generated videos will also include effects and music based on the results of the emotion engine.

[0244] A preview of the generated video content is generated by the server and sent to the terminal. The user can check this preview data and select the pattern that seems most suitable. When the user selects the selected video content, the selection information is sent from the terminal to the server. The server converts the selected video into a posting format for the online platform and posts the video to the specified account.

[0245] As a concrete example, consider the case where User B frequently shares fun topics about daily life on a communication app. User B posts many smiling photos and videos during these sessions. When User B uses this system, he or she first grants permission to access the video sharing function on the communication app and uploads photos and videos of daily life from his or her device. The server analyzes this data, and the emotion engine detects the emotion of enjoyment. Based on the analysis results, the generation AI generates multiple fun video patterns.

[0246] Next, previews of the generated fun videos are sent to the device, and User B can review and select them. Once selection is complete, the selected video is posted to the video sharing function of the communication app. In this way, User B can easily post high-quality, fun videos.

[0247] An example of a prompt is, "Based on the following talk data and photos, please generate video content that you believe the user is enjoying."

[0248] This system enables the generation of video content that reflects the user's emotions, further enriching the user's digital experience. Furthermore, emotion-based customization enables the provision of more personalized content. In this way, it is possible to support users in actively sharing information on an ongoing basis.

[0249] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0250] Step 1: Get User Data

[0251] To start using the system, the user consents to access the online platform. After confirming this consent, the device collects data such as the user's chat data, photos, and videos. The input data is text data, image data, and video data provided by the user, and the output is data sent from the device to the server. The device then sends the acquired data to the server.

[0252] Step 2: Save your data

[0253] The server receives the user data sent from the terminal.The server then stores the received data in a database.The input is the user data received from the terminal, and the output is the data stored in the database.

[0254] Step 3: Analyze the data

[0255] The server launches a generative AI module and an emotion engine to analyze user data stored in the database. The generative AI module uses natural language processing technology to analyze text information in the chat data and extract the user's interests and emotional state. The emotion engine uses image recognition technology to identify the content of photos and videos and determine the user's emotional state. The input is the user data stored in the database, and the output is the analysis results. Based on this, the server identifies the user's interests and emotional state.

[0256] Step 4: Sentiment Analysis

[0257] The emotion engine on the server analyzes the user's emotional state from the acquired user data. For example, the emotion "enjoying" may be detected from chat data. The input is the analyzed user data, and the output is the result of the judgment of the emotional state. The emotion engine identifies emotions from the user's text and facial expressions in images.

[0258] Step 5: Generate video content

[0259] The server's generation AI designs a storyboard of video content that is optimal for the user based on the analysis results of the emotion engine. It then automatically generates multiple video patterns based on this storyboard. Effects and music based on the results of the emotion engine are added to the generated videos. The input is the analysis results of the emotion engine, and the output is multiple video contents.

[0260] Step 6: Creating and providing preview data

[0261] The server creates preview data of the generated video content and sends it to the terminal. The input is the generated video content and the output is the preview data. The terminal presents the preview data to the user, allowing the user to check and select it.

[0262] Step 7: Select and post your video

[0263] The user selects the optimal video pattern and presses the post button. The device sends this selection information to the server. The server converts the selected video into the posting format of the online platform and posts the video to the specified account. The input is the user's video selection information, and the output is the video posted to the online platform.

[0264] In this way, users can easily create and post personalized emotion-based video content.

[0265] (Application example 2)

[0266] 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."

[0267] Conventional video content generation systems recommend and generate content without considering the user's emotional state, making it difficult to provide appropriate content that reflects the user's real-time emotional state. This results in a low-quality viewing experience and insufficient personalized experience. It also makes it difficult for users to efficiently find the content they desire, resulting in low satisfaction with content viewing.

[0268] 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.

[0269] In this invention, the server includes means for acquiring user data and real-time emotional data, means for analyzing the acquired user data and real-time emotional data, and means for automatically generating multiple video content items based on the analysis results, thereby enabling the provision of personalized content that reflects the user's real-time emotional state.

[0270] "User data" refers to information generated by a user, including message data, image data, and video data.

[0271] "Real-time emotional data" refers to data that represents the user's current emotional state, and is information that is primarily obtained from the user's facial expressions using image recognition technology.

[0272] "Natural language processing technology" is a technology for analyzing text data and understanding and extracting its content and meaning.

[0273] "Image recognition technology" is a technology that extracts and analyzes specific information from image data, and is particularly used for analyzing facial expressions.

[0274] "Emotion analysis technology" is a technology that identifies and analyzes a user's emotional state from text and image data.

[0275] "Personalized content" refers to content that is optimized based on the preferences and feelings of each individual user.

[0276] An "online platform" is a service or system available via the Internet that allows for the posting and sharing of videos.

[0277] The detailed description of the preferred embodiment of the present invention will be given using appropriate hardware and software.

[0278] System Configuration

[0279] This system acquires and analyzes user data and real-time emotional data, and generates and provides video content based on the analysis results. Specifically, it includes the following elements:

[0280] 1. User devices: Devices such as smart TVs and head-mounted displays (HMDs). These devices have built-in cameras and microphones to capture real-time emotional data from users.

[0281] 2. Server: Located in the cloud, it analyzes user data and emotional data using a generative AI model that combines natural language processing and image recognition technologies.

[0282] 3. Data transmission and reception means: A network configuration that transmits user data and emotion data to the server and sends the analysis results to the terminal.

[0283] System Operation

[0284] First, the user device collects user data, such as viewing history and real-time emotional data. The emotional data is acquired using a facial recognition camera, for example. With the user's consent, this data is sent to the server.

[0285] After receiving the data, the server analyzes the text data using natural language processing technology, while also analyzing real-time facial expression data using image recognition and emotion analysis technology. As a result of the analysis, the user's interests and emotional state are identified.

[0286] Next, the generative AI model automatically generates multiple video content pieces that are optimal for the user based on the analysis results. During this generation process, effects and music are also selected and applied according to the user's emotions. For example, if the user is having fun, cheerful music and bright effects are used.

[0287] The generated video content is sent to the user's device as a series of previews, where the user can review and select the video they consider most appropriate. Once selected, the selected video is posted to an online platform, enabling users to efficiently post personalized content based on their emotions.

[0288] Specific Examples

[0289] For example, consider a case where a user accesses the system through a smart TV and provides their viewing history and real-time emotional data. If the user smiles while watching a video, comedy movies or variety shows will be recommended based on the emotional data. The server generates appropriate content based on the analysis results and provides it to the user as a preview.

[0290] In this case, example prompts for the generative AI model might include, "If the user is having fun, what type of video would you recommend?" or "If you determine that the user is sad, what type of content would you recommend?"

[0291] In this way, our system provides personalized video recommendations that reflect the user's real-time emotions, enriching the user's viewing experience.

[0292] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0293] Step 1:

[0294] The user accesses the system via a smart TV or a head-mounted display (HMD).

[0295] Input: A request to access the system.

[0296] Output: Display of user consent screen.

[0297] How it works: The user's device displays a screen asking for consent to data collection and use of the facial recognition camera. If the user gives consent, the device proceeds to the next step.

[0298] Step 2:

[0299] The user terminal acquires user data and real-time emotion data.

[0300] Input: User viewing history, message data, and facial expression data.

[0301] Output: Collected user and sentiment data.

[0302] Operation: Using the built-in camera on the user's device, the system captures the user's facial expression data in real time, as well as collecting viewing history and message data.

[0303] Step 3:

[0304] The collected data is sent to a server.

[0305] Input: User data and real-time sentiment data.

[0306] Output: Sending data to the server.

[0307] Operation: The user device sends the acquired data to the server via the Internet.

[0308] Step 4:

[0309] The server analyzes the received data.

[0310] Input: User data and real-time emotion data sent to the server.

[0311] Output: Analysis results (user interests and emotional state).

[0312] How it works: The server uses natural language processing technology to analyze the user's viewing history and message data to extract the user's interests and concerns. At the same time, it uses image recognition and emotion analysis technology to analyze facial expression data and identify the user's emotional state.

[0313] Step 5:

[0314] Based on the analysis results, the server automatically generates multiple video contents using a generative AI model.

[0315] Input: Analysis results.

[0316] Output: Auto-generated video content.

[0317] How it works: The server inputs prompts into the generative AI model, which generates multiple optimal video content based on the user's emotions. For example, it uses prompts such as, "If the user is enjoying themselves, what kind of videos would you recommend?"

[0318] Step 6:

[0319] A preview of the generated video content is transmitted to the user terminal.

[0320] Input: Auto-generated video content.

[0321] Output: Video preview sent to user device.

[0322] Operation: The server sends the generated video content in a preview format to the user's device, which displays it for the user to review.

[0323] Step 7:

[0324] The user selects the video that seems most suitable.

[0325] Input: Video preview.

[0326] Output: The selected video content.

[0327] How it works: The user device accepts a selection from multiple previews and sends information about the video content selected by the user to the server.

[0328] Step 8:

[0329] The server posts the selected videos to an online platform.

[0330] Input: Selected video content information.

[0331] Output: Video posted on an online platform.

[0332] How it works: The server converts the selected video content into a posting format for the online platform and posts it to the specified account.

[0333] 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.

[0334] 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.

[0335] 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.

[0336] [Second embodiment]

[0337] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0338] 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.

[0339] 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).

[0340] 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.

[0341] 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.

[0342] 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).

[0343] 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.

[0344] 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.

[0345] 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.

[0346] 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.

[0347] 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.

[0348] 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."

[0349] The present invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Hereinafter, an embodiment of the system will be described with reference to a specific example.

[0350] Retrieving User Data

[0351] The user first consents to access the online platform. After obtaining consent, the device collects data such as chat data, photos, and videos from the user. This data is then sent to a server and stored in a database.

[0352] Data analysis

[0353] The server launches a generative AI module to analyze the stored user data. This analysis uses natural language processing and image recognition technologies. For example, text information contained in chat data is analyzed to extract the user's interests. Images and videos are also identified using image recognition algorithms, which are then used to identify the user's preferences.

[0354] Video content generation

[0355] Based on the analysis results, the AI ​​generates multiple patterns of video content that are optimal for the user. For example, if a user has a lot of travel-related data, the AI ​​will create multiple patterns of travel-related video content. The generated videos are based on a storyboard and are automatically edited using existing photos and video clips.

[0356] Creating and providing preview data

[0357] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[0358] Submission support

[0359] When the user presses the post button for the selected video, the device sends the selection information to the server, which converts the selected video into a posting format for the online platform and posts the video to the specified account.

[0360] Specific examples

[0361] For example, consider the case where User A uses LINE to send many messages about travel and cooking. User A also has many photos and videos taken at his travel destinations. When User A uses this system, he first authorizes access to LINE VOOM and uploads his travel photos and videos from his device. The server analyzes this data and generates multiple travel videos based on travel-related keywords and images.

[0362] Next, previews of the generated travel videos are sent to the device, and User A can review and select them. Once selection is complete, the selected video is posted to LINE VOOM. In this way, User A can easily post high-quality travel videos.

[0363] This system allows users to significantly reduce the time and effort required to create and edit video content. It also improves the quality of posted content, which is expected to increase user engagement. In this way, it is possible to support users in continuously posting content.

[0364] The processing flow will be explained below.

[0365] Step 1:

[0366] The user consents to access the online platform. The device obtains this consent and sends the authentication information to the server.

[0367] Step 2:

[0368] The device collects chat data, photos, and videos specified by the user and uploads them to the server, which then stores the uploaded data in a database.

[0369] Step 3:

[0370] The server starts the generation AI module, retrieves the saved user data from the database, and begins analysis.

[0371] Step 4:

[0372] Generative AI uses natural language processing technology to analyze chat data and extract user interests and preferences, while image recognition technology is used to analyze photos and videos and identify the objects and scenes they contain.

[0373] Step 5:

[0374] Based on the analysis results, the generative AI designs a storyboard of video content that is optimal for the user. Multiple video patterns are automatically generated based on this storyboard. Effects and music can also be added to the generated videos.

[0375] Step 6:

[0376] The server converts the generated video content into a preview format and transmits it to the terminal, which displays multiple previews to the user.

[0377] Step 7:

[0378] The user selects the video they like best from the previewed videos and presses the submit button, and the device sends this selection information to the server.

[0379] Step 8:

[0380] The server converts the selected video content into a posting format for the online platform and posts the video to the specified account.

[0381] Step 9:

[0382] The server collects engagement data (e.g., number of likes and comments) after posting, along with user feedback, which helps improve the accuracy of the generation AI.

[0383] Example 1

[0384] 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."

[0385] Modern social media platforms require users to post high-quality video content, but video editing and creation requires technical knowledge and a significant amount of time. This makes it difficult for average users to easily create and post high-quality video content. Automatic generation of personalized content based on user interests is also a challenging task.

[0386] 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.

[0387] In this invention, the server includes means for transmitting user data to the server using a security protocol and storing it in a database, means for activating a generation AI module to analyze the stored user data, means for using natural language processing technology and image recognition technology for the analysis to extract keywords, emotions, and interests from text information, means for creating prompts based on the analysis results and automatically generating multiple video content items using the generation AI, means for creating preview data of the generated multiple video content items and sending it to a terminal, means for a user to check and select the preview data on the terminal, and means for converting the selected video content items into a posting format for an online platform and posting the video items to a specified account. This enables users to easily generate and edit high-quality video content items and post them to an online platform without any special technical skills.

[0388] "User data" is a general term for information including text data, image data, and video data related to a user.

[0389] A "security protocol" is a communication protocol for encrypting and protecting data during transmission.

[0390] A "server" is a sophisticated computer system for analyzing, storing, and processing data.

[0391] A "database" is a system for efficiently storing, retrieving, and managing collected user data.

[0392] A "generative AI module" is a software module that uses artificial intelligence techniques to analyze data and generate new content. Examples include GPT-4 and DALL·E.

[0393] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human-spoken language. Examples include GPT for text analysis.

[0394] "Image recognition technology" is a technology that allows a computer to analyze the content of images and videos and recognize specific objects or scenes. Examples include OpenCV.

[0395] A "prompt" is an input sentence that conveys specific instructions or requests to the generating AI.

[0396] A "storyboard" is a diagram used in video production that illustrates the development of footage.

[0397] "Preview data" refers to samples or sample data of multiple video contents that are generated so that the user can check them.

[0398] An "online platform" is an internet service that allows users to post, share, and view content.

[0399] A "post format" is a format or standard for publishing content on an online platform.

[0400] This invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Hereinafter, an embodiment of this system will be described with specific examples.

[0401] Retrieving User Data

[0402] First, the user must consent to access to a specific online platform (e.g., a social media site). Once the user consents, the device collects the user's message data, image data, and video data. The collected data is transmitted to a server using a security protocol, and the server stores it in a database.

[0403] Data analysis

[0404] The server launches a generative AI module to analyze the user data stored in the database. This analysis uses natural language processing technology (e.g., GPT-4) and image recognition technology (e.g., OpenCV). Specifically, the server uses GPT-4 to extract keywords, emotions, and interests from text information, and OpenCV to analyze image and video content. This allows the server to identify the user's preferences and interests.

[0405] Video content generation

[0406] The server creates prompts based on the analysis results and automatically generates multiple video contents using generative AI (for example, a video generation version of DALL·E). The generated video contents are edited based on a storyboard and seamlessly combined with existing photos and video clips.

[0407] Creating and providing preview data

[0408] A preview of the generated video content is created by the server and sent to the device. The user can view multiple previews on the device and select the video pattern they think is best. The preview also includes effects and background music, so the user can check the overall atmosphere of the completed video.

[0409] Submission support

[0410] When a user selects a video and presses the "Post" button, the device sends the selection information to the server. The server then converts the selected video into the posting format for the specified online platform and automatically posts it to the specified account. The user can then view the final post on the device.

[0411] Specific examples

[0412] For example, consider the case where User A sends many messages about travel and cooking using a messaging app. User A also has many photos and videos taken at his travel destinations. When User A uses this system, he first consents to access a specific online platform (e.g., a travel sharing site) and uploads his travel photos and videos from his device. The server analyzes this data and generates multiple travel videos based on keywords and images related to "travel" and "cooking."

[0413] Next, previews of the generated travel videos are sent to the device, and User A can review and select them. Once the selection is complete, the selected videos are posted to the travel sharing site. In this way, User A can easily post high-quality travel videos.

[0414] Prompt Sentence Examples

[0415] "Given the following travel photo and message data, generate a travel video to post on a social media site. Use GPT-4 for text analysis and OpenCV for image recognition."

[0416] This system allows users to create and edit high-quality video content without any technical skills and easily post it to online platforms. It also enables the generation of personalized content based on users' interests, which is expected to increase engagement.

[0417] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0418] Step 1: Get User Data

[0419] The user consents to access the online platform. With the user's consent, the device collects message data, image data, and video data. The collected data is encrypted using a security protocol and sent to the server. The input is the user's consent and various data, and the output is the transmission of encrypted user data to the server.

[0420] Step 2: Save your data

[0421] The server receives the encrypted data sent from the terminal and stores it in a database. Specific operations include decrypting the data and writing it to the database early. The input is the encrypted user data, and the output is the user data stored in the database.

[0422] Step 3: Launching the analysis module

[0423] The server launches the generation AI module to analyze the stored user data. The input is the user data stored in the database, and the output is the launch of the analysis module. Specifically, the analysis module is initialized and the data is loaded.

[0424] Step 4: Analyzing the text data

[0425] The server uses GPT-4 to analyze user message data and extract keywords, emotions, and interests. The input is the user message data, and the output is the extracted keywords, emotions, and interests. Specific data processing includes tokenization, emotion scoring, and keyword extraction.

[0426] Step 5: Image and video data analysis

[0427] The server uses OpenCV to analyze the user's image and video data and identify scenes and objects. The input is the user's image and video data, and the output is a list of identified scenes and objects. Specifically, processing such as edge detection, object recognition, and scene classification is performed.

[0428] Step 6: Create a prompt

[0429] Based on the analysis results, the server creates a prompt to be passed to the generation AI. The input is the analysis results (keywords, emotions, interests, and a list of scenes and objects), and the output is the prompt text. Specifically, the text is generated by inserting values ​​into the appropriate template.

[0430] Step 7: Auto-generate videos

[0431] The generation AI automatically generates video content based on prompts. The input is the prompt text and the user's image and video data, and the output is an automatically generated video. The generation AI performs editing tasks such as arranging scenes, applying effects, and adding text.

[0432] Step 8: Creating Preview Data

[0433] The server creates preview data from automatically generated video content. The input is the automatically generated video, and the output is the preview data. Specifically, sample clips of the video and thumbnails are generated.

[0434] Step 9: Send preview data

[0435] The server sends the preview data to the terminal. The input is the preview data, and the output is the preview data sent to the terminal. This includes data compression and transmission.

[0436] Step 10: Preview and select

[0437] The user checks the preview data on the device and selects the most suitable video pattern. The input is the preview data displayed on the device, and the output is the user's selection information. Specific actions include rating each video and pressing the selection button.

[0438] Step 11: Submit your selections

[0439] The terminal sends the user's selection information to the server. The input is the user's selection information, and the output is the selection information sent to the server. This involves packaging the selection information and using a transmission protocol.

[0440] Step 12: Post your video

[0441] The server converts the selected video into a posting format for the online platform and posts the video to the specified account. The input is the selected video and posting format information, and the output is the video posted on the online platform. Specifically, this process includes format conversion, calling the platform API, and confirming that posting is complete.

[0442] (Application example 1)

[0443] 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."

[0444] The conventional content creation and posting process is laborious and time-consuming for users. It is also difficult to automatically generate high-quality video content that accurately reflects users' interests. Furthermore, there is a lack of a mechanism for quickly and easily posting the generated content to a platform. The objective of this invention is to solve these problems and provide a system that allows users to easily create and post high-quality video content.

[0445] 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.

[0446] In this invention, the server includes means for acquiring user data, means for analyzing the acquired user data, means for automatically generating multiple video contents based on the analysis results, means for allowing a user to select from the generated video contents, means for providing a preview of the generated video contents, and means for allowing a user to easily post the selected video, thereby enabling a user to quickly and easily generate and post video contents that suit their own interests.

[0447] Below are definitions of key terms based on the patent claims, rewritten to fit the application.

[0448] "User data" is a general term for information such as message data, image data, and video data that is generated or held by a user.

[0449] "Analysis" is the process of extracting useful information from collected user data and determining the user's hobbies and interests.

[0450] "Video content" refers to digital content in video format that is generated using technologies such as generative AI and is viewable by users.

[0451] "Preview" refers to a shortened display or trial viewing that allows you to check the overall picture and atmosphere of the generated video content in advance.

[0452] An "online platform" is an internet service or website where video content is published and users can view and share it.

[0453] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0454] "Image recognition technology" is a computer vision technology for identifying and analyzing useful information from image data.

[0455] "Generative AI" is an artificial intelligence technology that automatically generates new content based on large amounts of data.

[0456] "Automatic video generation" is the process of generating video content using pre-defined algorithms or AI models based on user data.

[0457] "Posting support" is a function that automatically posts the generated video content to the online platform specified by the user.

[0458] This invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Specific embodiments for realizing this system are described below.

[0459] Retrieving User Data

[0460] The user first consents to access the online platform. After obtaining consent, the device collects user data, such as message data, image data, and video data. This data is sent to a server and stored in a database.

[0461] Data analysis

[0462] The server launches a generative AI module to analyze the stored user data. Natural language processing and image recognition technologies are used for the analysis. For example, text information contained in message data is analyzed to extract the user's interests. Image and video data are also identified using image recognition algorithms, and this is also used as information about the user's preferences.

[0463] Video content generation

[0464] Based on the analysis results, the server generates multiple patterns of video content that are optimal for the user. The generation AI automatically edits the storyboard using existing image data and video data clips. This generates high-quality video content that is in line with the user's interests.

[0465] Creating and providing preview data

[0466] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[0467] Submission support

[0468] When a user presses the post button for the selected video, the device sends the selection information to the server, which then converts the selected video into the online platform's posting format and posts the video to the specified account, allowing users to easily share high-quality video content on the online platform.

[0469] Hardware and software used

[0470] The system is implemented using the following hardware and software:

[0471] Hardware: Smartphones, smart glasses

[0472] software:

[0473] OpenAI API (natural language processing)

[0474] MoviePy (video editing)

[0475] requests (data acquisition and transmission)

[0476] cv2 (image processing)

[0477] Specific examples

[0478] For example, if a user has a lot of travel-related data, they can input the following prompt sentence into the generative AI model:

[0479] Analyze user travel data and generate a high-quality travel video. Photos to be used include beaches, mountains, cityscapes, etc. The storyboard sequence should be arrival, sightseeing, dining, departure.

[0480] Based on this prompt, the generative AI automatically generates a travel video, which users can then preview and easily post to an online platform.

[0481] This invention enables users to quickly and easily create and post high-quality video content that matches their interests.

[0482] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0483] Step 1:

[0484] The user consents to access to the online platform. After obtaining the user's consent, the device collects user data such as message data, image data, and video data. The collected data is sent to a server and stored in a database. The input is user data, and the output is the user data stored in the database.

[0485] Step 2:

[0486] The server launches a generative AI module to analyze the stored user data. The input is the user data stored in the database, and the output is the analyzed data. Specifically, it uses natural language processing technology to analyze message data and extract user interests. It also uses image recognition technology to identify the content of image data and video data and obtain information about the user's preferences.

[0487] Step 3:

[0488] The server automatically generates multiple patterns of video content based on the analysis results. The input is the analysis results, and the output is multiple generated video content. Based on the storyboard, the generation AI automatically edits existing image data and video data clips to generate high-quality video content that is optimal for the user.

[0489] Step 4:

[0490] The server creates a preview of the generated video content and sends it to the terminal. The input is the generated video content, and the output is the preview data sent to the terminal. Specifically, a shortened version of the video for preview is generated and sent to the user's terminal.

[0491] Step 5:

[0492] The user checks the preview data and selects the most suitable pattern. The input is the preview data sent from the server, and the output is the selected video content. Specifically, the user watches the preview and checks the overall atmosphere, including effects and background music.

[0493] Step 6:

[0494] When a user presses the post button for the selected video, the device sends the selection information to the server. The input is the selected video content, and the output is the transmission of the selection information to the server. Specifically, clicking the post button sends the selection information to the server.

[0495] Step 7:

[0496] The server converts the selected video into the posting format of the online platform and posts the video to the specified account. The input is the selection information, and the output is the completion of posting to the online platform. The specific operation is to convert the video into the appropriate format and automatically process the posting.

[0497] 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.

[0498] This invention is a system that automatically generates multiple patterns of optimal video content based on user data, enables users to easily post the content to an online platform, and further combines it with an emotion engine that recognizes the user's emotions. Below, an embodiment of the system will be described based on a specific example.

[0499] Retrieving User Data

[0500] The user first consents to access the online platform. After obtaining consent, the device collects data such as chat data, photos, and videos from the user. This data is then sent to a server and stored in a database.

[0501] Data analysis

[0502] The server launches a generative AI module and emotion engine to analyze the stored user data. Natural language processing and image recognition technologies are used for the analysis. For example, text information contained in chat data is analyzed to extract not only the user's interests but also their emotional state. Image recognition algorithms are also used to identify the content of photos and videos, which are then used as information on the user's preferences and emotions.

[0503] Use of emotion engine

[0504] The emotion engine analyzes emotions from the acquired user data. This engine analyzes the user's text and facial expressions in videos to determine the user's emotional state. For example, if the engine detects from the chat data that the user is "having fun," it will use more cheerful content and brighter effects in the video content.

[0505] Video content generation

[0506] Based on the analysis results, the generative AI designs a storyboard of video content that is optimal for the user. Multiple video patterns are automatically generated based on this storyboard. Effects and music based on the results of the emotion engine are also added to the generated videos. For example, if the user is having fun, cheerful music and bright effects are used.

[0507] Creating and providing preview data

[0508] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[0509] Submission support

[0510] When the user presses the post button for the selected video, the device sends the selection information to the server, which converts the selected video into a posting format for the online platform and posts the video to the specified account.

[0511] Specific examples

[0512] For example, consider the case where User B frequently shares fun topics about their daily life on LINE. User B often posts smiling photos and videos. When User B uses this system, they first authorize access to LINE VOOM and upload photos and videos of their daily life from their device. The server analyzes this data, and the emotion engine detects the emotion of enjoyment. Based on the analysis results, the generation AI generates multiple fun video patterns.

[0513] Next, previews of the generated fun videos are sent to User B's device, and User B can review and select them. Once the selection is complete, the selected video is posted to LINE VOOM. In this way, User B can easily post high-quality, fun videos.

[0514] This system enables the generation of video content that reflects the user's emotions, further enriching the user's digital experience. Furthermore, emotion-based customization enables the provision of more personalized content. In this way, it is possible to support users in actively sharing information on an ongoing basis.

[0515] The processing flow will be explained below.

[0516] Step 1:

[0517] The user consents to access the online platform. The device obtains this consent and sends the authentication information to the server.

[0518] Step 2:

[0519] The device collects chat data, photos, and videos specified by the user and uploads them to the server, which then stores the uploaded data in a database.

[0520] Step 3:

[0521] The server starts the generative AI module and emotion engine, retrieves the saved user data from the database, and begins analysis.

[0522] Step 4:

[0523] The generative AI uses natural language processing technology to analyze the conversation data and extract the user's interests and emotions. For example, it identifies emotional keywords such as "fun" and "stressful" from the conversation data.

[0524] Step 5:

[0525] Generative AI uses image recognition technology to analyze photos and videos, identifying objects and scenes contained in them and simultaneously analyzing emotions from people's facial expressions.

[0526] Step 6:

[0527] The emotion engine analyzes the user's emotional state from chat data and image / video data. For example, if a user has many photos and videos of themselves having fun, it will identify their emotional state as "having fun."

[0528] Step 7:

[0529] Based on the analysis results, the generative AI designs a storyboard for video content that reflects the user's preferences and emotional state. Based on the results of the emotion engine, multiple video patterns are automatically generated, using fun music and bright effects, for example.

[0530] Step 8:

[0531] The server creates a preview of the generated video content and sends it to the device, which displays multiple previews to the user.

[0532] Step 9:

[0533] The user selects the video they like best from the previewed videos and presses the submit button, and the device sends this selection information to the server.

[0534] Step 10:

[0535] The server converts the selected video content into a posting format for the online platform and posts the video to the specified account.

[0536] Step 11:

[0537] The server collects engagement data (e.g., number of likes and comments) after posting, along with user feedback, which helps improve the accuracy of the generation AI.

[0538] Example 2

[0539] 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."

[0540] Conventional systems have the problem that the process for users to create effective video content is complicated, time-consuming, and laborious. In addition, it is difficult to generate personalized content based on emotions, and it has not been possible to automatically generate video content that is directly linked to the user's interests and emotions.

[0541] 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.

[0542] In this invention, the server includes means for acquiring user data, means for transmitting the acquired user data to the server and storing it in a database, means for activating a generation AI module and an emotion engine to analyze the stored user data and extract the user's interests and emotional state using natural language processing technology and image recognition technology, means for designing a storyboard of video content optimal for the user based on the analysis results and automatically generating multiple video patterns, means for transmitting previews of the generated multiple video content to the user's device so that the user can select one, and means for posting the selected video content to an online platform. This enables users to easily and quickly generate and post personalized video content based on emotions.

[0543] "User data" refers to information including text data, image data, and video data provided by a user.

[0544] A "server" is a system of electronic devices and software for receiving, storing, and analyzing user data.

[0545] A "database" is a system for managing and storing user data stored on a server.

[0546] The "generative AI module" is a module that uses artificial intelligence technology to analyze user data and generate optimal video content.

[0547] An "emotion engine" is a system for analyzing a user's emotional state from their text data and image data.

[0548] "Natural language processing technology" is a technology for analyzing a user's text data and understanding its meaning and emotions.

[0549] "Image recognition technology" is a technology that analyzes a user's image data and video data and recognizes their content and emotions.

[0550] A "storyboard" is a blueprint that visually represents the structure and flow of scenes in video content.

[0551] An "online platform" is a website or application for posting and sharing user-generated video content.

[0552] A "preview" is a video that is temporarily displayed to allow the user to check the generated video content.

[0553] This invention is a system that automatically generates multiple patterns of optimal video content based on user data and allows users to easily post them to online platforms. It also combines an emotion engine that recognizes the user's emotions.

[0554] First, a user consents to access the online platform to begin using the system. After obtaining the user's consent, the device collects data such as the user's chat data, photos, and videos. This data is sent to a server and stored in a database. The database is a system for managing and storing user data stored on the server.

[0555] The server then launches a generative AI module and an emotion engine to analyze the stored user data. The generative AI module uses natural language processing technology to analyze the text information in the chat data and extract the user's interests and emotional state. The emotion engine uses image recognition technology to identify the content of photos and videos and determine the user's emotional state.

[0556] For example, if the AI ​​detects that the user is enjoying the content, it will use the analysis results to design a storyboard for the optimal video content for the user and automatically generate multiple video patterns. The generated videos will also include effects and music based on the results of the emotion engine.

[0557] A preview of the generated video content is generated by the server and sent to the terminal. The user can check this preview data and select the pattern that seems most suitable. When the user selects the selected video content, the selection information is sent from the terminal to the server. The server converts the selected video into a posting format for the online platform and posts the video to the specified account.

[0558] As a concrete example, consider the case where User B frequently shares fun topics about daily life on a communication app. User B posts many smiling photos and videos during these sessions. When User B uses this system, he or she first grants permission to access the video sharing function on the communication app and uploads photos and videos of daily life from his or her device. The server analyzes this data, and the emotion engine detects the emotion of enjoyment. Based on the analysis results, the generation AI generates multiple fun video patterns.

[0559] Next, previews of the generated fun videos are sent to the device, and User B can review and select them. Once selection is complete, the selected video is posted to the video sharing function of the communication app. In this way, User B can easily post high-quality, fun videos.

[0560] An example of a prompt is, "Based on the following talk data and photos, please generate video content that you believe the user is enjoying."

[0561] This system enables the generation of video content that reflects the user's emotions, further enriching the user's digital experience. Furthermore, emotion-based customization enables the provision of more personalized content. In this way, it is possible to support users in actively sharing information on an ongoing basis.

[0562] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0563] Step 1: Get User Data

[0564] To start using the system, the user consents to access the online platform. After confirming this consent, the device collects data such as the user's chat data, photos, and videos. The input data is text data, image data, and video data provided by the user, and the output is data sent from the device to the server. The device then sends the acquired data to the server.

[0565] Step 2: Save your data

[0566] The server receives the user data sent from the terminal.The server then stores the received data in a database.The input is the user data received from the terminal, and the output is the data stored in the database.

[0567] Step 3: Analyze the data

[0568] The server launches a generative AI module and an emotion engine to analyze user data stored in the database. The generative AI module uses natural language processing technology to analyze text information in the chat data and extract the user's interests and emotional state. The emotion engine uses image recognition technology to identify the content of photos and videos and determine the user's emotional state. The input is the user data stored in the database, and the output is the analysis results. Based on this, the server identifies the user's interests and emotional state.

[0569] Step 4: Sentiment Analysis

[0570] The emotion engine on the server analyzes the user's emotional state from the acquired user data. For example, the emotion "enjoying" may be detected from chat data. The input is the analyzed user data, and the output is the result of the judgment of the emotional state. The emotion engine identifies emotions from the user's text and facial expressions in images.

[0571] Step 5: Generate video content

[0572] The server's generation AI designs a storyboard of video content that is optimal for the user based on the analysis results of the emotion engine. It then automatically generates multiple video patterns based on this storyboard. Effects and music based on the results of the emotion engine are added to the generated videos. The input is the analysis results of the emotion engine, and the output is multiple video contents.

[0573] Step 6: Creating and providing preview data

[0574] The server creates preview data of the generated video content and sends it to the terminal. The input is the generated video content and the output is the preview data. The terminal presents the preview data to the user, allowing the user to check and select it.

[0575] Step 7: Select and post your video

[0576] The user selects the optimal video pattern and presses the post button. The device sends this selection information to the server. The server converts the selected video into the posting format of the online platform and posts the video to the specified account. The input is the user's video selection information, and the output is the video posted to the online platform.

[0577] In this way, users can easily create and post personalized emotion-based video content.

[0578] (Application example 2)

[0579] 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."

[0580] Conventional video content generation systems recommend and generate content without considering the user's emotional state, making it difficult to provide appropriate content that reflects the user's real-time emotional state. This results in a low-quality viewing experience and insufficient personalized experience. It also makes it difficult for users to efficiently find the content they desire, resulting in low satisfaction with content viewing.

[0581] 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.

[0582] In this invention, the server includes means for acquiring user data and real-time emotional data, means for analyzing the acquired user data and real-time emotional data, and means for automatically generating multiple video content items based on the analysis results, thereby enabling the provision of personalized content that reflects the user's real-time emotional state.

[0583] "User data" refers to information generated by a user, including message data, image data, and video data.

[0584] "Real-time emotional data" refers to data that represents the user's current emotional state, and is information that is primarily obtained from the user's facial expressions using image recognition technology.

[0585] "Natural language processing technology" is a technology for analyzing text data and understanding and extracting its content and meaning.

[0586] "Image recognition technology" is a technology that extracts and analyzes specific information from image data, and is particularly used for analyzing facial expressions.

[0587] "Emotion analysis technology" is a technology that identifies and analyzes a user's emotional state from text and image data.

[0588] "Personalized content" refers to content that is optimized based on the preferences and feelings of each individual user.

[0589] An "online platform" is a service or system available via the Internet that allows for the posting and sharing of videos.

[0590] The detailed description of the preferred embodiment of the present invention will be given using appropriate hardware and software.

[0591] System Configuration

[0592] This system acquires and analyzes user data and real-time emotional data, and generates and provides video content based on the analysis results. Specifically, it includes the following elements:

[0593] 1. User devices: Devices such as smart TVs and head-mounted displays (HMDs). These devices have built-in cameras and microphones to capture real-time emotional data from users.

[0594] 2. Server: Located in the cloud, it analyzes user data and emotional data using a generative AI model that combines natural language processing and image recognition technologies.

[0595] 3. Data transmission and reception means: A network configuration that transmits user data and emotion data to the server and sends the analysis results to the terminal.

[0596] System Operation

[0597] First, the user device collects user data, such as viewing history and real-time emotional data. The emotional data is acquired using a facial recognition camera, for example. With the user's consent, this data is sent to the server.

[0598] After receiving the data, the server analyzes the text data using natural language processing technology, while also analyzing real-time facial expression data using image recognition and emotion analysis technology. As a result of the analysis, the user's interests and emotional state are identified.

[0599] Next, the generative AI model automatically generates multiple video content pieces that are optimal for the user based on the analysis results. During this generation process, effects and music are also selected and applied according to the user's emotions. For example, if the user is having fun, cheerful music and bright effects are used.

[0600] The generated video content is sent to the user's device as a series of previews, where the user can review and select the video they consider most appropriate. Once selected, the selected video is posted to an online platform, enabling users to efficiently post personalized content based on their emotions.

[0601] Specific Examples

[0602] For example, consider a case where a user accesses the system through a smart TV and provides their viewing history and real-time emotional data. If the user smiles while watching a video, comedy movies or variety shows will be recommended based on the emotional data. The server generates appropriate content based on the analysis results and provides it to the user as a preview.

[0603] In this case, example prompts for the generative AI model might include, "If the user is having fun, what type of video would you recommend?" or "If you determine that the user is sad, what type of content would you recommend?"

[0604] In this way, our system provides personalized video recommendations that reflect the user's real-time emotions, enriching the user's viewing experience.

[0605] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0606] Step 1:

[0607] The user accesses the system via a smart TV or a head-mounted display (HMD).

[0608] Input: A request to access the system.

[0609] Output: Display of user consent screen.

[0610] How it works: The user's device displays a screen asking for consent to data collection and use of the facial recognition camera. If the user gives consent, the device proceeds to the next step.

[0611] Step 2:

[0612] The user terminal acquires user data and real-time emotion data.

[0613] Input: User viewing history, message data, and facial expression data.

[0614] Output: Collected user and sentiment data.

[0615] Operation: Using the built-in camera on the user's device, the system captures the user's facial expression data in real time, as well as collecting viewing history and message data.

[0616] Step 3:

[0617] The collected data is sent to a server.

[0618] Input: User data and real-time sentiment data.

[0619] Output: Sending data to the server.

[0620] Operation: The user device sends the acquired data to the server via the Internet.

[0621] Step 4:

[0622] The server analyzes the received data.

[0623] Input: User data and real-time emotion data sent to the server.

[0624] Output: Analysis results (user interests and emotional state).

[0625] How it works: The server uses natural language processing technology to analyze the user's viewing history and message data to extract the user's interests and concerns. At the same time, it uses image recognition and emotion analysis technology to analyze facial expression data and identify the user's emotional state.

[0626] Step 5:

[0627] Based on the analysis results, the server automatically generates multiple video contents using a generative AI model.

[0628] Input: Analysis results.

[0629] Output: Auto-generated video content.

[0630] How it works: The server inputs prompts into the generative AI model, which generates multiple optimal video content based on the user's emotions. For example, it uses prompts such as, "If the user is enjoying themselves, what kind of videos would you recommend?"

[0631] Step 6:

[0632] A preview of the generated video content is transmitted to the user terminal.

[0633] Input: Auto-generated video content.

[0634] Output: Video preview sent to user device.

[0635] Operation: The server sends the generated video content in a preview format to the user's device, which displays it for the user to review.

[0636] Step 7:

[0637] The user selects the video that seems most suitable.

[0638] Input: Video preview.

[0639] Output: The selected video content.

[0640] How it works: The user device accepts a selection from multiple previews and sends information about the video content selected by the user to the server.

[0641] Step 8:

[0642] The server posts the selected videos to an online platform.

[0643] Input: Selected video content information.

[0644] Output: Video posted on an online platform.

[0645] How it works: The server converts the selected video content into a posting format for the online platform and posts it to the specified account.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] [Third embodiment]

[0650] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0651] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0652] 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).

[0653] 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.

[0654] 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.

[0655] 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).

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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."

[0662] The present invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Hereinafter, an embodiment of the system will be described with reference to a specific example.

[0663] Retrieving User Data

[0664] The user first consents to access the online platform. After obtaining consent, the device collects data such as chat data, photos, and videos from the user. This data is then sent to a server and stored in a database.

[0665] Data analysis

[0666] The server launches a generative AI module to analyze the stored user data. This analysis uses natural language processing and image recognition technologies. For example, text information contained in chat data is analyzed to extract the user's interests. Images and videos are also identified using image recognition algorithms, which are then used to identify the user's preferences.

[0667] Video content generation

[0668] Based on the analysis results, the AI ​​generates multiple patterns of video content that are optimal for the user. For example, if a user has a lot of travel-related data, the AI ​​will create multiple patterns of travel-related video content. The generated videos are based on a storyboard and are automatically edited using existing photos and video clips.

[0669] Creating and providing preview data

[0670] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[0671] Submission support

[0672] When the user presses the post button for the selected video, the device sends the selection information to the server, which converts the selected video into a posting format for the online platform and posts the video to the specified account.

[0673] Specific examples

[0674] For example, consider the case where User A uses LINE to send many messages about travel and cooking. User A also has many photos and videos taken at his travel destinations. When User A uses this system, he first authorizes access to LINE VOOM and uploads his travel photos and videos from his device. The server analyzes this data and generates multiple travel videos based on travel-related keywords and images.

[0675] Next, previews of the generated travel videos are sent to the device, and User A can review and select them. Once selection is complete, the selected video is posted to LINE VOOM. In this way, User A can easily post high-quality travel videos.

[0676] This system allows users to significantly reduce the time and effort required to create and edit video content. It also improves the quality of posted content, which is expected to increase user engagement. In this way, it is possible to support users in continuously posting content.

[0677] The processing flow will be explained below.

[0678] Step 1:

[0679] The user consents to access the online platform. The device obtains this consent and sends the authentication information to the server.

[0680] Step 2:

[0681] The device collects chat data, photos, and videos specified by the user and uploads them to the server, which then stores the uploaded data in a database.

[0682] Step 3:

[0683] The server starts the generation AI module, retrieves the saved user data from the database, and begins analysis.

[0684] Step 4:

[0685] Generative AI uses natural language processing technology to analyze chat data and extract user interests and preferences, while image recognition technology is used to analyze photos and videos and identify the objects and scenes they contain.

[0686] Step 5:

[0687] Based on the analysis results, the generative AI designs a storyboard of video content that is optimal for the user. Multiple video patterns are automatically generated based on this storyboard. Effects and music can also be added to the generated videos.

[0688] Step 6:

[0689] The server converts the generated video content into a preview format and transmits it to the terminal, which displays multiple previews to the user.

[0690] Step 7:

[0691] The user selects the video they like best from the previewed videos and presses the submit button, and the device sends this selection information to the server.

[0692] Step 8:

[0693] The server converts the selected video content into a posting format for the online platform and posts the video to the specified account.

[0694] Step 9:

[0695] The server collects engagement data (e.g., number of likes and comments) after posting, along with user feedback, which helps improve the accuracy of the generation AI.

[0696] Example 1

[0697] 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."

[0698] Modern social media platforms require users to post high-quality video content, but video editing and creation requires technical knowledge and a significant amount of time. This makes it difficult for average users to easily create and post high-quality video content. Automatic generation of personalized content based on user interests is also a challenging task.

[0699] 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.

[0700] In this invention, the server includes means for transmitting user data to the server using a security protocol and storing it in a database, means for activating a generation AI module to analyze the stored user data, means for using natural language processing technology and image recognition technology for the analysis to extract keywords, emotions, and interests from text information, means for creating prompts based on the analysis results and automatically generating multiple video content items using the generation AI, means for creating preview data of the generated multiple video content items and sending it to a terminal, means for a user to check and select the preview data on the terminal, and means for converting the selected video content items into a posting format for an online platform and posting the video items to a specified account. This enables users to easily generate and edit high-quality video content items and post them to an online platform without any special technical skills.

[0701] "User data" is a general term for information including text data, image data, and video data related to a user.

[0702] A "security protocol" is a communication protocol for encrypting and protecting data during transmission.

[0703] A "server" is a sophisticated computer system for analyzing, storing, and processing data.

[0704] A "database" is a system for efficiently storing, retrieving, and managing collected user data.

[0705] A "generative AI module" is a software module that uses artificial intelligence techniques to analyze data and generate new content. Examples include GPT-4 and DALL·E.

[0706] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human-spoken language. Examples include GPT for text analysis.

[0707] "Image recognition technology" is a technology that allows a computer to analyze the content of images and videos and recognize specific objects or scenes. Examples include OpenCV.

[0708] A "prompt" is an input sentence that conveys specific instructions or requests to the generating AI.

[0709] A "storyboard" is a diagram used in video production that illustrates the development of footage.

[0710] "Preview data" refers to samples or sample data of multiple video contents that are generated so that the user can check them.

[0711] An "online platform" is an internet service that allows users to post, share, and view content.

[0712] A "post format" is a format or standard for publishing content on an online platform.

[0713] This invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Hereinafter, an embodiment of this system will be described with specific examples.

[0714] Retrieving User Data

[0715] First, the user must consent to access to a specific online platform (e.g., a social media site). Once the user consents, the device collects the user's message data, image data, and video data. The collected data is transmitted to a server using a security protocol, and the server stores it in a database.

[0716] Data analysis

[0717] The server launches a generative AI module to analyze the user data stored in the database. This analysis uses natural language processing technology (e.g., GPT-4) and image recognition technology (e.g., OpenCV). Specifically, the server uses GPT-4 to extract keywords, emotions, and interests from text information, and OpenCV to analyze image and video content. This allows the server to identify the user's preferences and interests.

[0718] Video content generation

[0719] The server creates prompts based on the analysis results and automatically generates multiple video contents using generative AI (for example, a video generation version of DALL·E). The generated video contents are edited based on a storyboard and seamlessly combined with existing photos and video clips.

[0720] Creating and providing preview data

[0721] A preview of the generated video content is created by the server and sent to the device. The user can view multiple previews on the device and select the video pattern they think is best. The preview also includes effects and background music, so the user can check the overall atmosphere of the completed video.

[0722] Submission support

[0723] When a user selects a video and presses the "Post" button, the device sends the selection information to the server. The server then converts the selected video into the posting format for the specified online platform and automatically posts it to the specified account. The user can then view the final post on the device.

[0724] Specific examples

[0725] For example, consider the case where User A sends many messages about travel and cooking using a messaging app. User A also has many photos and videos taken at his travel destinations. When User A uses this system, he first consents to access a specific online platform (e.g., a travel sharing site) and uploads his travel photos and videos from his device. The server analyzes this data and generates multiple travel videos based on keywords and images related to "travel" and "cooking."

[0726] Next, previews of the generated travel videos are sent to the device, and User A can review and select them. Once the selection is complete, the selected videos are posted to the travel sharing site. In this way, User A can easily post high-quality travel videos.

[0727] Prompt Sentence Examples

[0728] "Given the following travel photo and message data, generate a travel video to post on a social media site. Use GPT-4 for text analysis and OpenCV for image recognition."

[0729] This system allows users to create and edit high-quality video content without any technical skills and easily post it to online platforms. It also enables the generation of personalized content based on users' interests, which is expected to increase engagement.

[0730] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0731] Step 1: Get User Data

[0732] The user consents to access the online platform. With the user's consent, the device collects message data, image data, and video data. The collected data is encrypted using a security protocol and sent to the server. The input is the user's consent and various data, and the output is the transmission of encrypted user data to the server.

[0733] Step 2: Save your data

[0734] The server receives the encrypted data sent from the terminal and stores it in a database. Specific operations include decrypting the data and writing it to the database early. The input is the encrypted user data, and the output is the user data stored in the database.

[0735] Step 3: Launching the analysis module

[0736] The server launches the generation AI module to analyze the stored user data. The input is the user data stored in the database, and the output is the launch of the analysis module. Specifically, the analysis module is initialized and the data is loaded.

[0737] Step 4: Analyzing the text data

[0738] The server uses GPT-4 to analyze user message data and extract keywords, emotions, and interests. The input is the user message data, and the output is the extracted keywords, emotions, and interests. Specific data processing includes tokenization, emotion scoring, and keyword extraction.

[0739] Step 5: Image and video data analysis

[0740] The server uses OpenCV to analyze the user's image and video data and identify scenes and objects. The input is the user's image and video data, and the output is a list of identified scenes and objects. Specifically, processing such as edge detection, object recognition, and scene classification is performed.

[0741] Step 6: Create a prompt

[0742] Based on the analysis results, the server creates a prompt to be passed to the generation AI. The input is the analysis results (keywords, emotions, interests, and a list of scenes and objects), and the output is the prompt text. Specifically, the text is generated by inserting values ​​into the appropriate template.

[0743] Step 7: Auto-generate videos

[0744] The generation AI automatically generates video content based on prompts. The input is the prompt text and the user's image and video data, and the output is an automatically generated video. The generation AI performs editing tasks such as arranging scenes, applying effects, and adding text.

[0745] Step 8: Creating Preview Data

[0746] The server creates preview data from automatically generated video content. The input is the automatically generated video, and the output is the preview data. Specifically, sample clips of the video and thumbnails are generated.

[0747] Step 9: Send preview data

[0748] The server sends the preview data to the terminal. The input is the preview data, and the output is the preview data sent to the terminal. This includes data compression and transmission.

[0749] Step 10: Preview and select

[0750] The user checks the preview data on the device and selects the most suitable video pattern. The input is the preview data displayed on the device, and the output is the user's selection information. Specific actions include rating each video and pressing the selection button.

[0751] Step 11: Submit your selections

[0752] The terminal sends the user's selection information to the server. The input is the user's selection information, and the output is the selection information sent to the server. This involves packaging the selection information and using a transmission protocol.

[0753] Step 12: Post your video

[0754] The server converts the selected video into a posting format for the online platform and posts the video to the specified account. The input is the selected video and posting format information, and the output is the video posted on the online platform. Specifically, this process includes format conversion, calling the platform API, and confirming that posting is complete.

[0755] (Application example 1)

[0756] 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."

[0757] The conventional content creation and posting process is laborious and time-consuming for users. It is also difficult to automatically generate high-quality video content that accurately reflects users' interests. Furthermore, there is a lack of a mechanism for quickly and easily posting the generated content to a platform. The objective of this invention is to solve these problems and provide a system that allows users to easily create and post high-quality video content.

[0758] 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.

[0759] In this invention, the server includes means for acquiring user data, means for analyzing the acquired user data, means for automatically generating multiple video contents based on the analysis results, means for allowing a user to select from the generated video contents, means for providing a preview of the generated video contents, and means for allowing a user to easily post the selected video, thereby enabling a user to quickly and easily generate and post video contents that suit their own interests.

[0760] Below are definitions of key terms based on the patent claims, rewritten to fit the application.

[0761] "User data" is a general term for information such as message data, image data, and video data that is generated or held by a user.

[0762] "Analysis" is the process of extracting useful information from collected user data and determining the user's hobbies and interests.

[0763] "Video content" refers to digital content in video format that is generated using technologies such as generative AI and is viewable by users.

[0764] "Preview" refers to a shortened display or trial viewing that allows you to check the overall picture and atmosphere of the generated video content in advance.

[0765] An "online platform" is an internet service or website where video content is published and users can view and share it.

[0766] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0767] "Image recognition technology" is a computer vision technology for identifying and analyzing useful information from image data.

[0768] "Generative AI" is an artificial intelligence technology that automatically generates new content based on large amounts of data.

[0769] "Automatic video generation" is the process of generating video content using pre-defined algorithms or AI models based on user data.

[0770] "Posting support" is a function that automatically posts the generated video content to the online platform specified by the user.

[0771] This invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Specific embodiments for realizing this system are described below.

[0772] Retrieving User Data

[0773] The user first consents to access the online platform. After obtaining consent, the device collects user data, such as message data, image data, and video data. This data is sent to a server and stored in a database.

[0774] Data analysis

[0775] The server launches a generative AI module to analyze the stored user data. Natural language processing and image recognition technologies are used for the analysis. For example, text information contained in message data is analyzed to extract the user's interests. Image and video data are also identified using image recognition algorithms, and this is also used as information about the user's preferences.

[0776] Video content generation

[0777] Based on the analysis results, the server generates multiple patterns of video content that are optimal for the user. The generation AI automatically edits the storyboard using existing image data and video data clips. This generates high-quality video content that is in line with the user's interests.

[0778] Creating and providing preview data

[0779] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[0780] Submission support

[0781] When a user presses the post button for the selected video, the device sends the selection information to the server, which then converts the selected video into the online platform's posting format and posts the video to the specified account, allowing users to easily share high-quality video content on the online platform.

[0782] Hardware and software used

[0783] The system is implemented using the following hardware and software:

[0784] Hardware: Smartphones, smart glasses

[0785] software:

[0786] OpenAI API (natural language processing)

[0787] MoviePy (video editing)

[0788] requests (data acquisition and transmission)

[0789] cv2 (image processing)

[0790] Specific examples

[0791] For example, if a user has a lot of travel-related data, they can input the following prompt sentence into the generative AI model:

[0792] Analyze user travel data and generate a high-quality travel video. Photos to be used include beaches, mountains, cityscapes, etc. The storyboard sequence should be arrival, sightseeing, dining, departure.

[0793] Based on this prompt, the generative AI automatically generates a travel video, which users can then preview and easily post to an online platform.

[0794] This invention enables users to quickly and easily create and post high-quality video content that matches their interests.

[0795] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0796] Step 1:

[0797] The user consents to access to the online platform. After obtaining the user's consent, the device collects user data such as message data, image data, and video data. The collected data is sent to a server and stored in a database. The input is user data, and the output is the user data stored in the database.

[0798] Step 2:

[0799] The server launches a generative AI module to analyze the stored user data. The input is the user data stored in the database, and the output is the analyzed data. Specifically, it uses natural language processing technology to analyze message data and extract user interests. It also uses image recognition technology to identify the content of image data and video data and obtain information about the user's preferences.

[0800] Step 3:

[0801] The server automatically generates multiple patterns of video content based on the analysis results. The input is the analysis results, and the output is multiple generated video content. Based on the storyboard, the generation AI automatically edits existing image data and video data clips to generate high-quality video content that is optimal for the user.

[0802] Step 4:

[0803] The server creates a preview of the generated video content and sends it to the terminal. The input is the generated video content, and the output is the preview data sent to the terminal. Specifically, a shortened version of the video for preview is generated and sent to the user's terminal.

[0804] Step 5:

[0805] The user checks the preview data and selects the most suitable pattern. The input is the preview data sent from the server, and the output is the selected video content. Specifically, the user watches the preview and checks the overall atmosphere, including effects and background music.

[0806] Step 6:

[0807] When a user presses the post button for the selected video, the device sends the selection information to the server. The input is the selected video content, and the output is the transmission of the selection information to the server. Specifically, clicking the post button sends the selection information to the server.

[0808] Step 7:

[0809] The server converts the selected video into the posting format of the online platform and posts the video to the specified account. The input is the selection information, and the output is the completion of posting to the online platform. The specific operation is to convert the video into the appropriate format and automatically process the posting.

[0810] 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.

[0811] This invention is a system that automatically generates multiple patterns of optimal video content based on user data, enables users to easily post the content to an online platform, and further combines it with an emotion engine that recognizes the user's emotions. Below, an embodiment of the system will be described based on a specific example.

[0812] Retrieving User Data

[0813] The user first consents to access the online platform. After obtaining consent, the device collects data such as chat data, photos, and videos from the user. This data is then sent to a server and stored in a database.

[0814] Data analysis

[0815] The server launches a generative AI module and emotion engine to analyze the stored user data. Natural language processing and image recognition technologies are used for the analysis. For example, text information contained in chat data is analyzed to extract not only the user's interests but also their emotional state. Image recognition algorithms are also used to identify the content of photos and videos, which are then used as information on the user's preferences and emotions.

[0816] Use of emotion engine

[0817] The emotion engine analyzes emotions from the acquired user data. This engine analyzes the user's text and facial expressions in videos to determine the user's emotional state. For example, if the engine detects from the chat data that the user is "having fun," it will use more cheerful content and brighter effects in the video content.

[0818] Video content generation

[0819] Based on the analysis results, the generative AI designs a storyboard of video content that is optimal for the user. Multiple video patterns are automatically generated based on this storyboard. Effects and music based on the results of the emotion engine are also added to the generated videos. For example, if the user is having fun, cheerful music and bright effects are used.

[0820] Creating and providing preview data

[0821] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[0822] Submission support

[0823] When the user presses the post button for the selected video, the device sends the selection information to the server, which converts the selected video into a posting format for the online platform and posts the video to the specified account.

[0824] Specific examples

[0825] For example, consider the case where User B frequently shares fun topics about their daily life on LINE. User B often posts smiling photos and videos. When User B uses this system, they first authorize access to LINE VOOM and upload photos and videos of their daily life from their device. The server analyzes this data, and the emotion engine detects the emotion of enjoyment. Based on the analysis results, the generation AI generates multiple fun video patterns.

[0826] Next, previews of the generated fun videos are sent to User B's device, and User B can review and select them. Once the selection is complete, the selected video is posted to LINE VOOM. In this way, User B can easily post high-quality, fun videos.

[0827] This system enables the generation of video content that reflects the user's emotions, further enriching the user's digital experience. Furthermore, emotion-based customization enables the provision of more personalized content. In this way, it is possible to support users in actively sharing information on an ongoing basis.

[0828] The processing flow will be explained below.

[0829] Step 1:

[0830] The user consents to access the online platform. The device obtains this consent and sends the authentication information to the server.

[0831] Step 2:

[0832] The device collects chat data, photos, and videos specified by the user and uploads them to the server, which then stores the uploaded data in a database.

[0833] Step 3:

[0834] The server starts the generative AI module and emotion engine, retrieves the saved user data from the database, and begins analysis.

[0835] Step 4:

[0836] The generative AI uses natural language processing technology to analyze the conversation data and extract the user's interests and emotions. For example, it identifies emotional keywords such as "fun" and "stressful" from the conversation data.

[0837] Step 5:

[0838] Generative AI uses image recognition technology to analyze photos and videos, identifying objects and scenes contained in them and simultaneously analyzing emotions from people's facial expressions.

[0839] Step 6:

[0840] The emotion engine analyzes the user's emotional state from chat data and image / video data. For example, if a user has many photos and videos of themselves having fun, it will identify their emotional state as "having fun."

[0841] Step 7:

[0842] Based on the analysis results, the generative AI designs a storyboard for video content that reflects the user's preferences and emotional state. Based on the results of the emotion engine, multiple video patterns are automatically generated, using fun music and bright effects, for example.

[0843] Step 8:

[0844] The server creates a preview of the generated video content and sends it to the device, which displays multiple previews to the user.

[0845] Step 9:

[0846] The user selects the video they like best from the previewed videos and presses the submit button, and the device sends this selection information to the server.

[0847] Step 10:

[0848] The server converts the selected video content into a posting format for the online platform and posts the video to the specified account.

[0849] Step 11:

[0850] The server collects engagement data (e.g., number of likes and comments) after posting, along with user feedback, which helps improve the accuracy of the generation AI.

[0851] Example 2

[0852] 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."

[0853] Conventional systems have the problem that the process for users to create effective video content is complicated, time-consuming, and laborious. In addition, it is difficult to generate personalized content based on emotions, and it has not been possible to automatically generate video content that is directly linked to the user's interests and emotions.

[0854] 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.

[0855] In this invention, the server includes means for acquiring user data, means for transmitting the acquired user data to the server and storing it in a database, means for activating a generation AI module and an emotion engine to analyze the stored user data and extract the user's interests and emotional state using natural language processing technology and image recognition technology, means for designing a storyboard of video content optimal for the user based on the analysis results and automatically generating multiple video patterns, means for transmitting previews of the generated multiple video content to the user's device so that the user can select one, and means for posting the selected video content to an online platform. This enables users to easily and quickly generate and post personalized video content based on emotions.

[0856] "User data" refers to information including text data, image data, and video data provided by a user.

[0857] A "server" is a system of electronic devices and software for receiving, storing, and analyzing user data.

[0858] A "database" is a system for managing and storing user data stored on a server.

[0859] The "generative AI module" is a module that uses artificial intelligence technology to analyze user data and generate optimal video content.

[0860] An "emotion engine" is a system for analyzing a user's emotional state from their text data and image data.

[0861] "Natural language processing technology" is a technology for analyzing a user's text data and understanding its meaning and emotions.

[0862] "Image recognition technology" is a technology that analyzes a user's image data and video data and recognizes their content and emotions.

[0863] A "storyboard" is a blueprint that visually represents the structure and flow of scenes in video content.

[0864] An "online platform" is a website or application for posting and sharing user-generated video content.

[0865] A "preview" is a video that is temporarily displayed to allow the user to check the generated video content.

[0866] This invention is a system that automatically generates multiple patterns of optimal video content based on user data and allows users to easily post them to online platforms. It also combines an emotion engine that recognizes the user's emotions.

[0867] First, a user consents to access the online platform to begin using the system. After obtaining the user's consent, the device collects data such as the user's chat data, photos, and videos. This data is sent to a server and stored in a database. The database is a system for managing and storing user data stored on the server.

[0868] The server then launches a generative AI module and an emotion engine to analyze the stored user data. The generative AI module uses natural language processing technology to analyze the text information in the chat data and extract the user's interests and emotional state. The emotion engine uses image recognition technology to identify the content of photos and videos and determine the user's emotional state.

[0869] For example, if the AI ​​detects that the user is enjoying the content, it will use the analysis results to design a storyboard for the optimal video content for the user and automatically generate multiple video patterns. The generated videos will also include effects and music based on the results of the emotion engine.

[0870] A preview of the generated video content is generated by the server and sent to the terminal. The user can check this preview data and select the pattern that seems most suitable. When the user selects the selected video content, the selection information is sent from the terminal to the server. The server converts the selected video into a posting format for the online platform and posts the video to the specified account.

[0871] As a concrete example, consider the case where User B frequently shares fun topics about daily life on a communication app. User B posts many smiling photos and videos during these sessions. When User B uses this system, he or she first grants permission to access the video sharing function on the communication app and uploads photos and videos of daily life from his or her device. The server analyzes this data, and the emotion engine detects the emotion of enjoyment. Based on the analysis results, the generation AI generates multiple fun video patterns.

[0872] Next, previews of the generated fun videos are sent to the device, and User B can review and select them. Once selection is complete, the selected video is posted to the video sharing function of the communication app. In this way, User B can easily post high-quality, fun videos.

[0873] An example of a prompt is, "Based on the following talk data and photos, please generate video content that you believe the user is enjoying."

[0874] This system enables the generation of video content that reflects the user's emotions, further enriching the user's digital experience. Furthermore, emotion-based customization enables the provision of more personalized content. In this way, it is possible to support users in actively sharing information on an ongoing basis.

[0875] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0876] Step 1: Get User Data

[0877] To start using the system, the user consents to access the online platform. After confirming this consent, the device collects data such as the user's chat data, photos, and videos. The input data is text data, image data, and video data provided by the user, and the output is data sent from the device to the server. The device then sends the acquired data to the server.

[0878] Step 2: Save your data

[0879] The server receives the user data sent from the terminal.The server then stores the received data in a database.The input is the user data received from the terminal, and the output is the data stored in the database.

[0880] Step 3: Analyze the data

[0881] The server launches a generative AI module and an emotion engine to analyze user data stored in the database. The generative AI module uses natural language processing technology to analyze text information in the chat data and extract the user's interests and emotional state. The emotion engine uses image recognition technology to identify the content of photos and videos and determine the user's emotional state. The input is the user data stored in the database, and the output is the analysis results. Based on this, the server identifies the user's interests and emotional state.

[0882] Step 4: Sentiment Analysis

[0883] The emotion engine on the server analyzes the user's emotional state from the acquired user data. For example, the emotion "enjoying" may be detected from chat data. The input is the analyzed user data, and the output is the result of the judgment of the emotional state. The emotion engine identifies emotions from the user's text and facial expressions in images.

[0884] Step 5: Generate video content

[0885] The server's generation AI designs a storyboard of video content that is optimal for the user based on the analysis results of the emotion engine. It then automatically generates multiple video patterns based on this storyboard. Effects and music based on the results of the emotion engine are added to the generated videos. The input is the analysis results of the emotion engine, and the output is multiple video contents.

[0886] Step 6: Creating and providing preview data

[0887] The server creates preview data of the generated video content and sends it to the terminal. The input is the generated video content and the output is the preview data. The terminal presents the preview data to the user, allowing the user to check and select it.

[0888] Step 7: Select and post your video

[0889] The user selects the optimal video pattern and presses the post button. The device sends this selection information to the server. The server converts the selected video into the posting format of the online platform and posts the video to the specified account. The input is the user's video selection information, and the output is the video posted to the online platform.

[0890] In this way, users can easily create and post personalized emotion-based video content.

[0891] (Application example 2)

[0892] 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."

[0893] Conventional video content generation systems recommend and generate content without considering the user's emotional state, making it difficult to provide appropriate content that reflects the user's real-time emotional state. This results in a low-quality viewing experience and insufficient personalized experience. It also makes it difficult for users to efficiently find the content they desire, resulting in low satisfaction with content viewing.

[0894] 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.

[0895] In this invention, the server includes means for acquiring user data and real-time emotional data, means for analyzing the acquired user data and real-time emotional data, and means for automatically generating multiple video content items based on the analysis results, thereby enabling the provision of personalized content that reflects the user's real-time emotional state.

[0896] "User data" refers to information generated by a user, including message data, image data, and video data.

[0897] "Real-time emotional data" refers to data that represents the user's current emotional state, and is information that is primarily obtained from the user's facial expressions using image recognition technology.

[0898] "Natural language processing technology" is a technology for analyzing text data and understanding and extracting its content and meaning.

[0899] "Image recognition technology" is a technology that extracts and analyzes specific information from image data, and is particularly used for analyzing facial expressions.

[0900] "Emotion analysis technology" is a technology that identifies and analyzes a user's emotional state from text and image data.

[0901] "Personalized content" refers to content that is optimized based on the preferences and feelings of each individual user.

[0902] An "online platform" is a service or system available via the Internet that allows for the posting and sharing of videos.

[0903] The detailed description of the preferred embodiment of the present invention will be given using appropriate hardware and software.

[0904] System Configuration

[0905] This system acquires and analyzes user data and real-time emotional data, and generates and provides video content based on the analysis results. Specifically, it includes the following elements:

[0906] 1. User devices: Devices such as smart TVs and head-mounted displays (HMDs). These devices have built-in cameras and microphones to capture real-time emotional data from users.

[0907] 2. Server: Located in the cloud, it analyzes user data and emotional data using a generative AI model that combines natural language processing and image recognition technologies.

[0908] 3. Data transmission and reception means: A network configuration that transmits user data and emotion data to the server and sends the analysis results to the terminal.

[0909] System Operation

[0910] First, the user device collects user data, such as viewing history and real-time emotional data. The emotional data is acquired using a facial recognition camera, for example. With the user's consent, this data is sent to the server.

[0911] After receiving the data, the server analyzes the text data using natural language processing technology, while also analyzing real-time facial expression data using image recognition and emotion analysis technology. As a result of the analysis, the user's interests and emotional state are identified.

[0912] Next, the generative AI model automatically generates multiple video content pieces that are optimal for the user based on the analysis results. During this generation process, effects and music are also selected and applied according to the user's emotions. For example, if the user is having fun, cheerful music and bright effects are used.

[0913] The generated video content is sent to the user's device as a series of previews, where the user can review and select the video they consider most appropriate. Once selected, the selected video is posted to an online platform, enabling users to efficiently post personalized content based on their emotions.

[0914] Specific Examples

[0915] For example, consider a case where a user accesses the system through a smart TV and provides their viewing history and real-time emotional data. If the user smiles while watching a video, comedy movies or variety shows will be recommended based on the emotional data. The server generates appropriate content based on the analysis results and provides it to the user as a preview.

[0916] In this case, example prompts for the generative AI model might include, "If the user is having fun, what type of video would you recommend?" or "If you determine that the user is sad, what type of content would you recommend?"

[0917] In this way, our system provides personalized video recommendations that reflect the user's real-time emotions, enriching the user's viewing experience.

[0918] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0919] Step 1:

[0920] The user accesses the system via a smart TV or a head-mounted display (HMD).

[0921] Input: A request to access the system.

[0922] Output: Display of user consent screen.

[0923] How it works: The user's device displays a screen asking for consent to data collection and use of the facial recognition camera. If the user gives consent, the device proceeds to the next step.

[0924] Step 2:

[0925] The user terminal acquires user data and real-time emotion data.

[0926] Input: User viewing history, message data, and facial expression data.

[0927] Output: Collected user and sentiment data.

[0928] Operation: Using the built-in camera on the user's device, the system captures the user's facial expression data in real time, as well as collecting viewing history and message data.

[0929] Step 3:

[0930] The collected data is sent to a server.

[0931] Input: User data and real-time sentiment data.

[0932] Output: Sending data to the server.

[0933] Operation: The user device sends the acquired data to the server via the Internet.

[0934] Step 4:

[0935] The server analyzes the received data.

[0936] Input: User data and real-time emotion data sent to the server.

[0937] Output: Analysis results (user interests and emotional state).

[0938] How it works: The server uses natural language processing technology to analyze the user's viewing history and message data to extract the user's interests and concerns. At the same time, it uses image recognition and emotion analysis technology to analyze facial expression data and identify the user's emotional state.

[0939] Step 5:

[0940] Based on the analysis results, the server automatically generates multiple video contents using a generative AI model.

[0941] Input: Analysis results.

[0942] Output: Auto-generated video content.

[0943] How it works: The server inputs prompts into the generative AI model, which generates multiple optimal video content based on the user's emotions. For example, it uses prompts such as, "If the user is enjoying themselves, what kind of videos would you recommend?"

[0944] Step 6:

[0945] A preview of the generated video content is transmitted to the user terminal.

[0946] Input: Auto-generated video content.

[0947] Output: Video preview sent to user device.

[0948] Operation: The server sends the generated video content in a preview format to the user's device, which displays it for the user to review.

[0949] Step 7:

[0950] The user selects the video that seems most suitable.

[0951] Input: Video preview.

[0952] Output: The selected video content.

[0953] How it works: The user device accepts a selection from multiple previews and sends information about the video content selected by the user to the server.

[0954] Step 8:

[0955] The server posts the selected videos to an online platform.

[0956] Input: Selected video content information.

[0957] Output: Video posted on an online platform.

[0958] How it works: The server converts the selected video content into a posting format for the online platform and posts it to the specified account.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] [Fourth embodiment]

[0963] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0964] 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.

[0965] 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).

[0966] 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.

[0967] 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.

[0968] 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).

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] 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.

[0975] 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."

[0976] The present invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Hereinafter, an embodiment of the system will be described with reference to a specific example.

[0977] Retrieving User Data

[0978] The user first consents to access the online platform. After obtaining consent, the device collects data such as chat data, photos, and videos from the user. This data is then sent to a server and stored in a database.

[0979] Data analysis

[0980] The server launches a generative AI module to analyze the stored user data. This analysis uses natural language processing and image recognition technologies. For example, text information contained in chat data is analyzed to extract the user's interests. Images and videos are also identified using image recognition algorithms, which are then used to identify the user's preferences.

[0981] Video content generation

[0982] Based on the analysis results, the AI ​​generates multiple patterns of video content that are optimal for the user. For example, if a user has a lot of travel-related data, the AI ​​will create multiple patterns of travel-related video content. The generated videos are based on a storyboard and are automatically edited using existing photos and video clips.

[0983] Creating and providing preview data

[0984] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[0985] Submission support

[0986] When the user presses the post button for the selected video, the device sends the selection information to the server, which converts the selected video into a posting format for the online platform and posts the video to the specified account.

[0987] Specific examples

[0988] For example, consider the case where User A uses LINE to send many messages about travel and cooking. User A also has many photos and videos taken at his travel destinations. When User A uses this system, he first authorizes access to LINE VOOM and uploads his travel photos and videos from his device. The server analyzes this data and generates multiple travel videos based on travel-related keywords and images.

[0989] Next, previews of the generated travel videos are sent to the device, and User A can review and select them. Once selection is complete, the selected video is posted to LINE VOOM. In this way, User A can easily post high-quality travel videos.

[0990] This system allows users to significantly reduce the time and effort required to create and edit video content. It also improves the quality of posted content, which is expected to increase user engagement. In this way, it is possible to support users in continuously posting content.

[0991] The processing flow will be explained below.

[0992] Step 1:

[0993] The user consents to access the online platform. The device obtains this consent and sends the authentication information to the server.

[0994] Step 2:

[0995] The device collects chat data, photos, and videos specified by the user and uploads them to the server, which then stores the uploaded data in a database.

[0996] Step 3:

[0997] The server starts the generation AI module, retrieves the saved user data from the database, and begins analysis.

[0998] Step 4:

[0999] Generative AI uses natural language processing technology to analyze chat data and extract user interests and preferences, while image recognition technology is used to analyze photos and videos and identify the objects and scenes they contain.

[1000] Step 5:

[1001] Based on the analysis results, the generative AI designs a storyboard of video content that is optimal for the user. Multiple video patterns are automatically generated based on this storyboard. Effects and music can also be added to the generated videos.

[1002] Step 6:

[1003] The server converts the generated video content into a preview format and transmits it to the terminal, which displays multiple previews to the user.

[1004] Step 7:

[1005] The user selects the video they like best from the previewed videos and presses the submit button, and the device sends this selection information to the server.

[1006] Step 8:

[1007] The server converts the selected video content into a posting format for the online platform and posts the video to the specified account.

[1008] Step 9:

[1009] The server collects engagement data (e.g., number of likes and comments) after posting, along with user feedback, which helps improve the accuracy of the generation AI.

[1010] Example 1

[1011] 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."

[1012] Modern social media platforms require users to post high-quality video content, but video editing and creation requires technical knowledge and a significant amount of time. This makes it difficult for average users to easily create and post high-quality video content. Automatic generation of personalized content based on user interests is also a challenging task.

[1013] 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.

[1014] In this invention, the server includes means for transmitting user data to the server using a security protocol and storing it in a database, means for activating a generation AI module to analyze the stored user data, means for using natural language processing technology and image recognition technology for the analysis to extract keywords, emotions, and interests from text information, means for creating prompts based on the analysis results and automatically generating multiple video content items using the generation AI, means for creating preview data of the generated multiple video content items and sending it to a terminal, means for a user to check and select the preview data on the terminal, and means for converting the selected video content items into a posting format for an online platform and posting the video items to a specified account. This enables users to easily generate and edit high-quality video content items and post them to an online platform without any special technical skills.

[1015] "User data" is a general term for information including text data, image data, and video data related to a user.

[1016] A "security protocol" is a communication protocol for encrypting and protecting data during transmission.

[1017] A "server" is a sophisticated computer system for analyzing, storing, and processing data.

[1018] A "database" is a system for efficiently storing, retrieving, and managing collected user data.

[1019] A "generative AI module" is a software module that uses artificial intelligence techniques to analyze data and generate new content. Examples include GPT-4 and DALL·E.

[1020] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human-spoken language. Examples include GPT for text analysis.

[1021] "Image recognition technology" is a technology that allows a computer to analyze the content of images and videos and recognize specific objects or scenes. Examples include OpenCV.

[1022] A "prompt" is an input sentence that conveys specific instructions or requests to the generating AI.

[1023] A "storyboard" is a diagram used in video production that illustrates the development of footage.

[1024] "Preview data" refers to samples or sample data of multiple video contents that are generated so that the user can check them.

[1025] An "online platform" is an internet service that allows users to post, share, and view content.

[1026] A "post format" is a format or standard for publishing content on an online platform.

[1027] This invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Hereinafter, an embodiment of this system will be described with specific examples.

[1028] Retrieving User Data

[1029] First, the user must consent to access to a specific online platform (e.g., a social media site). Once the user consents, the device collects the user's message data, image data, and video data. The collected data is transmitted to a server using a security protocol, and the server stores it in a database.

[1030] Data analysis

[1031] The server launches a generative AI module to analyze the user data stored in the database. This analysis uses natural language processing technology (e.g., GPT-4) and image recognition technology (e.g., OpenCV). Specifically, the server uses GPT-4 to extract keywords, emotions, and interests from text information, and OpenCV to analyze image and video content. This allows the server to identify the user's preferences and interests.

[1032] Video content generation

[1033] The server creates prompts based on the analysis results and automatically generates multiple video contents using generative AI (for example, a video generation version of DALL·E). The generated video contents are edited based on a storyboard and seamlessly combined with existing photos and video clips.

[1034] Creating and providing preview data

[1035] A preview of the generated video content is created by the server and sent to the device. The user can view multiple previews on the device and select the video pattern they think is best. The preview also includes effects and background music, so the user can check the overall atmosphere of the completed video.

[1036] Submission support

[1037] When a user selects a video and presses the "Post" button, the device sends the selection information to the server. The server then converts the selected video into the posting format for the specified online platform and automatically posts it to the specified account. The user can then view the final post on the device.

[1038] Specific examples

[1039] For example, consider the case where User A sends many messages about travel and cooking using a messaging app. User A also has many photos and videos taken at his travel destinations. When User A uses this system, he first consents to access a specific online platform (e.g., a travel sharing site) and uploads his travel photos and videos from his device. The server analyzes this data and generates multiple travel videos based on keywords and images related to "travel" and "cooking."

[1040] Next, previews of the generated travel videos are sent to the device, and User A can review and select them. Once the selection is complete, the selected videos are posted to the travel sharing site. In this way, User A can easily post high-quality travel videos.

[1041] Prompt Sentence Examples

[1042] "Given the following travel photo and message data, generate a travel video to post on a social media site. Use GPT-4 for text analysis and OpenCV for image recognition."

[1043] This system allows users to create and edit high-quality video content without any technical skills and easily post it to online platforms. It also enables the generation of personalized content based on users' interests, which is expected to increase engagement.

[1044] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1045] Step 1: Get User Data

[1046] The user consents to access the online platform. With the user's consent, the device collects message data, image data, and video data. The collected data is encrypted using a security protocol and sent to the server. The input is the user's consent and various data, and the output is the transmission of encrypted user data to the server.

[1047] Step 2: Save your data

[1048] The server receives the encrypted data sent from the terminal and stores it in a database. Specific operations include decrypting the data and writing it to the database early. The input is the encrypted user data, and the output is the user data stored in the database.

[1049] Step 3: Launching the analysis module

[1050] The server launches the generation AI module to analyze the stored user data. The input is the user data stored in the database, and the output is the launch of the analysis module. Specifically, the analysis module is initialized and the data is loaded.

[1051] Step 4: Analyzing the text data

[1052] The server uses GPT-4 to analyze user message data and extract keywords, emotions, and interests. The input is the user message data, and the output is the extracted keywords, emotions, and interests. Specific data processing includes tokenization, emotion scoring, and keyword extraction.

[1053] Step 5: Image and video data analysis

[1054] The server uses OpenCV to analyze the user's image and video data and identify scenes and objects. The input is the user's image and video data, and the output is a list of identified scenes and objects. Specifically, processing such as edge detection, object recognition, and scene classification is performed.

[1055] Step 6: Create a prompt

[1056] Based on the analysis results, the server creates a prompt to be passed to the generation AI. The input is the analysis results (keywords, emotions, interests, and a list of scenes and objects), and the output is the prompt text. Specifically, the text is generated by inserting values ​​into the appropriate template.

[1057] Step 7: Auto-generate videos

[1058] The generation AI automatically generates video content based on prompts. The input is the prompt text and the user's image and video data, and the output is an automatically generated video. The generation AI performs editing tasks such as arranging scenes, applying effects, and adding text.

[1059] Step 8: Creating Preview Data

[1060] The server creates preview data from automatically generated video content. The input is the automatically generated video, and the output is the preview data. Specifically, sample clips of the video and thumbnails are generated.

[1061] Step 9: Send preview data

[1062] The server sends the preview data to the terminal. The input is the preview data, and the output is the preview data sent to the terminal. This includes data compression and transmission.

[1063] Step 10: Preview and select

[1064] The user checks the preview data on the device and selects the most suitable video pattern. The input is the preview data displayed on the device, and the output is the user's selection information. Specific actions include rating each video and pressing the selection button.

[1065] Step 11: Submit your selections

[1066] The terminal sends the user's selection information to the server. The input is the user's selection information, and the output is the selection information sent to the server. This involves packaging the selection information and using a transmission protocol.

[1067] Step 12: Post your video

[1068] The server converts the selected video into a posting format for the online platform and posts the video to the specified account. The input is the selected video and posting format information, and the output is the video posted on the online platform. Specifically, this process includes format conversion, calling the platform API, and confirming that posting is complete.

[1069] (Application example 1)

[1070] 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."

[1071] The conventional content creation and posting process is laborious and time-consuming for users. It is also difficult to automatically generate high-quality video content that accurately reflects users' interests. Furthermore, there is a lack of a mechanism for quickly and easily posting the generated content to a platform. The objective of this invention is to solve these problems and provide a system that allows users to easily create and post high-quality video content.

[1072] 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.

[1073] In this invention, the server includes means for acquiring user data, means for analyzing the acquired user data, means for automatically generating multiple video contents based on the analysis results, means for allowing a user to select from the generated video contents, means for providing a preview of the generated video contents, and means for allowing a user to easily post the selected video, thereby enabling a user to quickly and easily generate and post video contents that suit their own interests.

[1074] Below are definitions of key terms based on the patent claims, rewritten to fit the application.

[1075] "User data" is a general term for information such as message data, image data, and video data that is generated or held by a user.

[1076] "Analysis" is the process of extracting useful information from collected user data and determining the user's hobbies and interests.

[1077] "Video content" refers to digital content in video format that is generated using technologies such as generative AI and is viewable by users.

[1078] "Preview" refers to a shortened display or trial viewing that allows you to check the overall picture and atmosphere of the generated video content in advance.

[1079] An "online platform" is an internet service or website where video content is published and users can view and share it.

[1080] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[1081] "Image recognition technology" is a computer vision technology for identifying and analyzing useful information from image data.

[1082] "Generative AI" is an artificial intelligence technology that automatically generates new content based on large amounts of data.

[1083] "Automatic video generation" is the process of generating video content using pre-defined algorithms or AI models based on user data.

[1084] "Posting support" is a function that automatically posts the generated video content to the online platform specified by the user.

[1085] This invention relates to a system that automatically generates multiple patterns of optimal video content based on user data and enables users to easily post the content to an online platform. Specific embodiments for realizing this system are described below.

[1086] Retrieving User Data

[1087] The user first consents to access the online platform. After obtaining consent, the device collects user data, such as message data, image data, and video data. This data is sent to a server and stored in a database.

[1088] Data analysis

[1089] The server launches a generative AI module to analyze the stored user data. Natural language processing and image recognition technologies are used for the analysis. For example, text information contained in message data is analyzed to extract the user's interests. Image and video data are also identified using image recognition algorithms, and this is also used as information about the user's preferences.

[1090] Video content generation

[1091] Based on the analysis results, the server generates multiple patterns of video content that are optimal for the user. The generation AI automatically edits the storyboard using existing image data and video data clips. This generates high-quality video content that is in line with the user's interests.

[1092] Creating and providing preview data

[1093] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[1094] Submission support

[1095] When a user presses the post button for the selected video, the device sends the selection information to the server, which then converts the selected video into the online platform's posting format and posts the video to the specified account, allowing users to easily share high-quality video content on the online platform.

[1096] Hardware and software used

[1097] The system is implemented using the following hardware and software:

[1098] Hardware: Smartphones, smart glasses

[1099] software:

[1100] OpenAI API (natural language processing)

[1101] MoviePy (video editing)

[1102] requests (data acquisition and transmission)

[1103] cv2 (image processing)

[1104] Specific examples

[1105] For example, if a user has a lot of travel-related data, they can input the following prompt sentence into the generative AI model:

[1106] Analyze user travel data and generate a high-quality travel video. Photos to be used include beaches, mountains, cityscapes, etc. The storyboard sequence should be arrival, sightseeing, dining, departure.

[1107] Based on this prompt, the generative AI automatically generates a travel video, which users can then preview and easily post to an online platform.

[1108] This invention enables users to quickly and easily create and post high-quality video content that matches their interests.

[1109] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1110] Step 1:

[1111] The user consents to access to the online platform. After obtaining the user's consent, the device collects user data such as message data, image data, and video data. The collected data is sent to a server and stored in a database. The input is user data, and the output is the user data stored in the database.

[1112] Step 2:

[1113] The server launches a generative AI module to analyze the stored user data. The input is the user data stored in the database, and the output is the analyzed data. Specifically, it uses natural language processing technology to analyze message data and extract user interests. It also uses image recognition technology to identify the content of image data and video data and obtain information about the user's preferences.

[1114] Step 3:

[1115] The server automatically generates multiple patterns of video content based on the analysis results. The input is the analysis results, and the output is multiple generated video content. Based on the storyboard, the generation AI automatically edits existing image data and video data clips to generate high-quality video content that is optimal for the user.

[1116] Step 4:

[1117] The server creates a preview of the generated video content and sends it to the terminal. The input is the generated video content, and the output is the preview data sent to the terminal. Specifically, a shortened version of the video for preview is generated and sent to the user's terminal.

[1118] Step 5:

[1119] The user checks the preview data and selects the most suitable pattern. The input is the preview data sent from the server, and the output is the selected video content. Specifically, the user watches the preview and checks the overall atmosphere, including effects and background music.

[1120] Step 6:

[1121] When a user presses the post button for the selected video, the device sends the selection information to the server. The input is the selected video content, and the output is the transmission of the selection information to the server. Specifically, clicking the post button sends the selection information to the server.

[1122] Step 7:

[1123] The server converts the selected video into the posting format of the online platform and posts the video to the specified account. The input is the selection information, and the output is the completion of posting to the online platform. The specific operation is to convert the video into the appropriate format and automatically process the posting.

[1124] 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.

[1125] This invention is a system that automatically generates multiple patterns of optimal video content based on user data, enables users to easily post the content to an online platform, and further combines it with an emotion engine that recognizes the user's emotions. Below, an embodiment of the system will be described based on a specific example.

[1126] Retrieving User Data

[1127] The user first consents to access the online platform. After obtaining consent, the device collects data such as chat data, photos, and videos from the user. This data is then sent to a server and stored in a database.

[1128] Data analysis

[1129] The server launches a generative AI module and emotion engine to analyze the stored user data. Natural language processing and image recognition technologies are used for the analysis. For example, text information contained in chat data is analyzed to extract not only the user's interests but also their emotional state. Image recognition algorithms are also used to identify the content of photos and videos, which are then used as information on the user's preferences and emotions.

[1130] Use of emotion engine

[1131] The emotion engine analyzes emotions from the acquired user data. This engine analyzes the user's text and facial expressions in videos to determine the user's emotional state. For example, if the engine detects from the chat data that the user is "having fun," it will use more cheerful content and brighter effects in the video content.

[1132] Video content generation

[1133] Based on the analysis results, the generative AI designs a storyboard of video content that is optimal for the user. Multiple video patterns are automatically generated based on this storyboard. Effects and music based on the results of the emotion engine are also added to the generated videos. For example, if the user is having fun, cheerful music and bright effects are used.

[1134] Creating and providing preview data

[1135] A preview of the generated video content is generated by the server and sent to the device. The user can check these preview data and select the pattern that seems best. When checking the preview, the user can check the overall atmosphere, including the effects and background music used.

[1136] Submission support

[1137] When the user presses the post button for the selected video, the device sends the selection information to the server, which converts the selected video into a posting format for the online platform and posts the video to the specified account.

[1138] Specific examples

[1139] For example, consider the case where User B frequently shares fun topics about their daily life on LINE. User B often posts smiling photos and videos. When User B uses this system, they first authorize access to LINE VOOM and upload photos and videos of their daily life from their device. The server analyzes this data, and the emotion engine detects the emotion of enjoyment. Based on the analysis results, the generation AI generates multiple fun video patterns.

[1140] Next, previews of the generated fun videos are sent to User B's device, and User B can review and select them. Once the selection is complete, the selected video is posted to LINE VOOM. In this way, User B can easily post high-quality, fun videos.

[1141] This system enables the generation of video content that reflects the user's emotions, further enriching the user's digital experience. Furthermore, emotion-based customization enables the provision of more personalized content. In this way, it is possible to support users in actively sharing information on an ongoing basis.

[1142] The processing flow will be explained below.

[1143] Step 1:

[1144] The user consents to access the online platform. The device obtains this consent and sends the authentication information to the server.

[1145] Step 2:

[1146] The device collects chat data, photos, and videos specified by the user and uploads them to the server, which then stores the uploaded data in a database.

[1147] Step 3:

[1148] The server starts the generative AI module and emotion engine, retrieves the saved user data from the database, and begins analysis.

[1149] Step 4:

[1150] The generative AI uses natural language processing technology to analyze the conversation data and extract the user's interests and emotions. For example, it identifies emotional keywords such as "fun" and "stressful" from the conversation data.

[1151] Step 5:

[1152] Generative AI uses image recognition technology to analyze photos and videos, identifying objects and scenes contained in them and simultaneously analyzing emotions from people's facial expressions.

[1153] Step 6:

[1154] The emotion engine analyzes the user's emotional state from chat data and image / video data. For example, if a user has many photos and videos of themselves having fun, it will identify their emotional state as "having fun."

[1155] Step 7:

[1156] Based on the analysis results, the generative AI designs a storyboard for video content that reflects the user's preferences and emotional state. Based on the results of the emotion engine, multiple video patterns are automatically generated, using fun music and bright effects, for example.

[1157] Step 8:

[1158] The server creates a preview of the generated video content and sends it to the device, which displays multiple previews to the user.

[1159] Step 9:

[1160] The user selects the video they like best from the previewed videos and presses the submit button, and the device sends this selection information to the server.

[1161] Step 10:

[1162] The server converts the selected video content into a posting format for the online platform and posts the video to the specified account.

[1163] Step 11:

[1164] The server collects engagement data (e.g., number of likes and comments) after posting, along with user feedback, which helps improve the accuracy of the generation AI.

[1165] Example 2

[1166] 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."

[1167] Conventional systems have the problem that the process for users to create effective video content is complicated, time-consuming, and laborious. In addition, it is difficult to generate personalized content based on emotions, and it has not been possible to automatically generate video content that is directly linked to the user's interests and emotions.

[1168] 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.

[1169] In this invention, the server includes means for acquiring user data, means for transmitting the acquired user data to the server and storing it in a database, means for activating a generation AI module and an emotion engine to analyze the stored user data and extract the user's interests and emotional state using natural language processing technology and image recognition technology, means for designing a storyboard of video content optimal for the user based on the analysis results and automatically generating multiple video patterns, means for transmitting previews of the generated multiple video content to the user's device so that the user can select one, and means for posting the selected video content to an online platform. This enables users to easily and quickly generate and post personalized video content based on emotions.

[1170] "User data" refers to information including text data, image data, and video data provided by a user.

[1171] A "server" is a system of electronic devices and software for receiving, storing, and analyzing user data.

[1172] A "database" is a system for managing and storing user data stored on a server.

[1173] The "generative AI module" is a module that uses artificial intelligence technology to analyze user data and generate optimal video content.

[1174] An "emotion engine" is a system for analyzing a user's emotional state from their text data and image data.

[1175] "Natural language processing technology" is a technology for analyzing a user's text data and understanding its meaning and emotions.

[1176] "Image recognition technology" is a technology that analyzes a user's image data and video data and recognizes their content and emotions.

[1177] A "storyboard" is a blueprint that visually represents the structure and flow of scenes in video content.

[1178] An "online platform" is a website or application for posting and sharing user-generated video content.

[1179] A "preview" is a video that is temporarily displayed to allow the user to check the generated video content.

[1180] This invention is a system that automatically generates multiple patterns of optimal video content based on user data and allows users to easily post them to online platforms. It also combines an emotion engine that recognizes the user's emotions.

[1181] First, a user consents to access the online platform to begin using the system. After obtaining the user's consent, the device collects data such as the user's chat data, photos, and videos. This data is sent to a server and stored in a database. The database is a system for managing and storing user data stored on the server.

[1182] The server then launches a generative AI module and an emotion engine to analyze the stored user data. The generative AI module uses natural language processing technology to analyze the text information in the chat data and extract the user's interests and emotional state. The emotion engine uses image recognition technology to identify the content of photos and videos and determine the user's emotional state.

[1183] For example, if the AI ​​detects that the user is enjoying the content, it will use the analysis results to design a storyboard for the optimal video content for the user and automatically generate multiple video patterns. The generated videos will also include effects and music based on the results of the emotion engine.

[1184] A preview of the generated video content is generated by the server and sent to the terminal. The user can check this preview data and select the pattern that seems most suitable. When the user selects the selected video content, the selection information is sent from the terminal to the server. The server converts the selected video into a posting format for the online platform and posts the video to the specified account.

[1185] As a concrete example, consider the case where User B frequently shares fun topics about daily life on a communication app. User B posts many smiling photos and videos during these sessions. When User B uses this system, he or she first grants permission to access the video sharing function on the communication app and uploads photos and videos of daily life from his or her device. The server analyzes this data, and the emotion engine detects the emotion of enjoyment. Based on the analysis results, the generation AI generates multiple fun video patterns.

[1186] Next, previews of the generated fun videos are sent to the device, and User B can review and select them. Once selection is complete, the selected video is posted to the video sharing function of the communication app. In this way, User B can easily post high-quality, fun videos.

[1187] An example of a prompt is, "Based on the following talk data and photos, please generate video content that you believe the user is enjoying."

[1188] This system enables the generation of video content that reflects the user's emotions, further enriching the user's digital experience. Furthermore, emotion-based customization enables the provision of more personalized content. In this way, it is possible to support users in actively sharing information on an ongoing basis.

[1189] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1190] Step 1: Get User Data

[1191] To start using the system, the user consents to access the online platform. After confirming this consent, the device collects data such as the user's chat data, photos, and videos. The input data is text data, image data, and video data provided by the user, and the output is data sent from the device to the server. The device then sends the acquired data to the server.

[1192] Step 2: Save your data

[1193] The server receives the user data sent from the terminal.The server then stores the received data in a database.The input is the user data received from the terminal, and the output is the data stored in the database.

[1194] Step 3: Analyze the data

[1195] The server launches a generative AI module and an emotion engine to analyze user data stored in the database. The generative AI module uses natural language processing technology to analyze text information in the chat data and extract the user's interests and emotional state. The emotion engine uses image recognition technology to identify the content of photos and videos and determine the user's emotional state. The input is the user data stored in the database, and the output is the analysis results. Based on this, the server identifies the user's interests and emotional state.

[1196] Step 4: Sentiment Analysis

[1197] The emotion engine on the server analyzes the user's emotional state from the acquired user data. For example, the emotion "enjoying" may be detected from chat data. The input is the analyzed user data, and the output is the result of the judgment of the emotional state. The emotion engine identifies emotions from the user's text and facial expressions in images.

[1198] Step 5: Generate video content

[1199] The server's generation AI designs a storyboard of video content that is optimal for the user based on the analysis results of the emotion engine. It then automatically generates multiple video patterns based on this storyboard. Effects and music based on the results of the emotion engine are added to the generated videos. The input is the analysis results of the emotion engine, and the output is multiple video contents.

[1200] Step 6: Creating and providing preview data

[1201] The server creates preview data of the generated video content and sends it to the terminal. The input is the generated video content and the output is the preview data. The terminal presents the preview data to the user, allowing the user to check and select it.

[1202] Step 7: Select and post your video

[1203] The user selects the optimal video pattern and presses the post button. The device sends this selection information to the server. The server converts the selected video into the posting format of the online platform and posts the video to the specified account. The input is the user's video selection information, and the output is the video posted to the online platform.

[1204] In this way, users can easily create and post personalized emotion-based video content.

[1205] (Application example 2)

[1206] 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."

[1207] Conventional video content generation systems recommend and generate content without considering the user's emotional state, making it difficult to provide appropriate content that reflects the user's real-time emotional state. This results in a low-quality viewing experience and insufficient personalized experience. It also makes it difficult for users to efficiently find the content they desire, resulting in low satisfaction with content viewing.

[1208] 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.

[1209] In this invention, the server includes means for acquiring user data and real-time emotional data, means for analyzing the acquired user data and real-time emotional data, and means for automatically generating multiple video content items based on the analysis results, thereby enabling the provision of personalized content that reflects the user's real-time emotional state.

[1210] "User data" refers to information generated by a user, including message data, image data, and video data.

[1211] "Real-time emotional data" refers to data that represents the user's current emotional state, and is information that is primarily obtained from the user's facial expressions using image recognition technology.

[1212] "Natural language processing technology" is a technology for analyzing text data and understanding and extracting its content and meaning.

[1213] "Image recognition technology" is a technology that extracts and analyzes specific information from image data, and is particularly used for analyzing facial expressions.

[1214] "Emotion analysis technology" is a technology that identifies and analyzes a user's emotional state from text and image data.

[1215] "Personalized content" refers to content that is optimized based on the preferences and feelings of each individual user.

[1216] An "online platform" is a service or system available via the Internet that allows for the posting and sharing of videos.

[1217] The detailed description of the preferred embodiment of the present invention will be given using appropriate hardware and software.

[1218] System Configuration

[1219] This system acquires and analyzes user data and real-time emotional data, and generates and provides video content based on the analysis results. Specifically, it includes the following elements:

[1220] 1. User devices: Devices such as smart TVs and head-mounted displays (HMDs). These devices have built-in cameras and microphones to capture real-time emotional data from users.

[1221] 2. Server: Located in the cloud, it analyzes user data and emotional data using a generative AI model that combines natural language processing and image recognition technologies.

[1222] 3. Data transmission and reception means: A network configuration that transmits user data and emotion data to the server and sends the analysis results to the terminal.

[1223] System Operation

[1224] First, the user device collects user data, such as viewing history and real-time emotional data. The emotional data is acquired using a facial recognition camera, for example. With the user's consent, this data is sent to the server.

[1225] After receiving the data, the server analyzes the text data using natural language processing technology, while also analyzing real-time facial expression data using image recognition and emotion analysis technology. As a result of the analysis, the user's interests and emotional state are identified.

[1226] Next, the generative AI model automatically generates multiple video content pieces that are optimal for the user based on the analysis results. During this generation process, effects and music are also selected and applied according to the user's emotions. For example, if the user is having fun, cheerful music and bright effects are used.

[1227] The generated video content is sent to the user's device as a series of previews, where the user can review and select the video they consider most appropriate. Once selected, the selected video is posted to an online platform, enabling users to efficiently post personalized content based on their emotions.

[1228] Specific Examples

[1229] For example, consider a case where a user accesses the system through a smart TV and provides their viewing history and real-time emotional data. If the user smiles while watching a video, comedy movies or variety shows will be recommended based on the emotional data. The server generates appropriate content based on the analysis results and provides it to the user as a preview.

[1230] In this case, example prompts for the generative AI model might include, "If the user is having fun, what type of video would you recommend?" or "If you determine that the user is sad, what type of content would you recommend?"

[1231] In this way, our system provides personalized video recommendations that reflect the user's real-time emotions, enriching the user's viewing experience.

[1232] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1233] Step 1:

[1234] The user accesses the system via a smart TV or a head-mounted display (HMD).

[1235] Input: A request to access the system.

[1236] Output: Display of user consent screen.

[1237] How it works: The user's device displays a screen asking for consent to data collection and use of the facial recognition camera. If the user gives consent, the device proceeds to the next step.

[1238] Step 2:

[1239] The user terminal acquires user data and real-time emotion data.

[1240] Input: User viewing history, message data, and facial expression data.

[1241] Output: Collected user and sentiment data.

[1242] Operation: Using the built-in camera on the user's device, the system captures the user's facial expression data in real time, as well as collecting viewing history and message data.

[1243] Step 3:

[1244] The collected data is sent to a server.

[1245] Input: User data and real-time sentiment data.

[1246] Output: Sending data to the server.

[1247] Operation: The user device sends the acquired data to the server via the Internet.

[1248] Step 4:

[1249] The server analyzes the received data.

[1250] Input: User data and real-time emotion data sent to the server.

[1251] Output: Analysis results (user interests and emotional state).

[1252] How it works: The server uses natural language processing technology to analyze the user's viewing history and message data to extract the user's interests and concerns. At the same time, it uses image recognition and emotion analysis technology to analyze facial expression data and identify the user's emotional state.

[1253] Step 5:

[1254] Based on the analysis results, the server automatically generates multiple video contents using a generative AI model.

[1255] Input: Analysis results.

[1256] Output: Auto-generated video content.

[1257] How it works: The server inputs prompts into the generative AI model, which generates multiple optimal video content based on the user's emotions. For example, it uses prompts such as, "If the user is enjoying themselves, what kind of videos would you recommend?"

[1258] Step 6:

[1259] A preview of the generated video content is transmitted to the user terminal.

[1260] Input: Auto-generated video content.

[1261] Output: Video preview sent to user device.

[1262] Operation: The server sends the generated video content in a preview format to the user's device, which displays it for the user to review.

[1263] Step 7:

[1264] The user selects the video that seems most suitable.

[1265] Input: Video preview.

[1266] Output: The selected video content.

[1267] How it works: The user device accepts a selection from multiple previews and sends information about the video content selected by the user to the server.

[1268] Step 8:

[1269] The server posts the selected videos to an online platform.

[1270] Input: Selected video content information.

[1271] Output: Video posted on an online platform.

[1272] How it works: The server converts the selected video content into a posting format for the online platform and posts it to the specified account.

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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.

[1279] 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).

[1280] 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.

[1281] 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."

[1282] 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.

[1283] 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).

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] 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.

[1290] 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.

[1291] 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.

[1292] 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.

[1293] 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.

[1294] The following is further disclosed regarding the above embodiment.

[1295] (Claim 1)

[1296] a means for obtaining user data;

[1297] means for analyzing the acquired user data;

[1298] A means for automatically generating multiple video contents based on the analysis results;

[1299] A means for allowing a user to select from the plurality of generated video contents;

[1300] means for posting the selected video content to an online platform;

[1301] A system including:

[1302] (Claim 2)

[1303] 10. The system of claim 1, wherein the user data includes message data, image data, and video data.

[1304] (Claim 3)

[1305] The system of claim 1, wherein the analysis uses natural language processing technology and image recognition technology.

[1306] "Example 1"

[1307] (Claim 1)

[1308] a means for obtaining user data;

[1309] a means for transmitting the acquired user data to a server using a security protocol and storing the data in a database;

[1310] means for invoking a generative AI module to analyze the stored user data;

[1311] The analysis uses natural language processing and image recognition technologies to extract keywords, emotions, and interests from text information.

[1312] A method to create prompts based on the analysis results and automatically generate multiple video contents using generative AI.

[1313] means for creating preview data of the generated plurality of video contents and transmitting the preview data to the terminal;

[1314] A means for users to view and select preview data on their devices;

[1315] means for converting the selected video content into a posting format for an online platform and posting the video to a designated account;

[1316] A system including:

[1317] (Claim 2)

[1318] 2. The system of claim 1, wherein the user data includes text data, image data, and video data.

[1319] (Claim 3)

[1320] 2. The system of claim 1, wherein natural language processing technology is used to analyze text data and image recognition technology is used to analyze image and video data.

[1321] "Application Example 1"

[1322] New Claims

[1323] (Claim 1)

[1324] a means for obtaining user data;

[1325] means for analyzing the acquired user data;

[1326] A means for automatically generating multiple video contents based on the analysis results;

[1327] A means for allowing a user to select from the plurality of generated video contents;

[1328] means for posting the selected video content to an online platform;

[1329] means for providing a preview of the generated video content;

[1330] A means for users to easily post videos of their choice,

[1331] A system including:

[1332] (Claim 2)

[1333] 10. The system of claim 1, wherein the user data includes message data, image data, and video data.

[1334] (Claim 3)

[1335] The system of claim 1, wherein the analysis uses natural language processing technology and image recognition technology.

[1336] "Example 2: Combining Emotion Engines"

[1337] (Claim 1)

[1338] a means for obtaining user data;

[1339] A means for transmitting the acquired user data to a server and storing it in a database;

[1340] means for activating a generative AI module and an emotion engine to analyze the stored user data and extract the user's interests and emotional state using natural language processing technology and image recognition technology;

[1341] A means for designing a storyboard of video content that is optimal for the user based on the analysis results and automatically generating multiple video patterns;

[1342] A means for transmitting previews of the generated video content to a user's terminal, allowing the user to select one of the previews;

[1343] means for posting the selected video content to an online platform;

[1344] A system including:

[1345] (Claim 2)

[1346] 2. The system of claim 1, wherein the user data includes text data, image data, and video data.

[1347] (Claim 3)

[1348] 2. The system according to claim 1, wherein the analysis uses natural language processing technology and image recognition technology, and an emotion engine is used to analyze the user's emotional state.

[1349] "Application example 2 when combining emotion engines"

[1350] (Claim 1)

[1351] a means for obtaining user data;

[1352] means for analyzing the acquired user data and real-time emotion data;

[1353] A means for automatically generating multiple video contents based on the analysis results;

[1354] A means for allowing a user to select from the plurality of generated video contents;

[1355] means for posting the selected video content to an online platform;

[1356] A system including:

[1357] (Claim 2)

[1358] 2. The system of claim 1, wherein the user data includes message data, image data, and video data, and the emotion data includes real-time facial expression data obtained using image recognition technology.

[1359] (Claim 3)

[1360] The system of claim 1, wherein the analysis uses natural language processing technology and image recognition technology, and further combines it with sentiment analysis technology to identify the user's emotional state. [Explanation of symbols]

[1361] 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 obtaining user data; means for analyzing the acquired user data; A means for automatically generating multiple video contents based on the analysis results; A means for allowing a user to select from the plurality of generated video contents; means for posting the selected video content to an online platform; A system including:

2. 2. The system of claim 1, wherein the user data includes message data, image data, and video data.

3. The system according to claim 1, wherein the analysis uses natural language processing technology and image recognition technology.

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