system

Abstract

We provide the system. [Solution] A means for uploading media data captured by the user using a device to a server, A means for analyzing media data received by a server using image recognition to identify people and composition, A method for selecting the optimal media data to use for video based on the analysis results, A method for automatically performing video editing on the server based on video generation parameters entered by the user, A system that includes means for saving edited videos to a server and providing users with access links to the videos.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, it is common to record events and important moments of daily life in photos and videos. However, the task of editing a large amount of media data taken by multiple users into a single high-quality video is time-consuming and labor-intensive, and technical knowledge is also required. Therefore, there is a need for a system that can easily integrate media data from multiple photographers and generate attractive videos even for non-expert users.

Means for Solving the Problems

[0005] The present invention provides a system that includes means for uploading media data captured by a user using a terminal to a server, means for the server to analyze the received media data using image recognition to identify people and composition, means for selecting the optimal media data to be used for video based on the analysis results, means for automatically editing the video on the server based on video generation parameters entered by the user, and means for saving the edited video on the server and providing the user with an access link to the video. This allows users to easily generate and share integrated, high-quality videos, significantly reducing the effort and time required.

[0006] A "terminal" is a device used by a user to take photos and videos and upload the data to a server.

[0007] A "server" is a central computer system that stores media data received from users and performs analysis and video editing.

[0008] "Media data" refers to the data of photos and videos taken by the user.

[0009] "Image recognition" is the process by which AI on a server analyzes media data to identify people and compositions.

[0010] "Video generation parameters" are settings that users enter to specify details such as the length of the video, music, and theme.

[0011] The method for automating "video editing" involves a process where AI on a server selects media data and then adds transitions and effects to generate the video.

[0012] An "access link" is a URL link that allows a user to view and download a video generated by a server. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video. Specific embodiments of this system are described below.

[0035] System Overview

[0036] 1. Shooting and uploading media data

[0037] Users use a dedicated device (e.g., a smartphone) to take photos and videos of events and everyday scenes.

[0038] The captured media data is uploaded to the server via a dedicated application.

[0039] 2. Data reception and analysis

[0040] The server receives and stores media data sent by the user.

[0041] The image recognition AI on the server analyzes the received media data to identify people and composition.

[0042] 3. Media Selection

[0043] The server's image recognition AI selects the most suitable photos and video clips based on the identification results.

[0044] The selected media data will be used in the subsequent video editing process.

[0045] 4. Parameter Input

[0046] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, and more.

[0047] The parameters entered by the user are sent to the server, which then acts as a guide for the video editing AI.

[0048] 5. Automatic video editing using AI

[0049] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data and user input parameters.

[0050] Video editing AI enables seamless transitions between videos and synchronizes music and visuals.

[0051] 6. Saving and sharing the generated video

[0052] The edited video is saved on the server, and an access link is provided to the user.

[0053] Users can use this access link to view and download the completed video.

[0054] Furthermore, users can easily share videos via social media or email.

[0055] Specific example

[0056] For example, User A attends a live music event and takes multiple photos and videos using a dedicated application. User A uploads this data to the server and then sets the video theme to "energetic atmosphere" and the video length to "5 minutes." This parameter input is done through the dedicated application's interface.

[0057] The server receives data from user A, and the image recognition AI identifies people (artists or audience members) and important scenes. From the identified media data, the optimal clips are selected, and the video editing AI automatically generates a 5-minute video using these clips. The video editing AI adds transition effects and specified energetic background music to complete the video.

[0058] The generated video is saved on the server, and a URL link to it is provided to User A. Through this link, User A can view and download a high-quality video that serves as a record of the event. Furthermore, User A can easily share this video with friends and fans via social media or email.

[0059] Thus, the system according to the present invention can efficiently and automatically analyze and edit vast amounts of media data captured by multiple users, and easily generate and share high-quality videos.

[0060] The following describes the processing flow.

[0061] Step 1:

[0062] User: Taking and uploading photos and videos

[0063] Users launch a dedicated app and take photos and videos of events and scenes.

[0064] Tapping the shutter button activates the device's camera function, and the captured photos and videos are temporarily saved to the device's memory.

[0065] The user taps the upload button within the app, selects the captured media data, and sends it to the server.

[0066] Step 2:

[0067] Server: Data reception and storage

[0068] The server receives media data sent by the user.

[0069] The received media data is stored in the server's storage system, and metadata (e.g., date and time of shooting, user ID, etc.) is added to each data item.

[0070] Step 3:

[0071] Server: Image recognition and analysis

[0072] The image recognition AI on the server begins analyzing the stored media data.

[0073] Image recognition AI performs face recognition, object recognition, and scene classification to identify people and important scenes.

[0074] The identification results are linked to each media data as metadata, preparing the system to select the most suitable media data.

[0075] Step 4:

[0076] User: Input of video generation parameters

[0077] Users access a parameter input screen for video generation within a dedicated app.

[0078] The user enters the required parameters (e.g., video length, theme, music) and taps the submit button.

[0079] The entered parameters will be sent to the server.

[0080] Step 5:

[0081] Server: Media selection and AI video editing startup

[0082] The server selects the optimal media data based on media data identified by image recognition AI and parameters specified by the user.

[0083] Based on the selected media data, the video editing AI is activated.

[0084] The video editing AI adds transitions and effects to selected photos and video clips to perform the editing.

[0085] Step 6:

[0086] Server: Adding music and effects

[0087] The video editing AI adds background music specified by the user to the video.

[0088] Insert text (e.g., event name, date) as needed, and add effects to generate the final video.

[0089] Step 7:

[0090] Server: Video storage and link generation

[0091] The edited video is then given its final processing, encoded, and saved.

[0092] Generate an access link to the saved video and provide that link to the user.

[0093] Step 8:

[0094] User: Receiving and sharing the completed video

[0095] Users view and download the completed video via a link provided by the server.

[0096] Users can share their completed videos with others via social media or email using the sharing function within the dedicated app.

[0097] Through the above processing steps, users can easily generate and share high-quality videos.

[0098] (Example 1)

[0099] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] In conventional systems, efficiently organizing large amounts of media data shot by users and compiling them into compelling videos was an extremely time-consuming and laborious process. Furthermore, for average users without specialized video editing knowledge, the process itself was a significant hurdle. Additionally, selecting the optimal clips from multiple media sources and synchronizing them with transition effects and background music was not easy. As a result, it was difficult for users to create and share high-quality videos.

[0101] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0102] In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the received media data by image recognition to identify people and composition, means for selecting the optimal media data to be used for the video based on the analysis results, means for automatically editing the video on the server based on video generation parameters entered by the user, means for automatically adding transition effects and other effects, means for seamlessly synchronizing the video and music, and means for saving the edited video to the server and providing the user with an access link to the video. As a result, users can easily create and share efficiently and high-quality edited videos without having specialized knowledge.

[0103] "Media data" refers to data in the form of photographs and videos.

[0104] "Terminal" refers to information processing devices used by users, such as smartphones, tablets, and computers.

[0105] A "server" refers to a computer system that receives and processes data sent by users from their terminals.

[0106] "Image recognition" refers to the technology used to identify people and compositions within media data.

[0107] "Analysis" refers to the process of examining data in detail and extracting specific information.

[0108] "Video generation parameters" refer to setting information that users input as instructions for video editing, such as video length, theme, and background music.

[0109] "Automatic editing" refers to a process where software performs editing operations on a video without user intervention.

[0110] "Transition effects" refer to visual effects used to make the transition between video clips smoother.

[0111] "Effects" refer to visual or auditory effects added to a video.

[0112] "Seamless" refers to a state that is uninterrupted and smooth.

[0113] "Synchronization" refers to the synchronization of video and music.

[0114] An "access link" refers to the URL that allows users to access the generated video.

[0115] Modes for carrying out the invention

[0116] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video. This system is implemented according to the following specific procedure.

[0117] System Overview

[0118] 1. Shooting and uploading media data

[0119] Users use devices such as smartphones and tablets to take photos and videos of events and everyday scenes. The captured media data is uploaded to a server via a dedicated application.

[0120] 2. Data reception and analysis

[0121] The server receives media data sent by the user and stores it in storage. The received media data is analyzed by an image recognition AI using the Google® Cloud Vision API. The analysis is performed to identify people and compositions, and the results are stored in the server's database.

[0122] 3. Media Selection

[0123] The server's image recognition AI selects the most suitable photos and video clips based on the analysis results. The selected media data is then used in the subsequent video editing process.

[0124] 4. Parameter Input

[0125] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, etc. The entered parameters are sent from the terminal to the server.

[0126] 5. Automatic video editing using AI

[0127] The server's video editing AI starts generating a video using editing tools such as the Adobe Premiere Pro API, based on the selected media data and user input parameters. Automatic editing adds transition effects and other effects, and seamlessly synchronizes the video and music.

[0128] 6. Saving and sharing the generated video

[0129] Once editing is complete, the video is saved to the server, and an access link is generated. This link is provided to the user, who can view and download the video through a dedicated application. It is also possible to easily share the video via social media or email.

[0130] Specific example

[0131] For example, user A attends a live music event and uses a dedicated application to take multiple photos and videos. They upload the captured data to a server, then set the video theme to "energetic atmosphere" and the video length to "5 minutes." These parameter inputs are made through the dedicated application's interface.

[0132] The server receives data from user A, and the image recognition AI uses the Google Cloud Vision API to identify people (artists or audience members) and important scenes. From the identified media data, the Adobe Premiere Pro API is used to select the best clips, and the video editing AI automatically generates a 5-minute video based on these. The video editing AI adds transition effects and specified energetic background music to complete the video.

[0133] The generated video is saved on the server, and a URL link to it is provided to User A. User A can view and download the high-quality video through this link. Furthermore, User A can easily share this video with friends and fans via social media or email.

[0134] This system allows users to easily create and share high-quality, efficiently edited videos, even without specialized knowledge.

[0135] Example of a prompt

[0136] "Use photos and videos taken by the user to create a 5-minute video with the theme 'Energetic Atmosphere.' Add transition effects and use the specified background music to ensure seamless video transitions and synchronization."

[0137] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0138] Step 1:

[0139] The user selects media data captured using their device and launches a dedicated application. The user then presses the "Upload" button to send the selected media data to the server.

[0140] Input: Photo and video data (device)

[0141] Output: Uploaded media data (server)

[0142] Specific operation: The user takes photos and videos of the event with their smartphone and selects the saved media data from the dedicated application. Then, they press the "Upload" button to start sending the data.

[0143] Step 2:

[0144] The server receives media data sent by the user and saves it to a specific directory. The server confirms that the upload is complete and notifies the user.

[0145] Input: Media data from the user (server)

[0146] Output: Saved media data (server)

[0147] Specific operation: The server receives the uploaded media data and saves it to the specified directory. Once the upload is complete, a "Upload complete" notification is sent to the user.

[0148] Step 3:

[0149] The server passes the stored media data to an image recognition AI (Google Cloud Vision API) for analysis. The image recognition AI identifies people and compositions, and saves the results to the server's database.

[0150] Input: Stored media data (server)

[0151] Output: Analysis results (server)

[0152] Specific operation: The server passes the received photos and videos to an image recognition AI. The Google Cloud Vision API analyzes the media data and identifies people and composition. The analysis results are stored in the server's database.

[0153] Step 4:

[0154] The server selects the optimal photos and video clips based on the analysis results of the image recognition AI. The selected media data is then provided to the next video editing stage.

[0155] Input: Analysis results (server)

[0156] Output: Selected media data (server)

[0157] Specific operation: Based on the analysis results, the server selects important scenes and high-quality photos from the event. The selected media data is then moved to a directory for video editing.

[0158] Step 5:

[0159] The user uses a dedicated application to input parameters for video generation. These parameters include the video length, theme, and background music. The entered parameters are sent to the server.

[0160] Input: Video generation parameters (device)

[0161] Output: Input parameters (server)

[0162] Specific operation: The user opens the application and sets the video generation options. For example, they might select "Video length: 5 minutes," "Theme: Energetic," and "Background music: Pop." After finishing the settings, they press the "Submit" button.

[0163] Step 6:

[0164] The server passes the selected media data and user input parameters to the video editing AI, and the automatic video generation begins.

[0165] Input: Selected media data, video generation parameters (server)

[0166] Output: Generated video (server)

[0167] Specific operation: The server uses the Adobe Premiere Pro API to edit the selected media data. It adds transition effects and other effects, and seamlessly synchronizes the video and music.

[0168] Step 7:

[0169] The server saves the generated video and creates an access link. This link is then provided to the user.

[0170] Input: Generated video (server)

[0171] Output: Access link (server, user)

[0172] Specific operation: After editing is complete, the video is saved to a directory on the server for long-term storage. The server generates an access link to the video and notifies the user.

[0173] Step 8:

[0174] Users can view and download the generated videos using the provided access link. They can also share the videos via social media or email.

[0175] Input: Access link (user)

[0176] Output: View, Download, Share (User)

[0177] Specific operation: The user clicks the notified link and watches a high-quality video. They can also download and save the video and share it on social media or via email as needed.

[0178] (Application Example 1)

[0179] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0180] Conventional video generation systems can analyze and edit multiple media data files shot by users to automatically generate integrated videos, but they lack the functionality to generate personalized advertising videos based on the individual user's interests and preferences. This results in limitations on the effectiveness and appeal of the advertisements.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0182] In this invention, the server includes means for uploading media data captured by the user using an information processing device to the server; means for analyzing the received media data by image recognition to identify people and composition; means for selecting the optimal media data to be used for the video based on the analysis results; means for a video editing AI to generate an advertising video using background music and effects entered by the user; and means for sharing the generated advertising video through the user's social networking service or communication means. This makes it possible to automatically generate and share personalized advertising videos tailored to the user's interests and preferences.

[0183] An "information processing device" is a device that has the ability to process media data captured by a user and upload it to a server.

[0184] "Media data" refers to digital files containing image information captured by the user, such as photos and videos.

[0185] A "server" is a centralized computer system that receives, analyzes, and stores media data uploaded by users.

[0186] "Image recognition" is a technology in which algorithms on a server analyze and identify people and compositions contained in media data.

[0187] "Optimal media data" refers to data of suitable quality and content that the server has selected for use in video based on its analysis results.

[0188] "Video generation parameters" refer to the instructions and settings necessary for video generation, such as the desired video length, theme, and background music.

[0189] "Video editing AI" refers to artificial intelligence that automatically edits and generates videos based on selected media data and parameters entered by the user.

[0190] "Background music" refers to music that plays in the background while a video is being generated.

[0191] A "transition" is a scene-switching effect used in video editing, and it is a technique used to make the visual flow of the resulting video smoother.

[0192] A "social networking service" is a platform that allows users to share information and content with other users online.

[0193] "Communication methods" refer to the media and systems used by users to share videos they have created with other users online.

[0194] An "advertising video" is video content intended to promote a product or introduce a service, and is generated to suit the interests and preferences of a specific user.

[0195] A detailed description of the system that implements this application is provided below.

[0196] System Overview

[0197] This system allows users to upload media data captured using information processing devices (e.g., smart glasses or smartphones) to a server, which then analyzes the data to automatically generate and share personalized advertising videos.

[0198] Hardware and software to be used

[0199] Hardware: Information processing devices (smart glasses, smartphones), servers

[0200] Software: Image recognition AI, video editing AI, server management software, upload and sharing platform

[0201] Data processing

[0202] 1. Shooting and uploading media data

[0203] Users use smart glasses or smartphones to photograph everyday scenes and products. The captured media data is uploaded to a server via a dedicated application.

[0204] 2. Data reception and analysis

[0205] The server receives media data sent by the user and performs analysis using image recognition AI. In this analysis process, it identifies people and compositions and identifies products and brands that the user might be interested in.

[0206] 3. Media Selection

[0207] The server's image recognition AI selects the optimal media data to use for the video based on the analysis results. The selected media data is then used in the subsequent video editing process.

[0208] 4. Parameter Input

[0209] The user inputs video generation parameters such as background music and effects through an information processing device. These parameters are sent to a server and used as instructions for the video editing AI.

[0210] 5. Automatic video editing using AI

[0211] The server's video editing AI automatically generates advertising videos with transition effects and visual effects based on selected media data and background music and effect parameters entered by the user.

[0212] 6. Saving and sharing the generated ad videos

[0213] The edited ad video is saved on the server, and an access link is provided to the user. The user can use this link to view and download the generated ad video. Users can also easily share the ad video via social media or email.

[0214] Specific example

[0215] Users film themselves ordering coffee at their favorite cafe using smart glasses. Once the data is uploaded, AI recognizes the cafe's brand and automatically generates a cafe-related advertising video. The generated video includes specified background music and effects.

[0216] Example of a prompt

[0217] "Please film a video of a user at home wearing smart glasses, visiting a coffee shop, and ordering coffee, then upload the video. The server will analyze the video and generate an ad video relevant to the brand."

[0218] In this way, personalized advertising videos based on user behavior can be automatically generated and easily shared. This improves the effectiveness and appeal of the ads, and enhances the user experience.

[0219] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0220] Step 1:

[0221] The user captures media data (photos and videos) using an information processing device (smart glasses or smartphone). The input is the media data captured by the user. The output is this media data uploaded to a server via a dedicated application.

[0222] Step 2:

[0223] The server receives media data uploaded by users. The server receives the user-uploaded media data as input. It stores this data and performs analysis using image recognition AI. The output is analysis data, which includes information about identified individuals and composition.

[0224] Step 3:

[0225] The server's image recognition AI selects the optimal media data to use for the video based on the analyzed data. The input is the analysis results of the image recognition AI. Based on these results, the optimal media data is selected. The output is the selected media data.

[0226] Step 4:

[0227] The user inputs video generation parameters (background music, effects, video length, etc.) using an information processing device. The user's specified video generation parameters are returned as input. These parameters are then sent to the server as output.

[0228] Step 5:

[0229] The server's video editing AI automatically edits videos based on selected media data and video generation parameters entered by the user. The input consists of selected media data and video generation parameters. The server adds transition effects and visual effects to the video and applies background music. The output is the generated advertisement video.

[0230] Step 6:

[0231] The server stores the generated ad video and provides the user with an access link. The input is the generated ad video. The output is that the user can view and download the ad video via this link. A link is also provided so that the user can share the video via social media or email.

[0232] The system is configured so that specific data processing and calculations are performed at each step, ultimately resulting in the automatic generation and delivery of personalized, high-quality advertising videos to the user.

[0233] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0234] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video using an emotion engine. Specific embodiments of this system are described below.

[0235] System Overview

[0236] 1. Shooting and uploading media data

[0237] Users use a dedicated device (e.g., a smartphone) to take photos and videos of events and everyday scenes.

[0238] The captured media data is uploaded to the server via a dedicated application.

[0239] 2. Data reception and analysis

[0240] The server receives and stores media data sent by the user.

[0241] The image recognition AI on the server analyzes the received media data to identify people and composition.

[0242] 3. Emotional analysis using an emotion engine

[0243] The emotion engine analyzes the user's emotions from the audio and images in the uploaded media data.

[0244] Emotion analysis includes voice tone detection and facial expression recognition, and this data is linked to media data as emotion tags.

[0245] 4. Media Selection

[0246] Based on the emotion analysis results from the server's image recognition AI and emotion engine, the optimal photos and video clips are selected.

[0247] The selected media data will be used in the subsequent video editing process.

[0248] 5. Parameter Input

[0249] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, and more.

[0250] The emotion engine optimizes the video generation parameters entered by the user through emotion analysis and makes suggestions.

[0251] The input and optimized parameters are sent to the server, which acts as a guide for the video editing AI.

[0252] 6. Automatic video editing using AI

[0253] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data, user input, and optimized parameters.

[0254] Video editing AI enables seamless transitions between videos and synchronizes music and visuals.

[0255] 7. Saving and sharing the generated video

[0256] The edited video is saved on the server, and an access link is provided to the user.

[0257] Users can use this access link to view and download the completed video.

[0258] Furthermore, users can easily share videos via social media or email.

[0259] Specific example

[0260] For example, User A takes photos and videos of a family trip and uploads them to the server using a dedicated application. After uploading this data to the server, User A sets the video theme to "Fun Family Trip" and the video length to "5 minutes". These parameter inputs are made through the dedicated application's interface.

[0261] The server receives data from user A, and the image recognition AI identifies people (family members) and important scenes. Simultaneously, the emotion engine analyzes user A's emotions from their voice tone and facial expressions. Based on the emotion analysis results, the most suitable media data is selected, and the video editing AI automatically generates a 5-minute video using these. The video editing AI adds transition effects and background music that matches the theme, and saves the completed video.

[0262] The generated video is saved on the server, and a URL link to it is provided to User A. Through this link, User A can view and download a high-quality video documenting their family trip. Furthermore, User A can easily share this video with friends and family via social media or email.

[0263] Thus, the system according to the present invention can efficiently and automatically analyze and edit vast amounts of media data captured by multiple users, and by using an emotion engine, it is possible to easily generate and share customized, high-quality videos.

[0264] The following describes the processing flow.

[0265] Step 1:

[0266] User: Taking and uploading photos and videos

[0267] Users launch a dedicated app and take photos and videos of events and scenes.

[0268] Tapping the shutter button activates the device's camera function, and the captured photos and videos are temporarily saved to the device's memory.

[0269] The user taps the upload button within the app, selects the captured media data, and sends it to the server.

[0270] Step 2:

[0271] Server: Data reception and storage

[0272] The server receives media data sent by the user.

[0273] The received media data is stored in the server's storage system, and metadata (e.g., date and time of shooting, user ID, etc.) is added to each data item.

[0274] Step 3:

[0275] Server: Image recognition and analysis

[0276] The image recognition AI on the server begins analyzing the stored media data.

[0277] The image recognition AI performs face recognition, object recognition, and scene classification to identify people and important scenes.

[0278] The identification results are linked to each media data as metadata and are used for the next processing step by the sentiment analysis engine.

[0279] Step 4:

[0280] Server: Sentiment analysis by the sentiment engine

[0281] The sentiment engine in the server analyzes the audio and images of the uploaded media data and extracts the user's sentiment.

[0282] In audio analysis, tones, pitches, speeds, etc. of the voice are analyzed, and in image analysis, emotion recognition technology is used to identify the emotional state (e.g., joy, surprise, sadness).

[0283] The sentiment analysis results are added to the media data as metadata.

[0284] Step 5:

[0285] Server: Selection of media

[0286] Based on the emotion analysis results by the server's image recognition AI and sentiment engine, the optimal photos and video clips are selected.

[0287] The media selection algorithm considers the identification results and sentiment analysis results and automatically selects the most appropriate scenes.

[0288] Step 6:

[0289] User: Input of video generation parameters

[0290] The user accesses the parameter input screen for video generation using a dedicated application.

[0291] The user enters the required parameters (e.g., video length, theme, music) and taps the submit button.

[0292] The emotion engine analyzes the video generation parameters entered by the user and proposes emotion-based, optimized parameters.

[0293] Once the user confirms the proposed parameters, the final parameters are sent to the server.

[0294] Step 7:

[0295] Server: Starting the video editing AI

[0296] The server's video editing AI starts the editing process based on the selected media data and the final user input parameters.

[0297] The video editing AI edits the video by adding transitions and effects, and inserts text and effects that reflect the results of emotion analysis.

[0298] Step 8:

[0299] Server: Adding music and effects

[0300] The video editing AI adds background music specified by the user to the video.

[0301] Insert text (e.g., event name, date) as needed, and add effects to generate the final video.

[0302] Step 9:

[0303] Server: Final processing and storage of videos

[0304] The edited video is then given its final processing, encoded, and saved.

[0305] Generate an access link for the saved video and provide the link to the user.

[0306] Step 10:

[0307] User: Receiving and sharing the completed video

[0308] The user browses and downloads the completed video through the link provided by the server.

[0309] The user uses the sharing function in the dedicated app to share the completed video with others via SNS or email.

[0310] Through the above processing steps, the user can easily generate and share high-quality videos utilizing sentiment analysis.

[0311] (Example 2)

[0312] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0313] Modern users often record their daily activities and events with photos and videos. However, it takes a great deal of time and effort to select highlight scenes from a vast amount of media data and manually edit videos according to emotions. Also, it is difficult for users who are not proficient in editing techniques to create custom videos. Therefore, there is a demand for a system that can analyze rich media data and automatically generate and share optimal videos based on emotions.

[0314] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the media data received by the server using image recognition to identify people and composition, means for analyzing the user's emotions from the audio and images in the media data based on the analysis results, means for selecting the optimal media data to be used for the video based on the analysis results, means for optimizing the video generation parameters entered by the user, means for automatically editing the video on the server based on the optimized video generation parameters, adding transitions and effects and switching seamlessly, means for saving the edited video on the server and providing the user with an access link to the video, and means for the user to share the video via SNS or email. As a result, the user can automatically generate the optimal video based on emotions from a vast amount of media data and easily share it.

[0315] A "user" is an individual or group that transmits and receives information.

[0316] A "terminal" refers to a computer system or device used by a user to input or display information.

[0317] "Media data" refers to visual and auditory content stored in digital format, such as photographs, videos, and audio files.

[0318] A "server" is a computer system that manages and processes data via a network and responds to user requests.

[0319] "Uploading" refers to transferring data from a device to a server.

[0320] "Image recognition" is the process of automatically identifying objects and features within an image using computer vision technology.

[0321] "Analysis" is the process of examining data in detail and extracting specific results or information.

[0322] A "person" refers to a human being recognized within media data.

[0323] "Composition" refers to the arrangement and balance of visual elements in photographs and videos.

[0324] An "emotion engine" refers to an algorithm or system used to analyze emotions from data such as voice and facial expressions.

[0325] "Emotions" refer to human psychological states and sensations such as joy, sadness, and surprise.

[0326] "Optimization" refers to the process of making adjustments and improvements based on given conditions and constraints in order to obtain the most effective and efficient results.

[0327] "Video generation parameters" refer to settings such as the desired video length, theme, and background music.

[0328] A "transition" is the effect used in video editing to switch from one scene to another.

[0329] "Effects" refer to visual or auditory effects added to videos or images.

[0330] An "access link" is a web address (URL) used to access specific data or services.

[0331] "SNS" is an abbreviation for Social Networking Service, which is an online platform for users to share content and interact with each other.

[0332] "Sharing" refers to exchanging information or data with other users.

[0333] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video using an emotion engine. Specific embodiments of this system are described below.

[0334] Shooting and uploading media data

[0335] Users use smartphones or other devices to take photos and videos of events and everyday scenes. The captured media data is uploaded to a server via a dedicated application. After shooting, users launch the app, select the captured data, and upload it. The server saves the received data to high-speed storage.

[0336] Data reception and analysis

[0337] The server stores the received media data, and an image recognition AI analyzes the data. The image recognition AI analyzes photos and videos, identifying people and important compositions. For example, it identifies the faces of family members or scenery and tags them with metadata.

[0338] Emotional analysis using an emotion engine

[0339] The server's emotion engine analyzes audio and images within media data to detect the user's emotions. This includes voice tone detection and facial expression recognition, and this data is linked to the media data as emotion tags.

[0340] Media Selection

[0341] Based on the analysis results from the server's image recognition AI and emotion engine, the optimal photos and video clips are selected. The selected media data is then used in the subsequent video editing process.

[0342] Parameter Input

[0343] Users input parameters for video generation using a dedicated application. These parameters include video length, theme, and background music. The emotion engine optimizes and suggests video generation parameters entered by the user. The input and optimized parameters are sent to the server and used to instruct the video editing AI.

[0344] Automatic video editing using AI

[0345] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data, user input, and optimized parameters. The video editing AI performs seamless transitions between videos and synchronizes music and video.

[0346] Saving and sharing the generated videos

[0347] Once editing is complete, the video is saved on the server, and an access link is provided to the user. The user can use this access link to view and download the finished video. Furthermore, the user can easily share the video via social media or email.

[0348] Specific example

[0349] For example, User A films a family trip and uploads multiple photos and videos to the server using a dedicated application. After uploading this data to the server, User A sets the video theme to "Fun Family Trip" and the video length to "5 minutes." This parameter input is done through the dedicated application's interface. The server receives the data from User A, and an image recognition AI identifies family members and important scenes. Simultaneously, an emotion engine analyzes User A's emotions from their voice tone and facial expressions. Along with the emotion analysis results, the optimal media data is selected, and a video editing AI automatically generates a 5-minute video using these. The video editing AI adds transition effects and background music that matches the theme, and saves the completed video. The generated video is stored on the server, and a URL link is provided to User A. Through this link, User A can view and download a high-quality video that serves as a record of their family trip. User A can also easily share this video with friends and family via social media or email.

[0350] Example of a prompt

[0351] "To create a fun video of your family trip, please upload the following photos and video clips. Also, please specify the theme and length of your video."

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The device captures and uploads media data.

[0355] Input: Photos and videos taken by the user with their device (e.g., smartphone).

[0356] Specific operation: The user takes photos of events or everyday scenes using their smartphone's camera app. After taking the photos, they launch a dedicated application, select the photos, and click the upload button.

[0357] Output: The selected media data is sent to the server.

[0358] Step 2:

[0359] The server receives and stores the media data.

[0360] Input: Media data uploaded from the device.

[0361] Specific operation: The server receives the uploaded data and saves it to high-speed storage. The saved data is registered in the database along with its metadata.

[0362] Output: Media data stored in the database.

[0363] Step 3:

[0364] The server uses image recognition AI to analyze media data.

[0365] Input: Saved media data.

[0366] Specific operation: An image recognition AI within the server analyzes stored photos and videos to identify people and important compositions.

[0367] Output: Information about identified individuals and compositions is added to the media data as metadata.

[0368] Step 4:

[0369] The server uses an emotion engine to analyze the emotions in media data.

[0370] Input: Analyzed media data.

[0371] Specific operation: The server activates the emotion engine and detects emotions from the tone of voice and facial expressions of people in the video. The detected emotion tags are linked to the media data.

[0372] Output: Media data with emotion tags attached.

[0373] Step 5:

[0374] The server selects the most suitable media based on the results of the image recognition AI and emotion engine.

[0375] Input: Media data tagged with emotion.

[0376] Specific operation: The server uses the analysis results to select the most suitable photos and video clips. Selection criteria include sentiment tags and the importance of the people involved.

[0377] Output: Selected optimal media data.

[0378] Step 6:

[0379] The user enters video generation parameters using a terminal.

[0380] Input: User's video generation parameters (e.g., video length, theme, background music, etc.).

[0381] Specific operation: The user inputs video generation parameters on the interface using a dedicated application.

[0382] Output: Parameters are sent to the server.

[0383] Step 7:

[0384] The server optimizes and suggests parameters based on sentiment analysis, using the parameters entered by the user.

[0385] Input: Video generation parameters entered by the user.

[0386] Specific operation: The server's emotion engine analyzes the user's input parameters and generates optimal suggestions. The suggested parameters are presented to the user through the application interface.

[0387] Output: Optimized video generation parameters.

[0388] Step 8:

[0389] The server uses AI-powered video editing software to automatically perform the editing.

[0390] Input: Optimized video generation parameters and selected media data.

[0391] Specific operation: The server's video editing AI automatically generates videos using selected media data and optimized parameters, adding transitions and effects. It also performs seamless video transitions and synchronizes music and video.

[0392] Output: The completed video file.

[0393] Step 9:

[0394] The server saves the generated video and creates an access link.

[0395] Input: The completed video file.

[0396] Specific operation: The completed video is saved to the server, and an access link to that video is generated. The access link is then notified to the user.

[0397] Output: The access link provided to the user.

[0398] Step 10:

[0399] Users view and share videos using access links.

[0400] Input: Access link.

[0401] Specific actions: Users use the provided access link to view and download the completed video. They can also share the video with other users via social media or email.

[0402] Output: Sharing videos with other users and viewing / downloading videos.

[0403] (Application Example 2)

[0404] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0405] Traditional content distribution services have made it difficult for users to easily and effectively edit videos and photos they have taken, and to generate and share high-quality highlight videos. In particular, the lack of emotion-based, personalized video editing made it difficult to create videos that matched the user's emotions and themes. Furthermore, the manual editing process was cumbersome, time-consuming, and laborious, which negatively impacted the user experience.

[0406] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the received media data by image recognition and identifying people and composition, means for selecting the optimal media data to be used for the video based on the analysis results, means for automatically editing the video on the server based on video generation parameters entered by the user, means for synchronizing the theme and background music with the generated video based on emotion analysis information, and means for saving the edited video on the server and providing the user with an access link to the video. As a result, users can automatically generate and share personalized, high-quality highlight videos based on emotions simply by uploading the media data they have captured.

[0407] "A means for users to upload media data they have captured using their device to a server" refers to a function that allows users to transfer photos and videos taken with their mobile devices to a server via the internet.

[0408] "Means for analyzing media data received by a server using image recognition to identify people and composition" refers to a technology that uses artificial intelligence and algorithms to analyze image information contained in photographs and videos received by a server to identify the subjects, people, and composition.

[0409] "A method for selecting the optimal media data to be used in a video based on analysis results" refers to a process of selecting the most appropriate photos and video clips based on the results of image recognition analysis and using them in the final video.

[0410] "A method for automatically editing videos on the server based on video generation parameters entered by the user" refers to a system in which the server automatically performs editing based on parameters such as the length, theme, and background music of the video specified by the user.

[0411] "A method for synchronizing themes and background music with generated videos based on sentiment analysis information" refers to a function that uses the user's sentiment information analyzed by the sentiment analysis engine to optimize and synchronize the video's theme and background music.

[0412] "A means of saving edited videos to a server and providing users with a link to access the videos" refers to a method of storing completed video files on a server and providing users with a link to access them.

[0413] The embodiments for carrying out the present invention will be described in detail below.

[0414] System Overview

[0415] System configuration:

[0416] The system of this invention begins with a user uploading media data (photos and videos) captured using a mobile device (e.g., a smartphone) to a server. This system consists of the following main components:

[0417] 1. Terminal:

[0418] Users use their mobile devices to photograph events and everyday scenes. The captured media data is uploaded to a server via a dedicated application.

[0419] 2. Server:

[0420] The server stores the received media data and analyzes it using image recognition AI and an emotion engine. Specifically, it analyzes images using OpenCV and analyzes emotions within the media data using DeepFace. Based on the analysis results, the most suitable media data is selected and automatically edited based on the video generation parameters entered by the user.

[0421] Specific example:

[0422] For example, a user films a family trip and uploads multiple photos and videos from their mobile device to the server. The user sets the video theme to "Fun Family Trip" and the video length to "5 minutes." The server uses image recognition AI to identify family members and important scenes, and an emotion engine analyzes emotions from voice tone and facial expressions. Based on these analysis results, the optimal media data is selected, and a video editing AI automatically generates a 5-minute highlight video by adding transition effects and background music that matches the theme.

[0423] Hardware and software

[0424] The following hardware and software will be used to implement this system.

[0425] Hardware:

[0426] Mobile devices (smartphones, etc.): Taking photos and uploading media data.

[0427] High-performance server: Performs media data analysis, storage, and video editing.

[0428] software:

[0429] OpenCV: An image recognition library used to analyze people and compositions.

[0430] DeepFace: An emotion analysis library used to analyze emotions within media data.

[0431] MoviePy is a video editing library used to generate videos based on selected media data.

[0432] Example prompts for a generative AI model:

[0433] "Analyze the following image and video data, select based on emotion, and create a highlight video that fits the theme. The theme is 'Family Trip,' and the video length should be 5 minutes."

[0434] [Video file path 1] [Video file path 2]...

[0435] Thus, the system of the present invention can automatically analyze media data captured by the user, select and edit the optimal media data using an emotion engine, and generate and share high-quality personalized videos.

[0436] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0437] Step 1:

[0438] The user uploads media data (photos and videos) captured using their device to the server. Specifically, the user transfers videos and photos they have taken to the server using a dedicated application. At this time, the server receives and stores the uploaded media data.

[0439] Input: Media data captured with the user's mobile device.

[0440] Output: Media data stored on the server.

[0441] Step 2:

[0442] The server analyzes the received media data using image recognition to identify people and composition. OpenCV is used to analyze the media data and recognize human faces and specific scenes within the image. This allows for identification, for example, of who the person in the photograph is or what the background is.

[0443] Input: Media data stored on the server.

[0444] Output: Information from the analyzed media data (people, composition, etc.).

[0445] Step 3:

[0446] The system uses an emotion engine to analyze emotions within media data. DeepFace is used to detect faces from media data (especially video frames) and determine emotions from facial expressions and voice tone. Based on these results, emotion tags are linked to the media data.

[0447] Input: Information from the analyzed media data (people, composition, etc.).

[0448] Output: Media data with added sentiment analysis information.

[0449] Step 4:

[0450] Based on the analysis results, the optimal media data is selected. Using a selection algorithm based on the sentiment analysis results and user-defined video generation parameters (e.g., theme, video length), the most appropriate media data is chosen. In this process, clips with a high proportion of positive sentiments such as "happy" and "surprise" are prioritized.

[0451] Input: Media data with added sentiment analysis information, and user video generation parameters.

[0452] Output: Selected optimal media data.

[0453] Step 5:

[0454] The server automatically performs video editing. Based on the selected media data and user-entered parameters, MoviePy is used to add transition effects, theme-appropriate background music, text clips, and more, generating a final highlight video.

[0455] Input: Selected optimal media data, user video generation parameters (e.g., theme, background music, video length).

[0456] Output: The completed highlight video.

[0457] Step 6:

[0458] The generated video is synchronized with themes and background music based on emotion analysis information. Based on information obtained from emotion tags, appropriate music and effects are synchronized for each scene in the video. This process results in emotionally rich and personalized videos.

[0459] Input: Completed highlight video, sentiment analysis information.

[0460] Output: A highlight video synchronized based on emotion analysis information.

[0461] Step 7:

[0462] The edited video is saved to the server, and the user is provided with an access link to the video. The completed video is stored as a database on the server, and by providing the user with an access link, the user can use that link to view, download, and share the video.

[0463] Input: A highlight video synchronized based on sentiment analysis information.

[0464] Output: The final video stored on the server and the access link provided to the user.

[0465] These steps allow users to easily create and share high-quality, personalized highlight videos without any hassle.

[0466] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0467] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0468] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0469] [Second Embodiment]

[0470] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0471] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0472] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0473] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0474] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0475] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0476] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0477] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0478] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0479] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0480] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0481] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0482] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video. Specific embodiments of this system are described below.

[0483] System Overview

[0484] 1. Shooting and uploading media data

[0485] Users use a dedicated device (e.g., a smartphone) to take photos and videos of events and everyday scenes.

[0486] The captured media data is uploaded to the server via a dedicated application.

[0487] 2. Data reception and analysis

[0488] The server receives and stores media data sent by the user.

[0489] The image recognition AI on the server analyzes the received media data to identify people and composition.

[0490] 3. Media Selection

[0491] The server's image recognition AI selects the most suitable photos and video clips based on the identification results.

[0492] The selected media data will be used in the subsequent video editing process.

[0493] 4. Parameter Input

[0494] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, and more.

[0495] The parameters entered by the user are sent to the server, which then acts as a guide for the video editing AI.

[0496] 5. Automatic video editing using AI

[0497] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data and user input parameters.

[0498] Video editing AI enables seamless transitions between videos and synchronizes music and visuals.

[0499] 6. Saving and sharing the generated video

[0500] The edited video is saved on the server, and an access link is provided to the user.

[0501] Users can use this access link to view and download the completed video.

[0502] Furthermore, users can easily share videos via social media or email.

[0503] Specific example

[0504] For example, User A attends a live music event and takes multiple photos and videos using a dedicated application. User A uploads this data to the server and then sets the video theme to "energetic atmosphere" and the video length to "5 minutes." This parameter input is done through the dedicated application's interface.

[0505] The server receives data from user A, and the image recognition AI identifies people (artists or audience members) and important scenes. From the identified media data, the optimal clips are selected, and the video editing AI automatically generates a 5-minute video using these clips. The video editing AI adds transition effects and specified energetic background music to complete the video.

[0506] The generated video is saved on the server, and a URL link to it is provided to User A. Through this link, User A can view and download a high-quality video that serves as a record of the event. Furthermore, User A can easily share this video with friends and fans via social media or email.

[0507] Thus, the system according to the present invention can efficiently and automatically analyze and edit vast amounts of media data captured by multiple users, and easily generate and share high-quality videos.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] User: Taking and uploading photos and videos

[0511] Users launch a dedicated app and take photos and videos of events and scenes.

[0512] Tapping the shutter button activates the device's camera function, and the captured photos and videos are temporarily saved to the device's memory.

[0513] The user taps the upload button within the app, selects the captured media data, and sends it to the server.

[0514] Step 2:

[0515] Server: Data reception and storage

[0516] The server receives media data sent by the user.

[0517] The received media data is stored in the server's storage system, and metadata (e.g., date and time of shooting, user ID, etc.) is added to each data item.

[0518] Step 3:

[0519] Server: Image recognition and analysis

[0520] The image recognition AI on the server begins analyzing the stored media data.

[0521] Image recognition AI performs face recognition, object recognition, and scene classification to identify people and important scenes.

[0522] The identification results are linked to each media data as metadata, preparing the system to select the most suitable media data.

[0523] Step 4:

[0524] User: Input of video generation parameters

[0525] Users access a parameter input screen for video generation within a dedicated app.

[0526] The user enters the required parameters (e.g., video length, theme, music) and taps the submit button.

[0527] The entered parameters will be sent to the server.

[0528] Step 5:

[0529] Server: Media selection and AI video editing startup

[0530] The server selects the optimal media data based on media data identified by image recognition AI and parameters specified by the user.

[0531] Based on the selected media data, the video editing AI is activated.

[0532] The video editing AI adds transitions and effects to selected photos and video clips to perform the editing.

[0533] Step 6:

[0534] Server: Adding music and effects

[0535] The video editing AI adds background music specified by the user to the video.

[0536] Insert text (e.g., event name, date) as needed, and add effects to generate the final video.

[0537] Step 7:

[0538] Server: Video storage and link generation

[0539] The edited video is then given its final processing, encoded, and saved.

[0540] Generate an access link to the saved video and provide that link to the user.

[0541] Step 8:

[0542] User: Receiving and sharing the completed video

[0543] Users view and download the completed video via a link provided by the server.

[0544] Users can share their completed videos with others via social media or email using the sharing function within the dedicated app.

[0545] Through the above processing steps, users can easily generate and share high-quality videos.

[0546] (Example 1)

[0547] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0548] In conventional systems, efficiently organizing large amounts of media data shot by users and compiling them into compelling videos was an extremely time-consuming and laborious process. Furthermore, for average users without specialized video editing knowledge, the process itself was a significant hurdle. Additionally, selecting the optimal clips from multiple media sources and synchronizing them with transition effects and background music was not easy. As a result, it was difficult for users to create and share high-quality videos.

[0549] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0550] In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the received media data by image recognition to identify people and composition, means for selecting the optimal media data to be used for the video based on the analysis results, means for automatically editing the video on the server based on video generation parameters entered by the user, means for automatically adding transition effects and other effects, means for seamlessly synchronizing the video and music, and means for saving the edited video to the server and providing the user with an access link to the video. As a result, users can easily create and share efficiently and high-quality edited videos without having specialized knowledge.

[0551] "Media data" refers to data in the form of photographs and videos.

[0552] "Terminal" refers to information processing devices used by users, such as smartphones, tablets, and computers.

[0553] A "server" refers to a computer system that receives and processes data sent by users from their terminals.

[0554] "Image recognition" refers to the technology used to identify people and compositions within media data.

[0555] "Analysis" refers to the process of examining data in detail and extracting specific information.

[0556] "Video generation parameters" refer to setting information that users input as instructions for video editing, such as video length, theme, and background music.

[0557] "Automatic editing" refers to a process where software performs editing operations on a video without user intervention.

[0558] "Transition effects" refer to visual effects used to make the transition between video clips smoother.

[0559] "Effects" refer to visual or auditory effects added to a video.

[0560] "Seamless" refers to a state that is uninterrupted and smooth.

[0561] "Synchronization" refers to the synchronization of video and music.

[0562] An "access link" refers to the URL that allows users to access the generated video.

[0563] Modes for carrying out the invention

[0564] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video. This system is implemented according to the following specific procedure.

[0565] System Overview

[0566] 1. Shooting and uploading media data

[0567] Users use devices such as smartphones and tablets to take photos and videos of events and everyday scenes. The captured media data is uploaded to a server via a dedicated application.

[0568] 2. Data reception and analysis

[0569] The server receives media data sent by the user and stores it in storage. The received media data is analyzed by an image recognition AI using the Google Cloud Vision API. The analysis is performed to identify people and compositions, and the results are stored in the server's database.

[0570] 3. Media Selection

[0571] The server's image recognition AI selects the most suitable photos and video clips based on the analysis results. The selected media data is then used in the subsequent video editing process.

[0572] 4. Parameter Input

[0573] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, etc. The entered parameters are sent from the terminal to the server.

[0574] 5. Automatic video editing using AI

[0575] The server's video editing AI starts generating a video using editing tools such as the Adobe Premiere Pro API, based on the selected media data and user input parameters. Automatic editing adds transition effects and other effects, and seamlessly synchronizes the video and music.

[0576] 6. Saving and sharing the generated video

[0577] Once editing is complete, the video is saved to the server, and an access link is generated. This link is provided to the user, who can view and download the video through a dedicated application. It is also possible to easily share the video via social media or email.

[0578] Specific example

[0579] For example, user A attends a live music event and uses a dedicated application to take multiple photos and videos. They upload the captured data to a server, then set the video theme to "energetic atmosphere" and the video length to "5 minutes." These parameter inputs are made through the dedicated application's interface.

[0580] The server receives data from user A, and the image recognition AI uses the Google Cloud Vision API to identify people (artists or audience members) and important scenes. From the identified media data, the Adobe Premiere Pro API is used to select the best clips, and the video editing AI automatically generates a 5-minute video based on these. The video editing AI adds transition effects and specified energetic background music to complete the video.

[0581] The generated video is saved on the server, and a URL link to it is provided to User A. User A can view and download the high-quality video through this link. Furthermore, User A can easily share this video with friends and fans via social media or email.

[0582] This system allows users to easily create and share high-quality, efficiently edited videos, even without specialized knowledge.

[0583] Example of a prompt

[0584] "Use photos and videos taken by the user to create a 5-minute video with the theme 'Energetic Atmosphere.' Add transition effects and use the specified background music to ensure seamless video transitions and synchronization."

[0585] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0586] Step 1:

[0587] The user selects media data captured using their device and launches a dedicated application. The user then presses the "Upload" button to send the selected media data to the server.

[0588] Input: Photo and video data (device)

[0589] Output: Uploaded media data (server)

[0590] Specific operation: The user takes photos and videos of the event with their smartphone and selects the saved media data from the dedicated application. Then, they press the "Upload" button to start sending the data.

[0591] Step 2:

[0592] The server receives media data sent by the user and saves it to a specific directory. The server confirms that the upload is complete and notifies the user.

[0593] Input: Media data from the user (server)

[0594] Output: Saved media data (server)

[0595] Specific operation: The server receives the uploaded media data and saves it to the specified directory. Once the upload is complete, a "Upload complete" notification is sent to the user.

[0596] Step 3:

[0597] The server passes the stored media data to an image recognition AI (Google Cloud Vision API) for analysis. The image recognition AI identifies people and compositions, and saves the results to the server's database.

[0598] Input: Stored media data (server)

[0599] Output: Analysis results (server)

[0600] Specific operation: The server passes the received photos and videos to an image recognition AI. The Google Cloud Vision API analyzes the media data and identifies people and composition. The analysis results are stored in the server's database.

[0601] Step 4:

[0602] The server selects the optimal photos and video clips based on the analysis results of the image recognition AI. The selected media data is then provided to the next video editing stage.

[0603] Input: Analysis results (server)

[0604] Output: Selected media data (server)

[0605] Specific operation: Based on the analysis results, the server selects important scenes and high-quality photos from the event. The selected media data is then moved to a directory for video editing.

[0606] Step 5:

[0607] The user uses a dedicated application to input parameters for video generation. These parameters include the video length, theme, and background music. The entered parameters are sent to the server.

[0608] Input: Video generation parameters (device)

[0609] Output: Input parameters (server)

[0610] Specific operation: The user opens the application and sets the video generation options. For example, they might select "Video length: 5 minutes," "Theme: Energetic," and "Background music: Pop." After finishing the settings, they press the "Submit" button.

[0611] Step 6:

[0612] The server passes the selected media data and user input parameters to the video editing AI, and the automatic video generation begins.

[0613] Input: Selected media data, video generation parameters (server)

[0614] Output: Generated video (server)

[0615] Specific operation: The server uses the Adobe Premiere Pro API to edit the selected media data. It adds transition effects and other effects, and seamlessly synchronizes the video and music.

[0616] Step 7:

[0617] The server saves the generated video and creates an access link. This link is then provided to the user.

[0618] Input: Generated video (server)

[0619] Output: Access link (server, user)

[0620] Specific operation: After editing is complete, the video is saved to a directory on the server for long-term storage. The server generates an access link to the video and notifies the user.

[0621] Step 8:

[0622] Users can view and download the generated videos using the provided access link. They can also share the videos via social media or email.

[0623] Input: Access link (user)

[0624] Output: View, Download, Share (User)

[0625] Specific operation: The user clicks the notified link and watches a high-quality video. They can also download and save the video and share it on social media or via email as needed.

[0626] (Application Example 1)

[0627] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0628] Conventional video generation systems can analyze and edit multiple media data files shot by users to automatically generate integrated videos, but they lack the functionality to generate personalized advertising videos based on the individual user's interests and preferences. This results in limitations on the effectiveness and appeal of the advertisements.

[0629] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0630] In this invention, the server includes means for uploading media data captured by the user using an information processing device to the server; means for analyzing the received media data by image recognition to identify people and composition; means for selecting the optimal media data to be used for the video based on the analysis results; means for a video editing AI to generate an advertising video using background music and effects entered by the user; and means for sharing the generated advertising video through the user's social networking service or communication means. This makes it possible to automatically generate and share personalized advertising videos tailored to the user's interests and preferences.

[0631] An "information processing device" is a device that has the ability to process media data captured by a user and upload it to a server.

[0632] "Media data" refers to digital files containing image information captured by the user, such as photos and videos.

[0633] A "server" is a centralized computer system that receives, analyzes, and stores media data uploaded by users.

[0634] "Image recognition" is a technology in which algorithms on a server analyze and identify people and compositions contained in media data.

[0635] "Optimal media data" refers to data of suitable quality and content that the server has selected for use in video based on its analysis results.

[0636] "Video generation parameters" refer to the instructions and settings necessary for video generation, such as the desired video length, theme, and background music.

[0637] "Video editing AI" refers to artificial intelligence that automatically edits and generates videos based on selected media data and parameters entered by the user.

[0638] "Background music" refers to music that plays in the background while a video is being generated.

[0639] A "transition" is a scene-switching effect used in video editing, and it is a technique used to make the visual flow of the resulting video smoother.

[0640] A "social networking service" is a platform that allows users to share information and content with other users online.

[0641] "Communication methods" refer to the media and systems used by users to share videos they have created with other users online.

[0642] An "advertising video" is video content intended to promote a product or introduce a service, and is generated to suit the interests and preferences of a specific user.

[0643] A detailed description of the system that implements this application is provided below.

[0644] System Overview

[0645] This system allows users to upload media data captured using information processing devices (e.g., smart glasses or smartphones) to a server, which then analyzes the data to automatically generate and share personalized advertising videos.

[0646] Hardware and software to be used

[0647] Hardware: Information processing devices (smart glasses, smartphones), servers

[0648] Software: Image recognition AI, video editing AI, server management software, upload and sharing platform

[0649] Data processing

[0650] 1. Shooting and uploading media data

[0651] Users use smart glasses or smartphones to photograph everyday scenes and products. The captured media data is uploaded to a server via a dedicated application.

[0652] 2. Data reception and analysis

[0653] The server receives media data sent by the user and performs analysis using image recognition AI. In this analysis process, it identifies people and compositions and identifies products and brands that the user might be interested in.

[0654] 3. Media Selection

[0655] The server's image recognition AI selects the optimal media data to use for the video based on the analysis results. The selected media data is then used in the subsequent video editing process.

[0656] 4. Parameter Input

[0657] The user inputs video generation parameters such as background music and effects through an information processing device. These parameters are sent to a server and used as instructions for the video editing AI.

[0658] 5. Automatic video editing using AI

[0659] The server's video editing AI automatically generates advertising videos with transition effects and visual effects based on selected media data and background music and effect parameters entered by the user.

[0660] 6. Saving and sharing the generated ad videos

[0661] The edited ad video is saved on the server, and an access link is provided to the user. The user can use this link to view and download the generated ad video. Users can also easily share the ad video via social media or email.

[0662] Specific example

[0663] Users film themselves ordering coffee at their favorite cafe using smart glasses. Once the data is uploaded, AI recognizes the cafe's brand and automatically generates a cafe-related advertising video. The generated video includes specified background music and effects.

[0664] Example of a prompt

[0665] "Please film a video of a user at home wearing smart glasses, visiting a coffee shop, and ordering coffee, then upload the video. The server will analyze the video and generate an ad video relevant to the brand."

[0666] In this way, personalized advertising videos based on user behavior can be automatically generated and easily shared. This improves the effectiveness and appeal of the ads, and enhances the user experience.

[0667] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0668] Step 1:

[0669] The user captures media data (photos and videos) using an information processing device (smart glasses or smartphone). The input is the media data captured by the user. The output is this media data uploaded to a server via a dedicated application.

[0670] Step 2:

[0671] The server receives media data uploaded by users. The server receives the user-uploaded media data as input. It stores this data and performs analysis using image recognition AI. The output is analysis data, which includes information about identified individuals and composition.

[0672] Step 3:

[0673] The server's image recognition AI selects the optimal media data to use for the video based on the analyzed data. The input is the analysis results of the image recognition AI. Based on these results, the optimal media data is selected. The output is the selected media data.

[0674] Step 4:

[0675] The user inputs video generation parameters (background music, effects, video length, etc.) using an information processing device. The user's specified video generation parameters are returned as input. These parameters are then sent to the server as output.

[0676] Step 5:

[0677] The server's video editing AI automatically edits videos based on selected media data and video generation parameters entered by the user. The input consists of selected media data and video generation parameters. The server adds transition effects and visual effects to the video and applies background music. The output is the generated advertisement video.

[0678] Step 6:

[0679] The server stores the generated ad video and provides the user with an access link. The input is the generated ad video. The output is that the user can view and download the ad video via this link. A link is also provided so that the user can share the video via social media or email.

[0680] The system is configured so that specific data processing and calculations are performed at each step, ultimately resulting in the automatic generation and delivery of personalized, high-quality advertising videos to the user.

[0681] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0682] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video using an emotion engine. Specific embodiments of this system are described below.

[0683] System Overview

[0684] 1. Shooting and uploading media data

[0685] Users use a dedicated device (e.g., a smartphone) to take photos and videos of events and everyday scenes.

[0686] The captured media data is uploaded to the server via a dedicated application.

[0687] 2. Data reception and analysis

[0688] The server receives and stores media data sent by the user.

[0689] The image recognition AI on the server analyzes the received media data to identify people and composition.

[0690] 3. Emotional analysis using an emotion engine

[0691] The emotion engine analyzes the user's emotions from the audio and images in the uploaded media data.

[0692] Emotion analysis includes voice tone detection and facial expression recognition, and this data is linked to media data as emotion tags.

[0693] 4. Media Selection

[0694] Based on the emotion analysis results from the server's image recognition AI and emotion engine, the optimal photos and video clips are selected.

[0695] The selected media data will be used in the subsequent video editing process.

[0696] 5. Parameter Input

[0697] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, and more.

[0698] The emotion engine optimizes the video generation parameters entered by the user through emotion analysis and makes suggestions.

[0699] The input and optimized parameters are sent to the server, which acts as a guide for the video editing AI.

[0700] 6. Automatic video editing using AI

[0701] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data, user input, and optimized parameters.

[0702] Video editing AI enables seamless transitions between videos and synchronizes music and visuals.

[0703] 7. Saving and sharing the generated video

[0704] The edited video is saved on the server, and an access link is provided to the user.

[0705] Users can use this access link to view and download the completed video.

[0706] Furthermore, users can easily share videos via social media or email.

[0707] Specific example

[0708] For example, User A takes photos and videos of a family trip and uploads them to the server using a dedicated application. After uploading this data to the server, User A sets the video theme to "Fun Family Trip" and the video length to "5 minutes". These parameter inputs are made through the dedicated application's interface.

[0709] The server receives data from user A, and the image recognition AI identifies people (family members) and important scenes. Simultaneously, the emotion engine analyzes user A's emotions from their voice tone and facial expressions. Based on the emotion analysis results, the most suitable media data is selected, and the video editing AI automatically generates a 5-minute video using these. The video editing AI adds transition effects and background music that matches the theme, and saves the completed video.

[0710] The generated video is saved on the server, and a URL link to it is provided to User A. Through this link, User A can view and download a high-quality video documenting their family trip. Furthermore, User A can easily share this video with friends and family via social media or email.

[0711] Thus, the system according to the present invention can efficiently and automatically analyze and edit vast amounts of media data captured by multiple users, and by using an emotion engine, it is possible to easily generate and share customized, high-quality videos.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] User: Taking and uploading photos and videos

[0715] Users launch a dedicated app and take photos and videos of events and scenes.

[0716] Tapping the shutter button activates the device's camera function, and the captured photos and videos are temporarily saved to the device's memory.

[0717] The user taps the upload button within the app, selects the captured media data, and sends it to the server.

[0718] Step 2:

[0719] Server: Data reception and storage

[0720] The server receives media data sent by the user.

[0721] The received media data is stored in the server's storage system, and metadata (e.g., date and time of shooting, user ID, etc.) is added to each data item.

[0722] Step 3:

[0723] Server: Image recognition and analysis

[0724] The image recognition AI on the server begins analyzing the stored media data.

[0725] Image recognition AI performs face recognition, object recognition, and scene classification to identify people and important scenes.

[0726] The identification results are linked to each media data as metadata and are then used in the next processing step by the sentiment analysis engine.

[0727] Step 4:

[0728] Server: Emotion analysis by emotion engine

[0729] The emotion engine on the server analyzes the audio and images of the uploaded media data to extract the user's emotions.

[0730] In voice analysis, the tone, pitch, and speed of the voice are analyzed, while in image analysis, facial recognition technology is used to identify emotional states (e.g., joy, surprise, sadness).

[0731] The sentiment analysis results are added to the media data as metadata.

[0732] Step 5:

[0733] Server: Media Selection

[0734] Based on the emotion analysis results from the server's image recognition AI and emotion engine, the optimal photos and video clips are selected.

[0735] The media selection algorithm automatically selects the most appropriate scene by considering the identification results and sentiment analysis results.

[0736] Step 6:

[0737] User: Input of video generation parameters

[0738] The user accesses the parameter input screen for video generation using a dedicated application.

[0739] The user enters the required parameters (e.g., video length, theme, music) and taps the submit button.

[0740] The emotion engine analyzes the video generation parameters entered by the user and proposes emotion-based, optimized parameters.

[0741] Once the user confirms the proposed parameters, the final parameters are sent to the server.

[0742] Step 7:

[0743] Server: Starting the video editing AI

[0744] The server's video editing AI starts the editing process based on the selected media data and the final user input parameters.

[0745] The video editing AI edits the video by adding transitions and effects, and inserts text and effects that reflect the results of emotion analysis.

[0746] Step 8:

[0747] Server: Adding music and effects

[0748] The video editing AI adds background music specified by the user to the video.

[0749] Insert text (e.g., event name, date) as needed, and add effects to generate the final video.

[0750] Step 9:

[0751] Server: Final processing and storage of videos

[0752] The edited video is then given its final processing, encoded, and saved.

[0753] Generate an access link for the saved video and provide that link to the user.

[0754] Step 10:

[0755] User: Receiving and sharing the completed video

[0756] Users view and download the completed video via a link provided by the server.

[0757] Users can share their completed videos with others via social media or email using the sharing function within the dedicated app.

[0758] Through the above processing steps, users can easily generate and share high-quality videos that utilize sentiment analysis.

[0759] (Example 2)

[0760] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0761] Modern users often record their daily activities and events with photos and videos, but manually selecting highlight scenes from vast amounts of media data and editing videos to match emotions is extremely time-consuming and laborious. Furthermore, creating custom videos is difficult for users unfamiliar with editing techniques. Therefore, there is a need for a system that can analyze extensive media data and automatically generate and share optimal videos based on emotions.

[0762] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the media data received by the server using image recognition to identify people and composition, means for analyzing the user's emotions from the audio and images in the media data based on the analysis results, means for selecting the optimal media data to be used for the video based on the analysis results, means for optimizing the video generation parameters entered by the user, means for automatically editing the video on the server based on the optimized video generation parameters, adding transitions and effects and switching seamlessly, means for saving the edited video on the server and providing the user with an access link to the video, and means for the user to share the video via SNS or email. As a result, the user can automatically generate the optimal video based on emotions from a vast amount of media data and easily share it.

[0763] A "user" is an individual or group that transmits and receives information.

[0764] A "terminal" refers to a computer system or device used by a user to input or display information.

[0765] "Media data" refers to visual and auditory content stored in digital format, such as photographs, videos, and audio files.

[0766] A "server" is a computer system that manages and processes data via a network and responds to user requests.

[0767] "Uploading" refers to transferring data from a device to a server.

[0768] "Image recognition" is the process of automatically identifying objects and features within an image using computer vision technology.

[0769] "Analysis" is the process of examining data in detail and extracting specific results or information.

[0770] A "person" refers to a human being recognized within media data.

[0771] "Composition" refers to the arrangement and balance of visual elements in photographs and videos.

[0772] An "emotion engine" refers to an algorithm or system used to analyze emotions from data such as voice and facial expressions.

[0773] "Emotions" refer to human psychological states and sensations such as joy, sadness, and surprise.

[0774] "Optimization" refers to the process of making adjustments and improvements based on given conditions and constraints in order to obtain the most effective and efficient results.

[0775] "Video generation parameters" refer to settings such as the desired video length, theme, and background music.

[0776] A "transition" is the effect used in video editing to switch from one scene to another.

[0777] "Effects" refer to visual or auditory effects added to videos or images.

[0778] An "access link" is a web address (URL) used to access specific data or services.

[0779] "SNS" is an abbreviation for Social Networking Service, which is an online platform for users to share content and interact with each other.

[0780] "Sharing" refers to exchanging information or data with other users.

[0781] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video using an emotion engine. Specific embodiments of this system are described below.

[0782] Shooting and uploading media data

[0783] Users use smartphones or other devices to take photos and videos of events and everyday scenes. The captured media data is uploaded to a server via a dedicated application. After shooting, users launch the app, select the captured data, and upload it. The server saves the received data to high-speed storage.

[0784] Data reception and analysis

[0785] The server stores the received media data, and an image recognition AI analyzes the data. The image recognition AI analyzes photos and videos, identifying people and important compositions. For example, it identifies the faces of family members or scenery and tags them with metadata.

[0786] Emotional analysis using an emotion engine

[0787] The server's emotion engine analyzes audio and images within media data to detect the user's emotions. This includes voice tone detection and facial expression recognition, and this data is linked to the media data as emotion tags.

[0788] Media Selection

[0789] Based on the analysis results from the server's image recognition AI and emotion engine, the optimal photos and video clips are selected. The selected media data is then used in the subsequent video editing process.

[0790] Parameter Input

[0791] Users input parameters for video generation using a dedicated application. These parameters include video length, theme, and background music. The emotion engine optimizes and suggests video generation parameters entered by the user. The input and optimized parameters are sent to the server and used to instruct the video editing AI.

[0792] Automatic video editing using AI

[0793] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data, user input, and optimized parameters. The video editing AI performs seamless transitions between videos and synchronizes music and video.

[0794] Saving and sharing the generated videos

[0795] Once editing is complete, the video is saved on the server, and an access link is provided to the user. The user can use this access link to view and download the finished video. Furthermore, the user can easily share the video via social media or email.

[0796] Specific example

[0797] For example, User A films a family trip and uploads multiple photos and videos to the server using a dedicated application. After uploading this data to the server, User A sets the video theme to "Fun Family Trip" and the video length to "5 minutes." This parameter input is done through the dedicated application's interface. The server receives the data from User A, and an image recognition AI identifies family members and important scenes. Simultaneously, an emotion engine analyzes User A's emotions from their voice tone and facial expressions. Along with the emotion analysis results, the optimal media data is selected, and a video editing AI automatically generates a 5-minute video using these. The video editing AI adds transition effects and background music that matches the theme, and saves the completed video. The generated video is stored on the server, and a URL link is provided to User A. Through this link, User A can view and download a high-quality video that serves as a record of their family trip. User A can also easily share this video with friends and family via social media or email.

[0798] Example of a prompt

[0799] "To create a fun video of your family trip, please upload the following photos and video clips. Also, please specify the theme and length of your video."

[0800] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0801] Step 1:

[0802] The device captures and uploads media data.

[0803] Input: Photos and videos taken by the user with their device (e.g., smartphone).

[0804] Specific operation: The user takes photos of events or everyday scenes using their smartphone's camera app. After taking the photos, they launch a dedicated application, select the photos, and click the upload button.

[0805] Output: The selected media data is sent to the server.

[0806] Step 2:

[0807] The server receives and stores the media data.

[0808] Input: Media data uploaded from the device.

[0809] Specific operation: The server receives the uploaded data and saves it to high-speed storage. The saved data is registered in the database along with its metadata.

[0810] Output: Media data stored in the database.

[0811] Step 3:

[0812] The server uses image recognition AI to analyze media data.

[0813] Input: Saved media data.

[0814] Specific operation: An image recognition AI within the server analyzes stored photos and videos to identify people and important compositions.

[0815] Output: Information about identified individuals and compositions is added to the media data as metadata.

[0816] Step 4:

[0817] The server uses an emotion engine to analyze the emotions in media data.

[0818] Input: Analyzed media data.

[0819] Specific operation: The server activates the emotion engine and detects emotions from the tone of voice and facial expressions of people in the video. The detected emotion tags are linked to the media data.

[0820] Output: Media data with emotion tags attached.

[0821] Step 5:

[0822] The server selects the most suitable media based on the results of the image recognition AI and emotion engine.

[0823] Input: Media data tagged with emotion.

[0824] Specific operation: The server uses the analysis results to select the most suitable photos and video clips. Selection criteria include sentiment tags and the importance of the people involved.

[0825] Output: Selected optimal media data.

[0826] Step 6:

[0827] The user enters video generation parameters using a terminal.

[0828] Input: User's video generation parameters (e.g., video length, theme, background music, etc.).

[0829] Specific operation: The user inputs video generation parameters on the interface using a dedicated application.

[0830] Output: Parameters are sent to the server.

[0831] Step 7:

[0832] The server optimizes and suggests parameters based on sentiment analysis, using the parameters entered by the user.

[0833] Input: Video generation parameters entered by the user.

[0834] Specific operation: The server's emotion engine analyzes the user's input parameters and generates optimal suggestions. The suggested parameters are presented to the user through the application interface.

[0835] Output: Optimized video generation parameters.

[0836] Step 8:

[0837] The server uses AI-powered video editing software to automatically perform the editing.

[0838] Input: Optimized video generation parameters and selected media data.

[0839] Specific operation: The server's video editing AI automatically generates videos using selected media data and optimized parameters, adding transitions and effects. It also performs seamless video transitions and synchronizes music and video.

[0840] Output: The completed video file.

[0841] Step 9:

[0842] The server saves the generated video and creates an access link.

[0843] Input: The completed video file.

[0844] Specific operation: The completed video is saved to the server, and an access link to that video is generated. The access link is then notified to the user.

[0845] Output: The access link provided to the user.

[0846] Step 10:

[0847] Users view and share videos using access links.

[0848] Input: Access link.

[0849] Specific actions: Users use the provided access link to view and download the completed video. They can also share the video with other users via social media or email.

[0850] Output: Sharing videos with other users and viewing / downloading videos.

[0851] (Application Example 2)

[0852] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0853] Traditional content distribution services have made it difficult for users to easily and effectively edit videos and photos they have taken, and to generate and share high-quality highlight videos. In particular, the lack of emotion-based, personalized video editing made it difficult to create videos that matched the user's emotions and themes. Furthermore, the manual editing process was cumbersome, time-consuming, and laborious, which negatively impacted the user experience.

[0854] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the received media data by image recognition and identifying people and composition, means for selecting the optimal media data to be used for the video based on the analysis results, means for automatically editing the video on the server based on video generation parameters entered by the user, means for synchronizing the theme and background music with the generated video based on emotion analysis information, and means for saving the edited video on the server and providing the user with an access link to the video. As a result, users can automatically generate and share personalized, high-quality highlight videos based on emotions simply by uploading the media data they have captured.

[0855] "A means for users to upload media data they have captured using their device to a server" refers to a function that allows users to transfer photos and videos taken with their mobile devices to a server via the internet.

[0856] "Means for analyzing media data received by a server using image recognition to identify people and composition" refers to a technology that uses artificial intelligence and algorithms to analyze image information contained in photographs and videos received by a server to identify the subjects, people, and composition.

[0857] "A method for selecting the optimal media data to be used in a video based on analysis results" refers to a process of selecting the most appropriate photos and video clips based on the results of image recognition analysis and using them in the final video.

[0858] "A method for automatically editing videos on the server based on video generation parameters entered by the user" refers to a system in which the server automatically performs editing based on parameters such as the length, theme, and background music of the video specified by the user.

[0859] "A method for synchronizing themes and background music with generated videos based on sentiment analysis information" refers to a function that uses the user's sentiment information analyzed by the sentiment analysis engine to optimize and synchronize the video's theme and background music.

[0860] "A means of saving edited videos to a server and providing users with a link to access the videos" refers to a method of storing completed video files on a server and providing users with a link to access them.

[0861] The embodiments for carrying out the present invention will be described in detail below.

[0862] System Overview

[0863] System configuration:

[0864] The system of this invention begins with a user uploading media data (photos and videos) captured using a mobile device (e.g., a smartphone) to a server. This system consists of the following main components:

[0865] 1. Terminal:

[0866] Users use their mobile devices to photograph events and everyday scenes. The captured media data is uploaded to a server via a dedicated application.

[0867] 2. Server:

[0868] The server stores the received media data and analyzes it using image recognition AI and an emotion engine. Specifically, it analyzes images using OpenCV and analyzes emotions within the media data using DeepFace. Based on the analysis results, the most suitable media data is selected and automatically edited based on the video generation parameters entered by the user.

[0869] Specific example:

[0870] For example, a user films a family trip and uploads multiple photos and videos from their mobile device to the server. The user sets the video theme to "Fun Family Trip" and the video length to "5 minutes." The server uses image recognition AI to identify family members and important scenes, and an emotion engine analyzes emotions from voice tone and facial expressions. Based on these analysis results, the optimal media data is selected, and a video editing AI automatically generates a 5-minute highlight video by adding transition effects and background music that matches the theme.

[0871] Hardware and software

[0872] The following hardware and software will be used to implement this system.

[0873] Hardware:

[0874] Mobile devices (smartphones, etc.): Taking photos and uploading media data.

[0875] High-performance server: Performs media data analysis, storage, and video editing.

[0876] software:

[0877] OpenCV: An image recognition library used to analyze people and compositions.

[0878] DeepFace: An emotion analysis library used to analyze emotions within media data.

[0879] MoviePy is a video editing library used to generate videos based on selected media data.

[0880] Example prompts for a generative AI model:

[0881] "Analyze the following image and video data, select based on emotion, and create a highlight video that fits the theme. The theme is 'Family Trip,' and the video length should be 5 minutes."

[0882] [Video file path 1] [Video file path 2]...

[0883] Thus, the system of the present invention can automatically analyze media data captured by the user, select and edit the optimal media data using an emotion engine, and generate and share high-quality personalized videos.

[0884] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0885] Step 1:

[0886] The user uploads media data (photos and videos) captured using their device to the server. Specifically, the user transfers videos and photos they have taken to the server using a dedicated application. At this time, the server receives and stores the uploaded media data.

[0887] Input: Media data captured with the user's mobile device.

[0888] Output: Media data stored on the server.

[0889] Step 2:

[0890] The server analyzes the received media data using image recognition to identify people and composition. OpenCV is used to analyze the media data and recognize human faces and specific scenes within the image. This allows for identification, for example, of who the person in the photograph is or what the background is.

[0891] Input: Media data stored on the server.

[0892] Output: Information from the analyzed media data (people, composition, etc.).

[0893] Step 3:

[0894] The system uses an emotion engine to analyze emotions within media data. DeepFace is used to detect faces from media data (especially video frames) and determine emotions from facial expressions and voice tone. Based on these results, emotion tags are linked to the media data.

[0895] Input: Information from the analyzed media data (people, composition, etc.).

[0896] Output: Media data with added sentiment analysis information.

[0897] Step 4:

[0898] Based on the analysis results, the optimal media data is selected. Using a selection algorithm based on the sentiment analysis results and user-defined video generation parameters (e.g., theme, video length), the most appropriate media data is chosen. In this process, clips with a high proportion of positive sentiments such as "happy" and "surprise" are prioritized.

[0899] Input: Media data with added sentiment analysis information, and user video generation parameters.

[0900] Output: Selected optimal media data.

[0901] Step 5:

[0902] The server automatically performs video editing. Based on the selected media data and user-entered parameters, MoviePy is used to add transition effects, theme-appropriate background music, text clips, and more, generating a final highlight video.

[0903] Input: Selected optimal media data, user video generation parameters (e.g., theme, background music, video length).

[0904] Output: The completed highlight video.

[0905] Step 6:

[0906] The generated video is synchronized with themes and background music based on emotion analysis information. Based on information obtained from emotion tags, appropriate music and effects are synchronized for each scene in the video. This process results in emotionally rich and personalized videos.

[0907] Input: Completed highlight video, sentiment analysis information.

[0908] Output: A highlight video synchronized based on emotion analysis information.

[0909] Step 7:

[0910] The edited video is saved to the server, and the user is provided with an access link to the video. The completed video is stored as a database on the server, and by providing the user with an access link, the user can use that link to view, download, and share the video.

[0911] Input: A highlight video synchronized based on sentiment analysis information.

[0912] Output: The final video stored on the server and the access link provided to the user.

[0913] These steps allow users to easily create and share high-quality, personalized highlight videos without any hassle.

[0914] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0915] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0916] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0917] [Third Embodiment]

[0918] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0919] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0920] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0921] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0922] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0923] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0924] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0925] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0926] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0927] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0928] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0929] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0930] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video. Specific embodiments of this system are described below.

[0931] System Overview

[0932] 1. Shooting and uploading media data

[0933] Users use a dedicated device (e.g., a smartphone) to take photos and videos of events and everyday scenes.

[0934] The captured media data is uploaded to the server via a dedicated application.

[0935] 2. Data reception and analysis

[0936] The server receives and stores media data sent by the user.

[0937] The image recognition AI on the server analyzes the received media data to identify people and composition.

[0938] 3. Media Selection

[0939] The server's image recognition AI selects the most suitable photos and video clips based on the identification results.

[0940] The selected media data will be used in the subsequent video editing process.

[0941] 4. Parameter Input

[0942] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, and more.

[0943] The parameters entered by the user are sent to the server, which then acts as a guide for the video editing AI.

[0944] 5. Automatic video editing using AI

[0945] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data and user input parameters.

[0946] Video editing AI enables seamless transitions between videos and synchronizes music and visuals.

[0947] 6. Saving and sharing the generated video

[0948] The edited video is saved on the server, and an access link is provided to the user.

[0949] Users can use this access link to view and download the completed video.

[0950] Furthermore, users can easily share videos via social media or email.

[0951] Specific example

[0952] For example, User A attends a live music event and takes multiple photos and videos using a dedicated application. User A uploads this data to the server and then sets the video theme to "energetic atmosphere" and the video length to "5 minutes." This parameter input is done through the dedicated application's interface.

[0953] The server receives data from user A, and the image recognition AI identifies people (artists or audience members) and important scenes. From the identified media data, the optimal clips are selected, and the video editing AI automatically generates a 5-minute video using these clips. The video editing AI adds transition effects and specified energetic background music to complete the video.

[0954] The generated video is saved on the server, and a URL link to it is provided to User A. Through this link, User A can view and download a high-quality video that serves as a record of the event. Furthermore, User A can easily share this video with friends and fans via social media or email.

[0955] Thus, the system according to the present invention can efficiently and automatically analyze and edit vast amounts of media data captured by multiple users, and easily generate and share high-quality videos.

[0956] The following describes the processing flow.

[0957] Step 1:

[0958] User: Taking and uploading photos and videos

[0959] Users launch a dedicated app and take photos and videos of events and scenes.

[0960] Tapping the shutter button activates the device's camera function, and the captured photos and videos are temporarily saved to the device's memory.

[0961] The user taps the upload button within the app, selects the captured media data, and sends it to the server.

[0962] Step 2:

[0963] Server: Data reception and storage

[0964] The server receives media data sent by the user.

[0965] The received media data is stored in the server's storage system, and metadata (e.g., date and time of shooting, user ID, etc.) is added to each data item.

[0966] Step 3:

[0967] Server: Image recognition and analysis

[0968] The image recognition AI on the server begins analyzing the stored media data.

[0969] Image recognition AI performs face recognition, object recognition, and scene classification to identify people and important scenes.

[0970] The identification results are linked to each media data as metadata, preparing the system to select the most suitable media data.

[0971] Step 4:

[0972] User: Input of video generation parameters

[0973] Users access a parameter input screen for video generation within a dedicated app.

[0974] The user enters the required parameters (e.g., video length, theme, music) and taps the submit button.

[0975] The entered parameters will be sent to the server.

[0976] Step 5:

[0977] Server: Media selection and AI video editing startup

[0978] The server selects the optimal media data based on media data identified by image recognition AI and parameters specified by the user.

[0979] Based on the selected media data, the video editing AI is activated.

[0980] The video editing AI adds transitions and effects to selected photos and video clips to perform the editing.

[0981] Step 6:

[0982] Server: Adding music and effects

[0983] The video editing AI adds background music specified by the user to the video.

[0984] Insert text (e.g., event name, date) as needed, and add effects to generate the final video.

[0985] Step 7:

[0986] Server: Video storage and link generation

[0987] The edited video is then given its final processing, encoded, and saved.

[0988] Generate an access link to the saved video and provide that link to the user.

[0989] Step 8:

[0990] User: Receiving and sharing the completed video

[0991] Users view and download the completed video via a link provided by the server.

[0992] Users can share their completed videos with others via social media or email using the sharing function within the dedicated app.

[0993] Through the above processing steps, users can easily generate and share high-quality videos.

[0994] (Example 1)

[0995] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0996] In conventional systems, efficiently organizing large amounts of media data shot by users and compiling them into compelling videos was an extremely time-consuming and laborious process. Furthermore, for average users without specialized video editing knowledge, the process itself was a significant hurdle. Additionally, selecting the optimal clips from multiple media sources and synchronizing them with transition effects and background music was not easy. As a result, it was difficult for users to create and share high-quality videos.

[0997] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0998] In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the received media data by image recognition to identify people and composition, means for selecting the optimal media data to be used for the video based on the analysis results, means for automatically editing the video on the server based on video generation parameters entered by the user, means for automatically adding transition effects and other effects, means for seamlessly synchronizing the video and music, and means for saving the edited video to the server and providing the user with an access link to the video. As a result, users can easily create and share efficiently and high-quality edited videos without having specialized knowledge.

[0999] "Media data" refers to data in the form of photographs and videos.

[1000] "Terminal" refers to information processing devices used by users, such as smartphones, tablets, and computers.

[1001] A "server" refers to a computer system that receives and processes data sent by users from their terminals.

[1002] "Image recognition" refers to the technology used to identify people and compositions within media data.

[1003] "Analysis" refers to the process of examining data in detail and extracting specific information.

[1004] "Video generation parameters" refer to setting information that users input as instructions for video editing, such as video length, theme, and background music.

[1005] "Automatic editing" refers to a process where software performs editing operations on a video without user intervention.

[1006] "Transition effects" refer to visual effects used to make the transition between video clips smoother.

[1007] "Effects" refer to visual or auditory effects added to a video.

[1008] "Seamless" refers to a state that is uninterrupted and smooth.

[1009] "Synchronization" refers to the synchronization of video and music.

[1010] An "access link" refers to the URL that allows users to access the generated video.

[1011] Modes for carrying out the invention

[1012] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video. This system is implemented according to the following specific procedure.

[1013] System Overview

[1014] 1. Shooting and uploading media data

[1015] Users use devices such as smartphones and tablets to take photos and videos of events and everyday scenes. The captured media data is uploaded to a server via a dedicated application.

[1016] 2. Data reception and analysis

[1017] The server receives media data sent by the user and stores it in storage. The received media data is analyzed by an image recognition AI using the Google Cloud Vision API. The analysis is performed to identify people and compositions, and the results are stored in the server's database.

[1018] 3. Media Selection

[1019] The server's image recognition AI selects the most suitable photos and video clips based on the analysis results. The selected media data is then used in the subsequent video editing process.

[1020] 4. Parameter Input

[1021] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, etc. The entered parameters are sent from the terminal to the server.

[1022] 5. Automatic video editing using AI

[1023] The server's video editing AI starts generating a video using editing tools such as the Adobe Premiere Pro API, based on the selected media data and user input parameters. Automatic editing adds transition effects and other effects, and seamlessly synchronizes the video and music.

[1024] 6. Saving and sharing the generated video

[1025] Once editing is complete, the video is saved to the server, and an access link is generated. This link is provided to the user, who can view and download the video through a dedicated application. It is also possible to easily share the video via social media or email.

[1026] Specific example

[1027] For example, user A attends a live music event and uses a dedicated application to take multiple photos and videos. They upload the captured data to a server, then set the video theme to "energetic atmosphere" and the video length to "5 minutes." These parameter inputs are made through the dedicated application's interface.

[1028] The server receives data from user A, and the image recognition AI uses the Google Cloud Vision API to identify people (artists or audience members) and important scenes. From the identified media data, the Adobe Premiere Pro API is used to select the best clips, and the video editing AI automatically generates a 5-minute video based on these. The video editing AI adds transition effects and specified energetic background music to complete the video.

[1029] The generated video is saved on the server, and a URL link to it is provided to User A. User A can view and download the high-quality video through this link. Furthermore, User A can easily share this video with friends and fans via social media or email.

[1030] This system allows users to easily create and share high-quality, efficiently edited videos, even without specialized knowledge.

[1031] Example of a prompt

[1032] "Use photos and videos taken by the user to create a 5-minute video with the theme 'Energetic Atmosphere.' Add transition effects and use the specified background music to ensure seamless video transitions and synchronization."

[1033] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1034] Step 1:

[1035] The user selects media data captured using their device and launches a dedicated application. The user then presses the "Upload" button to send the selected media data to the server.

[1036] Input: Photo and video data (device)

[1037] Output: Uploaded media data (server)

[1038] Specific operation: The user takes photos and videos of the event with their smartphone and selects the saved media data from the dedicated application. Then, they press the "Upload" button to start sending the data.

[1039] Step 2:

[1040] The server receives media data sent by the user and saves it to a specific directory. The server confirms that the upload is complete and notifies the user.

[1041] Input: Media data from the user (server)

[1042] Output: Saved media data (server)

[1043] Specific operation: The server receives the uploaded media data and saves it to the specified directory. Once the upload is complete, a "Upload complete" notification is sent to the user.

[1044] Step 3:

[1045] The server passes the stored media data to an image recognition AI (Google Cloud Vision API) for analysis. The image recognition AI identifies people and compositions, and saves the results to the server's database.

[1046] Input: Stored media data (server)

[1047] Output: Analysis results (server)

[1048] Specific operation: The server passes the received photos and videos to an image recognition AI. The Google Cloud Vision API analyzes the media data and identifies people and composition. The analysis results are stored in the server's database.

[1049] Step 4:

[1050] The server selects the optimal photos and video clips based on the analysis results of the image recognition AI. The selected media data is then provided to the next video editing stage.

[1051] Input: Analysis results (server)

[1052] Output: Selected media data (server)

[1053] Specific operation: Based on the analysis results, the server selects important scenes and high-quality photos from the event. The selected media data is then moved to a directory for video editing.

[1054] Step 5:

[1055] The user uses a dedicated application to input parameters for video generation. These parameters include the video length, theme, and background music. The entered parameters are sent to the server.

[1056] Input: Video generation parameters (device)

[1057] Output: Input parameters (server)

[1058] Specific operation: The user opens the application and sets the video generation options. For example, they might select "Video length: 5 minutes," "Theme: Energetic," and "Background music: Pop." After finishing the settings, they press the "Submit" button.

[1059] Step 6:

[1060] The server passes the selected media data and user input parameters to the video editing AI, and the automatic video generation begins.

[1061] Input: Selected media data, video generation parameters (server)

[1062] Output: Generated video (server)

[1063] Specific operation: The server uses the Adobe Premiere Pro API to edit the selected media data. It adds transition effects and other effects, and seamlessly synchronizes the video and music.

[1064] Step 7:

[1065] The server saves the generated video and creates an access link. This link is then provided to the user.

[1066] Input: Generated video (server)

[1067] Output: Access link (server, user)

[1068] Specific operation: After editing is complete, the video is saved to a directory on the server for long-term storage. The server generates an access link to the video and notifies the user.

[1069] Step 8:

[1070] Users can view and download the generated videos using the provided access link. They can also share the videos via social media or email.

[1071] Input: Access link (user)

[1072] Output: View, Download, Share (User)

[1073] Specific operation: The user clicks the notified link and watches a high-quality video. They can also download and save the video and share it on social media or via email as needed.

[1074] (Application Example 1)

[1075] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1076] Conventional video generation systems can analyze and edit multiple media data files shot by users to automatically generate integrated videos, but they lack the functionality to generate personalized advertising videos based on the individual user's interests and preferences. This results in limitations on the effectiveness and appeal of the advertisements.

[1077] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1078] In this invention, the server includes means for uploading media data captured by the user using an information processing device to the server; means for analyzing the received media data by image recognition to identify people and composition; means for selecting the optimal media data to be used for the video based on the analysis results; means for a video editing AI to generate an advertising video using background music and effects entered by the user; and means for sharing the generated advertising video through the user's social networking service or communication means. This makes it possible to automatically generate and share personalized advertising videos tailored to the user's interests and preferences.

[1079] An "information processing device" is a device that has the ability to process media data captured by a user and upload it to a server.

[1080] "Media data" refers to digital files containing image information captured by the user, such as photos and videos.

[1081] A "server" is a centralized computer system that receives, analyzes, and stores media data uploaded by users.

[1082] "Image recognition" is a technology in which algorithms on a server analyze and identify people and compositions contained in media data.

[1083] "Optimal media data" refers to data of suitable quality and content that the server has selected for use in video based on its analysis results.

[1084] "Video generation parameters" refer to the instructions and settings necessary for video generation, such as the desired video length, theme, and background music.

[1085] "Video editing AI" refers to artificial intelligence that automatically edits and generates videos based on selected media data and parameters entered by the user.

[1086] "Background music" refers to music that plays in the background while a video is being generated.

[1087] A "transition" is a scene-switching effect used in video editing, and it is a technique used to make the visual flow of the resulting video smoother.

[1088] A "social networking service" is a platform that allows users to share information and content with other users online.

[1089] "Communication methods" refer to the media and systems used by users to share videos they have created with other users online.

[1090] An "advertising video" is video content intended to promote a product or introduce a service, and is generated to suit the interests and preferences of a specific user.

[1091] A detailed description of the system that implements this application is provided below.

[1092] System Overview

[1093] This system allows users to upload media data captured using information processing devices (e.g., smart glasses or smartphones) to a server, which then analyzes the data to automatically generate and share personalized advertising videos.

[1094] Hardware and software to be used

[1095] Hardware: Information processing devices (smart glasses, smartphones), servers

[1096] Software: Image recognition AI, video editing AI, server management software, upload and sharing platform

[1097] Data processing

[1098] 1. Shooting and uploading media data

[1099] Users use smart glasses or smartphones to photograph everyday scenes and products. The captured media data is uploaded to a server via a dedicated application.

[1100] 2. Data reception and analysis

[1101] The server receives media data sent by the user and performs analysis using image recognition AI. In this analysis process, it identifies people and compositions and identifies products and brands that the user might be interested in.

[1102] 3. Media Selection

[1103] The server's image recognition AI selects the optimal media data to use for the video based on the analysis results. The selected media data is then used in the subsequent video editing process.

[1104] 4. Parameter Input

[1105] The user inputs video generation parameters such as background music and effects through an information processing device. These parameters are sent to a server and used as instructions for the video editing AI.

[1106] 5. Automatic video editing using AI

[1107] The server's video editing AI automatically generates advertising videos with transition effects and visual effects based on selected media data and background music and effect parameters entered by the user.

[1108] 6. Saving and sharing the generated ad videos

[1109] The edited ad video is saved on the server, and an access link is provided to the user. The user can use this link to view and download the generated ad video. Users can also easily share the ad video via social media or email.

[1110] Specific example

[1111] Users film themselves ordering coffee at their favorite cafe using smart glasses. Once the data is uploaded, AI recognizes the cafe's brand and automatically generates a cafe-related advertising video. The generated video includes specified background music and effects.

[1112] Example of a prompt

[1113] "Please film a video of a user at home wearing smart glasses, visiting a coffee shop, and ordering coffee, then upload the video. The server will analyze the video and generate an ad video relevant to the brand."

[1114] In this way, personalized advertising videos based on user behavior can be automatically generated and easily shared. This improves the effectiveness and appeal of the ads, and enhances the user experience.

[1115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1116] Step 1:

[1117] The user captures media data (photos and videos) using an information processing device (smart glasses or smartphone). The input is the media data captured by the user. The output is this media data uploaded to a server via a dedicated application.

[1118] Step 2:

[1119] The server receives media data uploaded by users. The server receives the user-uploaded media data as input. It stores this data and performs analysis using image recognition AI. The output is analysis data, which includes information about identified individuals and composition.

[1120] Step 3:

[1121] The server's image recognition AI selects the optimal media data to use for the video based on the analyzed data. The input is the analysis results of the image recognition AI. Based on these results, the optimal media data is selected. The output is the selected media data.

[1122] Step 4:

[1123] The user inputs video generation parameters (background music, effects, video length, etc.) using an information processing device. The user's specified video generation parameters are returned as input. These parameters are then sent to the server as output.

[1124] Step 5:

[1125] The server's video editing AI automatically edits videos based on selected media data and video generation parameters entered by the user. The input consists of selected media data and video generation parameters. The server adds transition effects and visual effects to the video and applies background music. The output is the generated advertisement video.

[1126] Step 6:

[1127] The server stores the generated ad video and provides the user with an access link. The input is the generated ad video. The output is that the user can view and download the ad video via this link. A link is also provided so that the user can share the video via social media or email.

[1128] The system is configured so that specific data processing and calculations are performed at each step, ultimately resulting in the automatic generation and delivery of personalized, high-quality advertising videos to the user.

[1129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1130] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video using an emotion engine. Specific embodiments of this system are described below.

[1131] System Overview

[1132] 1. Shooting and uploading media data

[1133] Users use a dedicated device (e.g., a smartphone) to take photos and videos of events and everyday scenes.

[1134] The captured media data is uploaded to the server via a dedicated application.

[1135] 2. Data reception and analysis

[1136] The server receives and stores media data sent by the user.

[1137] The image recognition AI on the server analyzes the received media data to identify people and composition.

[1138] 3. Emotional analysis using an emotion engine

[1139] The emotion engine analyzes the user's emotions from the audio and images in the uploaded media data.

[1140] Emotion analysis includes voice tone detection and facial expression recognition, and this data is linked to media data as emotion tags.

[1141] 4. Media Selection

[1142] Based on the emotion analysis results from the server's image recognition AI and emotion engine, the optimal photos and video clips are selected.

[1143] The selected media data will be used in the subsequent video editing process.

[1144] 5. Parameter Input

[1145] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, and more.

[1146] The emotion engine optimizes the video generation parameters entered by the user through emotion analysis and makes suggestions.

[1147] The input and optimized parameters are sent to the server, which acts as a guide for the video editing AI.

[1148] 6. Automatic video editing using AI

[1149] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data, user input, and optimized parameters.

[1150] Video editing AI enables seamless transitions between videos and synchronizes music and visuals.

[1151] 7. Saving and sharing the generated video

[1152] The edited video is saved on the server, and an access link is provided to the user.

[1153] Users can use this access link to view and download the completed video.

[1154] Furthermore, users can easily share videos via social media or email.

[1155] Specific example

[1156] For example, User A takes photos and videos of a family trip and uploads them to the server using a dedicated application. After uploading this data to the server, User A sets the video theme to "Fun Family Trip" and the video length to "5 minutes". These parameter inputs are made through the dedicated application's interface.

[1157] The server receives data from user A, and the image recognition AI identifies people (family members) and important scenes. Simultaneously, the emotion engine analyzes user A's emotions from their voice tone and facial expressions. Based on the emotion analysis results, the most suitable media data is selected, and the video editing AI automatically generates a 5-minute video using these. The video editing AI adds transition effects and background music that matches the theme, and saves the completed video.

[1158] The generated video is saved on the server, and a URL link to it is provided to User A. Through this link, User A can view and download a high-quality video documenting their family trip. Furthermore, User A can easily share this video with friends and family via social media or email.

[1159] Thus, the system according to the present invention can efficiently and automatically analyze and edit vast amounts of media data captured by multiple users, and by using an emotion engine, it is possible to easily generate and share customized, high-quality videos.

[1160] The following describes the processing flow.

[1161] Step 1:

[1162] User: Taking and uploading photos and videos

[1163] Users launch a dedicated app and take photos and videos of events and scenes.

[1164] Tapping the shutter button activates the device's camera function, and the captured photos and videos are temporarily saved to the device's memory.

[1165] The user taps the upload button within the app, selects the captured media data, and sends it to the server.

[1166] Step 2:

[1167] Server: Data reception and storage

[1168] The server receives media data sent by the user.

[1169] The received media data is stored in the server's storage system, and metadata (e.g., date and time of shooting, user ID, etc.) is added to each data item.

[1170] Step 3:

[1171] Server: Image recognition and analysis

[1172] The image recognition AI on the server begins analyzing the stored media data.

[1173] Image recognition AI performs face recognition, object recognition, and scene classification to identify people and important scenes.

[1174] The identification results are linked to each media data as metadata and are then used in the next processing step by the sentiment analysis engine.

[1175] Step 4:

[1176] Server: Emotion analysis by emotion engine

[1177] The emotion engine on the server analyzes the audio and images of the uploaded media data to extract the user's emotions.

[1178] In voice analysis, the tone, pitch, and speed of the voice are analyzed, while in image analysis, facial recognition technology is used to identify emotional states (e.g., joy, surprise, sadness).

[1179] The sentiment analysis results are added to the media data as metadata.

[1180] Step 5:

[1181] Server: Media Selection

[1182] Based on the emotion analysis results from the server's image recognition AI and emotion engine, the optimal photos and video clips are selected.

[1183] The media selection algorithm automatically selects the most appropriate scene by considering the identification results and sentiment analysis results.

[1184] Step 6:

[1185] User: Input of video generation parameters

[1186] The user accesses the parameter input screen for video generation using a dedicated application.

[1187] The user enters the required parameters (e.g., video length, theme, music) and taps the submit button.

[1188] The emotion engine analyzes the video generation parameters entered by the user and proposes emotion-based, optimized parameters.

[1189] Once the user confirms the proposed parameters, the final parameters are sent to the server.

[1190] Step 7:

[1191] Server: Starting the video editing AI

[1192] The server's video editing AI starts the editing process based on the selected media data and the final user input parameters.

[1193] The video editing AI edits the video by adding transitions and effects, and inserts text and effects that reflect the results of emotion analysis.

[1194] Step 8:

[1195] Server: Adding music and effects

[1196] The video editing AI adds background music specified by the user to the video.

[1197] Insert text (e.g., event name, date) as needed, and add effects to generate the final video.

[1198] Step 9:

[1199] Server: Final processing and storage of videos

[1200] The edited video is then given its final processing, encoded, and saved.

[1201] Generate an access link for the saved video and provide that link to the user.

[1202] Step 10:

[1203] User: Receiving and sharing the completed video

[1204] Users view and download the completed video via a link provided by the server.

[1205] Users can share their completed videos with others via social media or email using the sharing function within the dedicated app.

[1206] Through the above processing steps, users can easily generate and share high-quality videos that utilize sentiment analysis.

[1207] (Example 2)

[1208] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1209] Modern users often record their daily activities and events with photos and videos, but manually selecting highlight scenes from vast amounts of media data and editing videos to match emotions is extremely time-consuming and laborious. Furthermore, creating custom videos is difficult for users unfamiliar with editing techniques. Therefore, there is a need for a system that can analyze extensive media data and automatically generate and share optimal videos based on emotions.

[1210] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the media data received by the server using image recognition to identify people and composition, means for analyzing the user's emotions from the audio and images in the media data based on the analysis results, means for selecting the optimal media data to be used for the video based on the analysis results, means for optimizing the video generation parameters entered by the user, means for automatically editing the video on the server based on the optimized video generation parameters, adding transitions and effects and switching seamlessly, means for saving the edited video on the server and providing the user with an access link to the video, and means for the user to share the video via SNS or email. As a result, the user can automatically generate the optimal video based on emotions from a vast amount of media data and easily share it.

[1211] A "user" is an individual or group that transmits and receives information.

[1212] A "terminal" refers to a computer system or device used by a user to input or display information.

[1213] "Media data" refers to visual and auditory content stored in digital format, such as photographs, videos, and audio files.

[1214] A "server" is a computer system that manages and processes data via a network and responds to user requests.

[1215] "Uploading" refers to transferring data from a device to a server.

[1216] "Image recognition" is the process of automatically identifying objects and features within an image using computer vision technology.

[1217] "Analysis" is the process of examining data in detail and extracting specific results or information.

[1218] A "person" refers to a human being recognized within media data.

[1219] "Composition" refers to the arrangement and balance of visual elements in photographs and videos.

[1220] An "emotion engine" refers to an algorithm or system used to analyze emotions from data such as voice and facial expressions.

[1221] "Emotions" refer to human psychological states and sensations such as joy, sadness, and surprise.

[1222] "Optimization" refers to the process of making adjustments and improvements based on given conditions and constraints in order to obtain the most effective and efficient results.

[1223] "Video generation parameters" refer to settings such as the desired video length, theme, and background music.

[1224] A "transition" is the effect used in video editing to switch from one scene to another.

[1225] "Effects" refer to visual or auditory effects added to videos or images.

[1226] An "access link" is a web address (URL) used to access specific data or services.

[1227] "SNS" is an abbreviation for Social Networking Service, which is an online platform for users to share content and interact with each other.

[1228] "Sharing" refers to exchanging information or data with other users.

[1229] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video using an emotion engine. Specific embodiments of this system are described below.

[1230] Shooting and uploading media data

[1231] Users use smartphones or other devices to take photos and videos of events and everyday scenes. The captured media data is uploaded to a server via a dedicated application. After shooting, users launch the app, select the captured data, and upload it. The server saves the received data to high-speed storage.

[1232] Data reception and analysis

[1233] The server stores the received media data, and an image recognition AI analyzes the data. The image recognition AI analyzes photos and videos, identifying people and important compositions. For example, it identifies the faces of family members or scenery and tags them with metadata.

[1234] Emotional analysis using an emotion engine

[1235] The server's emotion engine analyzes audio and images within media data to detect the user's emotions. This includes voice tone detection and facial expression recognition, and this data is linked to the media data as emotion tags.

[1236] Media Selection

[1237] Based on the analysis results from the server's image recognition AI and emotion engine, the optimal photos and video clips are selected. The selected media data is then used in the subsequent video editing process.

[1238] Parameter Input

[1239] Users input parameters for video generation using a dedicated application. These parameters include video length, theme, and background music. The emotion engine optimizes and suggests video generation parameters entered by the user. The input and optimized parameters are sent to the server and used to instruct the video editing AI.

[1240] Automatic video editing using AI

[1241] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data, user input, and optimized parameters. The video editing AI performs seamless transitions between videos and synchronizes music and video.

[1242] Saving and sharing the generated videos

[1243] Once editing is complete, the video is saved on the server, and an access link is provided to the user. The user can use this access link to view and download the finished video. Furthermore, the user can easily share the video via social media or email.

[1244] Specific example

[1245] For example, User A films a family trip and uploads multiple photos and videos to the server using a dedicated application. After uploading this data to the server, User A sets the video theme to "Fun Family Trip" and the video length to "5 minutes." This parameter input is done through the dedicated application's interface. The server receives the data from User A, and an image recognition AI identifies family members and important scenes. Simultaneously, an emotion engine analyzes User A's emotions from their voice tone and facial expressions. Along with the emotion analysis results, the optimal media data is selected, and a video editing AI automatically generates a 5-minute video using these. The video editing AI adds transition effects and background music that matches the theme, and saves the completed video. The generated video is stored on the server, and a URL link is provided to User A. Through this link, User A can view and download a high-quality video that serves as a record of their family trip. User A can also easily share this video with friends and family via social media or email.

[1246] Example of a prompt

[1247] "To create a fun video of your family trip, please upload the following photos and video clips. Also, please specify the theme and length of your video."

[1248] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1249] Step 1:

[1250] The device captures and uploads media data.

[1251] Input: Photos and videos taken by the user with their device (e.g., smartphone).

[1252] Specific operation: The user takes photos of events or everyday scenes using their smartphone's camera app. After taking the photos, they launch a dedicated application, select the photos, and click the upload button.

[1253] Output: The selected media data is sent to the server.

[1254] Step 2:

[1255] The server receives and stores the media data.

[1256] Input: Media data uploaded from the device.

[1257] Specific operation: The server receives the uploaded data and saves it to high-speed storage. The saved data is registered in the database along with its metadata.

[1258] Output: Media data stored in the database.

[1259] Step 3:

[1260] The server uses image recognition AI to analyze media data.

[1261] Input: Saved media data.

[1262] Specific operation: An image recognition AI within the server analyzes stored photos and videos to identify people and important compositions.

[1263] Output: Information about identified individuals and compositions is added to the media data as metadata.

[1264] Step 4:

[1265] The server uses an emotion engine to analyze the emotions in media data.

[1266] Input: Analyzed media data.

[1267] Specific operation: The server activates the emotion engine and detects emotions from the tone of voice and facial expressions of people in the video. The detected emotion tags are linked to the media data.

[1268] Output: Media data with emotion tags attached.

[1269] Step 5:

[1270] The server selects the most suitable media based on the results of the image recognition AI and emotion engine.

[1271] Input: Media data tagged with emotion.

[1272] Specific operation: The server uses the analysis results to select the most suitable photos and video clips. Selection criteria include sentiment tags and the importance of the people involved.

[1273] Output: Selected optimal media data.

[1274] Step 6:

[1275] The user enters video generation parameters using a terminal.

[1276] Input: User's video generation parameters (e.g., video length, theme, background music, etc.).

[1277] Specific operation: The user inputs video generation parameters on the interface using a dedicated application.

[1278] Output: Parameters are sent to the server.

[1279] Step 7:

[1280] The server optimizes and suggests parameters based on sentiment analysis, using the parameters entered by the user.

[1281] Input: Video generation parameters entered by the user.

[1282] Specific operation: The server's emotion engine analyzes the user's input parameters and generates optimal suggestions. The suggested parameters are presented to the user through the application interface.

[1283] Output: Optimized video generation parameters.

[1284] Step 8:

[1285] The server uses AI-powered video editing software to automatically perform the editing.

[1286] Input: Optimized video generation parameters and selected media data.

[1287] Specific operation: The server's video editing AI automatically generates videos using selected media data and optimized parameters, adding transitions and effects. It also performs seamless video transitions and synchronizes music and video.

[1288] Output: The completed video file.

[1289] Step 9:

[1290] The server saves the generated video and creates an access link.

[1291] Input: The completed video file.

[1292] Specific operation: The completed video is saved to the server, and an access link to that video is generated. The access link is then notified to the user.

[1293] Output: The access link provided to the user.

[1294] Step 10:

[1295] Users view and share videos using access links.

[1296] Input: Access link.

[1297] Specific actions: Users use the provided access link to view and download the completed video. They can also share the video with other users via social media or email.

[1298] Output: Sharing videos with other users and viewing / downloading videos.

[1299] (Application Example 2)

[1300] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1301] Traditional content distribution services have made it difficult for users to easily and effectively edit videos and photos they have taken, and to generate and share high-quality highlight videos. In particular, the lack of emotion-based, personalized video editing made it difficult to create videos that matched the user's emotions and themes. Furthermore, the manual editing process was cumbersome, time-consuming, and laborious, which negatively impacted the user experience.

[1302] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the received media data by image recognition and identifying people and composition, means for selecting the optimal media data to be used for the video based on the analysis results, means for automatically editing the video on the server based on video generation parameters entered by the user, means for synchronizing the theme and background music with the generated video based on emotion analysis information, and means for saving the edited video on the server and providing the user with an access link to the video. As a result, users can automatically generate and share personalized, high-quality highlight videos based on emotions simply by uploading the media data they have captured.

[1303] "A means for users to upload media data they have captured using their device to a server" refers to a function that allows users to transfer photos and videos taken with their mobile devices to a server via the internet.

[1304] "Means for analyzing media data received by a server using image recognition to identify people and composition" refers to a technology that uses artificial intelligence and algorithms to analyze image information contained in photographs and videos received by a server to identify the subjects, people, and composition.

[1305] "A method for selecting the optimal media data to be used in a video based on analysis results" refers to a process of selecting the most appropriate photos and video clips based on the results of image recognition analysis and using them in the final video.

[1306] "A method for automatically editing videos on the server based on video generation parameters entered by the user" refers to a system in which the server automatically performs editing based on parameters such as the length, theme, and background music of the video specified by the user.

[1307] "A method for synchronizing themes and background music with generated videos based on sentiment analysis information" refers to a function that uses the user's sentiment information analyzed by the sentiment analysis engine to optimize and synchronize the video's theme and background music.

[1308] "A means of saving edited videos to a server and providing users with a link to access the videos" refers to a method of storing completed video files on a server and providing users with a link to access them.

[1309] The embodiments for carrying out the present invention will be described in detail below.

[1310] System Overview

[1311] System configuration:

[1312] The system of this invention begins with a user uploading media data (photos and videos) captured using a mobile device (e.g., a smartphone) to a server. This system consists of the following main components:

[1313] 1. Terminal:

[1314] Users use their mobile devices to photograph events and everyday scenes. The captured media data is uploaded to a server via a dedicated application.

[1315] 2. Server:

[1316] The server stores the received media data and analyzes it using image recognition AI and an emotion engine. Specifically, it analyzes images using OpenCV and analyzes emotions within the media data using DeepFace. Based on the analysis results, the most suitable media data is selected and automatically edited based on the video generation parameters entered by the user.

[1317] Specific example:

[1318] For example, a user films a family trip and uploads multiple photos and videos from their mobile device to the server. The user sets the video theme to "Fun Family Trip" and the video length to "5 minutes." The server uses image recognition AI to identify family members and important scenes, and an emotion engine analyzes emotions from voice tone and facial expressions. Based on these analysis results, the optimal media data is selected, and a video editing AI automatically generates a 5-minute highlight video by adding transition effects and background music that matches the theme.

[1319] Hardware and software

[1320] The following hardware and software will be used to implement this system.

[1321] Hardware:

[1322] Mobile devices (smartphones, etc.): Taking photos and uploading media data.

[1323] High-performance server: Performs media data analysis, storage, and video editing.

[1324] software:

[1325] OpenCV: An image recognition library used to analyze people and compositions.

[1326] DeepFace: An emotion analysis library used to analyze emotions within media data.

[1327] MoviePy is a video editing library used to generate videos based on selected media data.

[1328] Example prompts for a generative AI model:

[1329] "Analyze the following image and video data, select based on emotion, and create a highlight video that fits the theme. The theme is 'Family Trip,' and the video length should be 5 minutes."

[1330] [Video file path 1] [Video file path 2]...

[1331] Thus, the system of the present invention can automatically analyze media data captured by the user, select and edit the optimal media data using an emotion engine, and generate and share high-quality personalized videos.

[1332] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1333] Step 1:

[1334] The user uploads media data (photos and videos) captured using their device to the server. Specifically, the user transfers videos and photos they have taken to the server using a dedicated application. At this time, the server receives and stores the uploaded media data.

[1335] Input: Media data captured with the user's mobile device.

[1336] Output: Media data stored on the server.

[1337] Step 2:

[1338] The server analyzes the received media data using image recognition to identify people and composition. OpenCV is used to analyze the media data and recognize human faces and specific scenes within the image. This allows for identification, for example, of who the person in the photograph is or what the background is.

[1339] Input: Media data stored on the server.

[1340] Output: Information from the analyzed media data (people, composition, etc.).

[1341] Step 3:

[1342] The system uses an emotion engine to analyze emotions within media data. DeepFace is used to detect faces from media data (especially video frames) and determine emotions from facial expressions and voice tone. Based on these results, emotion tags are linked to the media data.

[1343] Input: Information from the analyzed media data (people, composition, etc.).

[1344] Output: Media data with added sentiment analysis information.

[1345] Step 4:

[1346] Based on the analysis results, the optimal media data is selected. Using a selection algorithm based on the sentiment analysis results and user-defined video generation parameters (e.g., theme, video length), the most appropriate media data is chosen. In this process, clips with a high proportion of positive sentiments such as "happy" and "surprise" are prioritized.

[1347] Input: Media data with added sentiment analysis information, and user video generation parameters.

[1348] Output: Selected optimal media data.

[1349] Step 5:

[1350] The server automatically performs video editing. Based on the selected media data and user-entered parameters, MoviePy is used to add transition effects, theme-appropriate background music, text clips, and more, generating a final highlight video.

[1351] Input: Selected optimal media data, user video generation parameters (e.g., theme, background music, video length).

[1352] Output: The completed highlight video.

[1353] Step 6:

[1354] The generated video is synchronized with themes and background music based on emotion analysis information. Based on information obtained from emotion tags, appropriate music and effects are synchronized for each scene in the video. This process results in emotionally rich and personalized videos.

[1355] Input: Completed highlight video, sentiment analysis information.

[1356] Output: A highlight video synchronized based on emotion analysis information.

[1357] Step 7:

[1358] The edited video is saved to the server, and the user is provided with an access link to the video. The completed video is stored as a database on the server, and by providing the user with an access link, the user can use that link to view, download, and share the video.

[1359] Input: A highlight video synchronized based on sentiment analysis information.

[1360] Output: The final video stored on the server and the access link provided to the user.

[1361] These steps allow users to easily create and share high-quality, personalized highlight videos without any hassle.

[1362] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1363] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1364] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1365] [Fourth Embodiment]

[1366] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1367] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1368] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1369] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1370] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1371] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1372] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1373] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1374] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1375] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1376] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1377] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1378] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1379] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video. Specific embodiments of this system are described below.

[1380] System Overview

[1381] 1. Shooting and uploading media data

[1382] Users use a dedicated device (e.g., a smartphone) to take photos and videos of events and everyday scenes.

[1383] The captured media data is uploaded to the server via a dedicated application.

[1384] 2. Data reception and analysis

[1385] The server receives and stores media data sent by the user.

[1386] The image recognition AI on the server analyzes the received media data to identify people and composition.

[1387] 3. Media Selection

[1388] The server's image recognition AI selects the most suitable photos and video clips based on the identification results.

[1389] The selected media data will be used in the subsequent video editing process.

[1390] 4. Parameter Input

[1391] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, and more.

[1392] The parameters entered by the user are sent to the server, which then acts as a guide for the video editing AI.

[1393] 5. Automatic video editing using AI

[1394] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data and user input parameters.

[1395] Video editing AI enables seamless transitions between videos and synchronizes music and visuals.

[1396] 6. Saving and sharing the generated video

[1397] The edited video is saved on the server, and an access link is provided to the user.

[1398] Users can use this access link to view and download the completed video.

[1399] Furthermore, users can easily share videos via social media or email.

[1400] Specific example

[1401] For example, User A attends a live music event and takes multiple photos and videos using a dedicated application. User A uploads this data to the server and then sets the video theme to "energetic atmosphere" and the video length to "5 minutes." This parameter input is done through the dedicated application's interface.

[1402] The server receives data from user A, and the image recognition AI identifies people (artists or audience members) and important scenes. From the identified media data, the optimal clips are selected, and the video editing AI automatically generates a 5-minute video using these clips. The video editing AI adds transition effects and specified energetic background music to complete the video.

[1403] The generated video is saved on the server, and a URL link to it is provided to User A. Through this link, User A can view and download a high-quality video that serves as a record of the event. Furthermore, User A can easily share this video with friends and fans via social media or email.

[1404] Thus, the system according to the present invention can efficiently and automatically analyze and edit vast amounts of media data captured by multiple users, and easily generate and share high-quality videos.

[1405] The following describes the processing flow.

[1406] Step 1:

[1407] User: Taking and uploading photos and videos

[1408] Users launch a dedicated app and take photos and videos of events and scenes.

[1409] Tapping the shutter button activates the device's camera function, and the captured photos and videos are temporarily saved to the device's memory.

[1410] The user taps the upload button within the app, selects the captured media data, and sends it to the server.

[1411] Step 2:

[1412] Server: Data reception and storage

[1413] The server receives media data sent by the user.

[1414] The received media data is stored in the server's storage system, and metadata (e.g., date and time of shooting, user ID, etc.) is added to each data item.

[1415] Step 3:

[1416] Server: Image recognition and analysis

[1417] The image recognition AI on the server begins analyzing the stored media data.

[1418] Image recognition AI performs face recognition, object recognition, and scene classification to identify people and important scenes.

[1419] The identification results are linked to each media data as metadata, preparing the system to select the most suitable media data.

[1420] Step 4:

[1421] User: Input of video generation parameters

[1422] Users access a parameter input screen for video generation within a dedicated app.

[1423] The user enters the required parameters (e.g., video length, theme, music) and taps the submit button.

[1424] The entered parameters will be sent to the server.

[1425] Step 5:

[1426] Server: Media selection and AI video editing startup

[1427] The server selects the optimal media data based on media data identified by image recognition AI and parameters specified by the user.

[1428] Based on the selected media data, the video editing AI is activated.

[1429] The video editing AI adds transitions and effects to selected photos and video clips to perform the editing.

[1430] Step 6:

[1431] Server: Adding music and effects

[1432] The video editing AI adds background music specified by the user to the video.

[1433] Insert text (e.g., event name, date) as needed, and add effects to generate the final video.

[1434] Step 7:

[1435] Server: Video storage and link generation

[1436] The edited video is then given its final processing, encoded, and saved.

[1437] Generate an access link to the saved video and provide that link to the user.

[1438] Step 8:

[1439] User: Receiving and sharing the completed video

[1440] Users view and download the completed video via a link provided by the server.

[1441] Users can share their completed videos with others via social media or email using the sharing function within the dedicated app.

[1442] Through the above processing steps, users can easily generate and share high-quality videos.

[1443] (Example 1)

[1444] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1445] In conventional systems, efficiently organizing large amounts of media data shot by users and compiling them into compelling videos was an extremely time-consuming and laborious process. Furthermore, for average users without specialized video editing knowledge, the process itself was a significant hurdle. Additionally, selecting the optimal clips from multiple media sources and synchronizing them with transition effects and background music was not easy. As a result, it was difficult for users to create and share high-quality videos.

[1446] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1447] In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the received media data by image recognition to identify people and composition, means for selecting the optimal media data to be used for the video based on the analysis results, means for automatically editing the video on the server based on video generation parameters entered by the user, means for automatically adding transition effects and other effects, means for seamlessly synchronizing the video and music, and means for saving the edited video to the server and providing the user with an access link to the video. As a result, users can easily create and share efficiently and high-quality edited videos without having specialized knowledge.

[1448] "Media data" refers to data in the form of photographs and videos.

[1449] "Terminal" refers to information processing devices used by users, such as smartphones, tablets, and computers.

[1450] A "server" refers to a computer system that receives and processes data sent by users from their terminals.

[1451] "Image recognition" refers to the technology used to identify people and compositions within media data.

[1452] "Analysis" refers to the process of examining data in detail and extracting specific information.

[1453] "Video generation parameters" refer to setting information that users input as instructions for video editing, such as video length, theme, and background music.

[1454] "Automatic editing" refers to a process where software performs editing operations on a video without user intervention.

[1455] "Transition effects" refer to visual effects used to make the transition between video clips smoother.

[1456] "Effects" refer to visual or auditory effects added to a video.

[1457] "Seamless" refers to a state that is uninterrupted and smooth.

[1458] "Synchronization" refers to the synchronization of video and music.

[1459] An "access link" refers to the URL that allows users to access the generated video.

[1460] Modes for carrying out the invention

[1461] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video. This system is implemented according to the following specific procedure.

[1462] System Overview

[1463] 1. Shooting and uploading media data

[1464] Users use devices such as smartphones and tablets to take photos and videos of events and everyday scenes. The captured media data is uploaded to a server via a dedicated application.

[1465] 2. Data reception and analysis

[1466] The server receives media data sent by the user and stores it in storage. The received media data is analyzed by an image recognition AI using the Google Cloud Vision API. The analysis is performed to identify people and compositions, and the results are stored in the server's database.

[1467] 3. Media Selection

[1468] The server's image recognition AI selects the most suitable photos and video clips based on the analysis results. The selected media data is then used in the subsequent video editing process.

[1469] 4. Parameter Input

[1470] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, etc. The entered parameters are sent from the terminal to the server.

[1471] 5. Automatic video editing using AI

[1472] The server's video editing AI starts generating a video using editing tools such as the Adobe Premiere Pro API, based on the selected media data and user input parameters. Automatic editing adds transition effects and other effects, and seamlessly synchronizes the video and music.

[1473] 6. Saving and sharing the generated video

[1474] Once editing is complete, the video is saved to the server, and an access link is generated. This link is provided to the user, who can view and download the video through a dedicated application. It is also possible to easily share the video via social media or email.

[1475] Specific example

[1476] For example, user A attends a live music event and uses a dedicated application to take multiple photos and videos. They upload the captured data to a server, then set the video theme to "energetic atmosphere" and the video length to "5 minutes." These parameter inputs are made through the dedicated application's interface.

[1477] The server receives data from user A, and the image recognition AI uses the Google Cloud Vision API to identify people (artists or audience members) and important scenes. From the identified media data, the Adobe Premiere Pro API is used to select the best clips, and the video editing AI automatically generates a 5-minute video based on these. The video editing AI adds transition effects and specified energetic background music to complete the video.

[1478] The generated video is saved on the server, and a URL link to it is provided to User A. User A can view and download the high-quality video through this link. Furthermore, User A can easily share this video with friends and fans via social media or email.

[1479] This system allows users to easily create and share high-quality, efficiently edited videos, even without specialized knowledge.

[1480] Example of a prompt

[1481] "Use photos and videos taken by the user to create a 5-minute video with the theme 'Energetic Atmosphere.' Add transition effects and use the specified background music to ensure seamless video transitions and synchronization."

[1482] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1483] Step 1:

[1484] The user selects media data captured using their device and launches a dedicated application. The user then presses the "Upload" button to send the selected media data to the server.

[1485] Input: Photo and video data (device)

[1486] Output: Uploaded media data (server)

[1487] Specific operation: The user takes photos and videos of the event with their smartphone and selects the saved media data from the dedicated application. Then, they press the "Upload" button to start sending the data.

[1488] Step 2:

[1489] The server receives media data sent by the user and saves it to a specific directory. The server confirms that the upload is complete and notifies the user.

[1490] Input: Media data from the user (server)

[1491] Output: Saved media data (server)

[1492] Specific operation: The server receives the uploaded media data and saves it to the specified directory. Once the upload is complete, a "Upload complete" notification is sent to the user.

[1493] Step 3:

[1494] The server passes the stored media data to an image recognition AI (Google Cloud Vision API) for analysis. The image recognition AI identifies people and compositions, and saves the results to the server's database.

[1495] Input: Stored media data (server)

[1496] Output: Analysis results (server)

[1497] Specific operation: The server passes the received photos and videos to an image recognition AI. The Google Cloud Vision API analyzes the media data and identifies people and composition. The analysis results are stored in the server's database.

[1498] Step 4:

[1499] The server selects the optimal photos and video clips based on the analysis results of the image recognition AI. The selected media data is then provided to the next video editing stage.

[1500] Input: Analysis results (server)

[1501] Output: Selected media data (server)

[1502] Specific operation: Based on the analysis results, the server selects important scenes and high-quality photos from the event. The selected media data is then moved to a directory for video editing.

[1503] Step 5:

[1504] The user uses a dedicated application to input parameters for video generation. These parameters include the video length, theme, and background music. The entered parameters are sent to the server.

[1505] Input: Video generation parameters (device)

[1506] Output: Input parameters (server)

[1507] Specific operation: The user opens the application and sets the video generation options. For example, they might select "Video length: 5 minutes," "Theme: Energetic," and "Background music: Pop." After finishing the settings, they press the "Submit" button.

[1508] Step 6:

[1509] The server passes the selected media data and user input parameters to the video editing AI, and the automatic video generation begins.

[1510] Input: Selected media data, video generation parameters (server)

[1511] Output: Generated video (server)

[1512] Specific operation: The server uses the Adobe Premiere Pro API to edit the selected media data. It adds transition effects and other effects, and seamlessly synchronizes the video and music.

[1513] Step 7:

[1514] The server saves the generated video and creates an access link. This link is then provided to the user.

[1515] Input: Generated video (server)

[1516] Output: Access link (server, user)

[1517] Specific operation: After editing is complete, the video is saved to a directory on the server for long-term storage. The server generates an access link to the video and notifies the user.

[1518] Step 8:

[1519] Users can view and download the generated videos using the provided access link. They can also share the videos via social media or email.

[1520] Input: Access link (user)

[1521] Output: View, Download, Share (User)

[1522] Specific operation: The user clicks the notified link and watches a high-quality video. They can also download and save the video and share it on social media or via email as needed.

[1523] (Application Example 1)

[1524] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1525] Conventional video generation systems can analyze and edit multiple media data files shot by users to automatically generate integrated videos, but they lack the functionality to generate personalized advertising videos based on the individual user's interests and preferences. This results in limitations on the effectiveness and appeal of the advertisements.

[1526] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1527] In this invention, the server includes means for uploading media data captured by the user using an information processing device to the server; means for analyzing the received media data by image recognition to identify people and composition; means for selecting the optimal media data to be used for the video based on the analysis results; means for a video editing AI to generate an advertising video using background music and effects entered by the user; and means for sharing the generated advertising video through the user's social networking service or communication means. This makes it possible to automatically generate and share personalized advertising videos tailored to the user's interests and preferences.

[1528] An "information processing device" is a device that has the ability to process media data captured by a user and upload it to a server.

[1529] "Media data" refers to digital files containing image information captured by the user, such as photos and videos.

[1530] A "server" is a centralized computer system that receives, analyzes, and stores media data uploaded by users.

[1531] "Image recognition" is a technology in which algorithms on a server analyze and identify people and compositions contained in media data.

[1532] "Optimal media data" refers to data of suitable quality and content that the server has selected for use in video based on its analysis results.

[1533] "Video generation parameters" refer to the instructions and settings necessary for video generation, such as the desired video length, theme, and background music.

[1534] "Video editing AI" refers to artificial intelligence that automatically edits and generates videos based on selected media data and parameters entered by the user.

[1535] "Background music" refers to music that plays in the background while a video is being generated.

[1536] A "transition" is a scene-switching effect used in video editing, and it is a technique used to make the visual flow of the resulting video smoother.

[1537] A "social networking service" is a platform that allows users to share information and content with other users online.

[1538] "Communication methods" refer to the media and systems used by users to share videos they have created with other users online.

[1539] An "advertising video" is video content intended to promote a product or introduce a service, and is generated to suit the interests and preferences of a specific user.

[1540] A detailed description of the system that implements this application is provided below.

[1541] System Overview

[1542] This system allows users to upload media data captured using information processing devices (e.g., smart glasses or smartphones) to a server, which then analyzes the data to automatically generate and share personalized advertising videos.

[1543] Hardware and software to be used

[1544] Hardware: Information processing devices (smart glasses, smartphones), servers

[1545] Software: Image recognition AI, video editing AI, server management software, upload and sharing platform

[1546] Data processing

[1547] 1. Shooting and uploading media data

[1548] Users use smart glasses or smartphones to photograph everyday scenes and products. The captured media data is uploaded to a server via a dedicated application.

[1549] 2. Data reception and analysis

[1550] The server receives media data sent by the user and performs analysis using image recognition AI. In this analysis process, it identifies people and compositions and identifies products and brands that the user might be interested in.

[1551] 3. Media Selection

[1552] The server's image recognition AI selects the optimal media data to use for the video based on the analysis results. The selected media data is then used in the subsequent video editing process.

[1553] 4. Parameter Input

[1554] The user inputs video generation parameters such as background music and effects through an information processing device. These parameters are sent to a server and used as instructions for the video editing AI.

[1555] 5. Automatic video editing using AI

[1556] The server's video editing AI automatically generates advertising videos with transition effects and visual effects based on selected media data and background music and effect parameters entered by the user.

[1557] 6. Saving and sharing the generated ad videos

[1558] The edited ad video is saved on the server, and an access link is provided to the user. The user can use this link to view and download the generated ad video. Users can also easily share the ad video via social media or email.

[1559] Specific example

[1560] Users film themselves ordering coffee at their favorite cafe using smart glasses. Once the data is uploaded, AI recognizes the cafe's brand and automatically generates a cafe-related advertising video. The generated video includes specified background music and effects.

[1561] Example of a prompt

[1562] "Please film a video of a user at home wearing smart glasses, visiting a coffee shop, and ordering coffee, then upload the video. The server will analyze the video and generate an ad video relevant to the brand."

[1563] In this way, personalized advertising videos based on user behavior can be automatically generated and easily shared. This improves the effectiveness and appeal of the ads, and enhances the user experience.

[1564] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1565] Step 1:

[1566] The user captures media data (photos and videos) using an information processing device (smart glasses or smartphone). The input is the media data captured by the user. The output is this media data uploaded to a server via a dedicated application.

[1567] Step 2:

[1568] The server receives media data uploaded by users. The server receives the user-uploaded media data as input. It stores this data and performs analysis using image recognition AI. The output is analysis data, which includes information about identified individuals and composition.

[1569] Step 3:

[1570] The server's image recognition AI selects the optimal media data to use for the video based on the analyzed data. The input is the analysis results of the image recognition AI. Based on these results, the optimal media data is selected. The output is the selected media data.

[1571] Step 4:

[1572] The user inputs video generation parameters (background music, effects, video length, etc.) using an information processing device. The user's specified video generation parameters are returned as input. These parameters are then sent to the server as output.

[1573] Step 5:

[1574] The server's video editing AI automatically edits videos based on selected media data and video generation parameters entered by the user. The input consists of selected media data and video generation parameters. The server adds transition effects and visual effects to the video and applies background music. The output is the generated advertisement video.

[1575] Step 6:

[1576] The server stores the generated ad video and provides the user with an access link. The input is the generated ad video. The output is that the user can view and download the ad video via this link. A link is also provided so that the user can share the video via social media or email.

[1577] The system is configured so that specific data processing and calculations are performed at each step, ultimately resulting in the automatic generation and delivery of personalized, high-quality advertising videos to the user.

[1578] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1579] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video using an emotion engine. Specific embodiments of this system are described below.

[1580] System Overview

[1581] 1. Shooting and uploading media data

[1582] Users use a dedicated device (e.g., a smartphone) to take photos and videos of events and everyday scenes.

[1583] The captured media data is uploaded to the server via a dedicated application.

[1584] 2. Data reception and analysis

[1585] The server receives and stores media data sent by the user.

[1586] The image recognition AI on the server analyzes the received media data to identify people and composition.

[1587] 3. Emotional analysis using an emotion engine

[1588] The emotion engine analyzes the user's emotions from the audio and images in the uploaded media data.

[1589] Emotion analysis includes voice tone detection and facial expression recognition, and this data is linked to media data as emotion tags.

[1590] 4. Media Selection

[1591] Based on the emotion analysis results from the server's image recognition AI and emotion engine, the optimal photos and video clips are selected.

[1592] The selected media data will be used in the subsequent video editing process.

[1593] 5. Parameter Input

[1594] The user uses a dedicated application to input parameters for video generation. These parameters include video length, theme, background music, and more.

[1595] The emotion engine optimizes the video generation parameters entered by the user through emotion analysis and makes suggestions.

[1596] The input and optimized parameters are sent to the server, which acts as a guide for the video editing AI.

[1597] 6. Automatic video editing using AI

[1598] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data, user input, and optimized parameters.

[1599] Video editing AI enables seamless transitions between videos and synchronizes music and visuals.

[1600] 7. Saving and sharing the generated video

[1601] The edited video is saved on the server, and an access link is provided to the user.

[1602] Users can use this access link to view and download the completed video.

[1603] Furthermore, users can easily share videos via social media or email.

[1604] Specific example

[1605] For example, User A takes photos and videos of a family trip and uploads them to the server using a dedicated application. After uploading this data to the server, User A sets the video theme to "Fun Family Trip" and the video length to "5 minutes". These parameter inputs are made through the dedicated application's interface.

[1606] The server receives data from user A, and the image recognition AI identifies people (family members) and important scenes. Simultaneously, the emotion engine analyzes user A's emotions from their voice tone and facial expressions. Based on the emotion analysis results, the most suitable media data is selected, and the video editing AI automatically generates a 5-minute video using these. The video editing AI adds transition effects and background music that matches the theme, and saves the completed video.

[1607] The generated video is saved on the server, and a URL link to it is provided to User A. Through this link, User A can view and download a high-quality video documenting their family trip. Furthermore, User A can easily share this video with friends and family via social media or email.

[1608] Thus, the system according to the present invention can efficiently and automatically analyze and edit vast amounts of media data captured by multiple users, and by using an emotion engine, it is possible to easily generate and share customized, high-quality videos.

[1609] The following describes the processing flow.

[1610] Step 1:

[1611] User: Taking and uploading photos and videos

[1612] Users launch a dedicated app and take photos and videos of events and scenes.

[1613] Tapping the shutter button activates the device's camera function, and the captured photos and videos are temporarily saved to the device's memory.

[1614] The user taps the upload button within the app, selects the captured media data, and sends it to the server.

[1615] Step 2:

[1616] Server: Data reception and storage

[1617] The server receives media data sent by the user.

[1618] The received media data is stored in the server's storage system, and metadata (e.g., date and time of shooting, user ID, etc.) is added to each data item.

[1619] Step 3:

[1620] Server: Image recognition and analysis

[1621] The image recognition AI on the server begins analyzing the stored media data.

[1622] Image recognition AI performs face recognition, object recognition, and scene classification to identify people and important scenes.

[1623] The identification results are linked to each media data as metadata and are then used in the next processing step by the sentiment analysis engine.

[1624] Step 4:

[1625] Server: Emotion analysis by emotion engine

[1626] The emotion engine on the server analyzes the audio and images of the uploaded media data to extract the user's emotions.

[1627] In voice analysis, the tone, pitch, and speed of the voice are analyzed, while in image analysis, facial recognition technology is used to identify emotional states (e.g., joy, surprise, sadness).

[1628] The sentiment analysis results are added to the media data as metadata.

[1629] Step 5:

[1630] Server: Media Selection

[1631] Based on the emotion analysis results from the server's image recognition AI and emotion engine, the optimal photos and video clips are selected.

[1632] The media selection algorithm automatically selects the most appropriate scene by considering the identification results and sentiment analysis results.

[1633] Step 6:

[1634] User: Input of video generation parameters

[1635] The user accesses the parameter input screen for video generation using a dedicated application.

[1636] The user enters the required parameters (e.g., video length, theme, music) and taps the submit button.

[1637] The emotion engine analyzes the video generation parameters entered by the user and proposes emotion-based, optimized parameters.

[1638] Once the user confirms the proposed parameters, the final parameters are sent to the server.

[1639] Step 7:

[1640] Server: Starting the video editing AI

[1641] The server's video editing AI starts the editing process based on the selected media data and the final user input parameters.

[1642] The video editing AI edits the video by adding transitions and effects, and inserts text and effects that reflect the results of emotion analysis.

[1643] Step 8:

[1644] Server: Adding music and effects

[1645] The video editing AI adds background music specified by the user to the video.

[1646] Insert text (e.g., event name, date) as needed, and add effects to generate the final video.

[1647] Step 9:

[1648] Server: Final processing and storage of videos

[1649] The edited video is then given its final processing, encoded, and saved.

[1650] Generate an access link for the saved video and provide that link to the user.

[1651] Step 10:

[1652] User: Receiving and sharing the completed video

[1653] Users view and download the completed video via a link provided by the server.

[1654] Users can share their completed videos with others via social media or email using the sharing function within the dedicated app.

[1655] Through the above processing steps, users can easily generate and share high-quality videos that utilize sentiment analysis.

[1656] (Example 2)

[1657] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1658] Modern users often record their daily activities and events with photos and videos, but manually selecting highlight scenes from vast amounts of media data and editing videos to match emotions is extremely time-consuming and laborious. Furthermore, creating custom videos is difficult for users unfamiliar with editing techniques. Therefore, there is a need for a system that can analyze extensive media data and automatically generate and share optimal videos based on emotions.

[1659] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the media data received by the server using image recognition to identify people and composition, means for analyzing the user's emotions from the audio and images in the media data based on the analysis results, means for selecting the optimal media data to be used for the video based on the analysis results, means for optimizing the video generation parameters entered by the user, means for automatically editing the video on the server based on the optimized video generation parameters, adding transitions and effects and switching seamlessly, means for saving the edited video on the server and providing the user with an access link to the video, and means for the user to share the video via SNS or email. As a result, the user can automatically generate the optimal video based on emotions from a vast amount of media data and easily share it.

[1660] A "user" is an individual or group that transmits and receives information.

[1661] A "terminal" refers to a computer system or device used by a user to input or display information.

[1662] "Media data" refers to visual and auditory content stored in digital format, such as photographs, videos, and audio files.

[1663] A "server" is a computer system that manages and processes data via a network and responds to user requests.

[1664] "Uploading" refers to transferring data from a device to a server.

[1665] "Image recognition" is the process of automatically identifying objects and features within an image using computer vision technology.

[1666] "Analysis" is the process of examining data in detail and extracting specific results or information.

[1667] A "person" refers to a human being recognized within media data.

[1668] "Composition" refers to the arrangement and balance of visual elements in photographs and videos.

[1669] An "emotion engine" refers to an algorithm or system used to analyze emotions from data such as voice and facial expressions.

[1670] "Emotions" refer to human psychological states and sensations such as joy, sadness, and surprise.

[1671] "Optimization" refers to the process of making adjustments and improvements based on given conditions and constraints in order to obtain the most effective and efficient results.

[1672] "Video generation parameters" refer to settings such as the desired video length, theme, and background music.

[1673] A "transition" is the effect used in video editing to switch from one scene to another.

[1674] "Effects" refer to visual or auditory effects added to videos or images.

[1675] An "access link" is a web address (URL) used to access specific data or services.

[1676] "SNS" is an abbreviation for Social Networking Service, which is an online platform for users to share content and interact with each other.

[1677] "Sharing" refers to exchanging information or data with other users.

[1678] The system according to the present invention aims to automatically analyze media data captured by a user and generate a single integrated video using an emotion engine. Specific embodiments of this system are described below.

[1679] Shooting and uploading media data

[1680] Users use smartphones or other devices to take photos and videos of events and everyday scenes. The captured media data is uploaded to a server via a dedicated application. After shooting, users launch the app, select the captured data, and upload it. The server saves the received data to high-speed storage.

[1681] Data reception and analysis

[1682] The server stores the received media data, and an image recognition AI analyzes the data. The image recognition AI analyzes photos and videos, identifying people and important compositions. For example, it identifies the faces of family members or scenery and tags them with metadata.

[1683] Emotional analysis using an emotion engine

[1684] The server's emotion engine analyzes audio and images within media data to detect the user's emotions. This includes voice tone detection and facial expression recognition, and this data is linked to the media data as emotion tags.

[1685] Media Selection

[1686] Based on the analysis results from the server's image recognition AI and emotion engine, the optimal photos and video clips are selected. The selected media data is then used in the subsequent video editing process.

[1687] Parameter Input

[1688] Users input parameters for video generation using a dedicated application. These parameters include video length, theme, and background music. The emotion engine optimizes and suggests video generation parameters entered by the user. The input and optimized parameters are sent to the server and used to instruct the video editing AI.

[1689] Automatic video editing using AI

[1690] The server's video editing AI automatically generates videos by adding transitions and effects based on selected media data, user input, and optimized parameters. The video editing AI performs seamless transitions between videos and synchronizes music and video.

[1691] Saving and sharing the generated videos

[1692] Once editing is complete, the video is saved on the server, and an access link is provided to the user. The user can use this access link to view and download the finished video. Furthermore, the user can easily share the video via social media or email.

[1693] Specific example

[1694] For example, User A films a family trip and uploads multiple photos and videos to the server using a dedicated application. After uploading this data to the server, User A sets the video theme to "Fun Family Trip" and the video length to "5 minutes." This parameter input is done through the dedicated application's interface. The server receives the data from User A, and an image recognition AI identifies family members and important scenes. Simultaneously, an emotion engine analyzes User A's emotions from their voice tone and facial expressions. Along with the emotion analysis results, the optimal media data is selected, and a video editing AI automatically generates a 5-minute video using these. The video editing AI adds transition effects and background music that matches the theme, and saves the completed video. The generated video is stored on the server, and a URL link is provided to User A. Through this link, User A can view and download a high-quality video that serves as a record of their family trip. User A can also easily share this video with friends and family via social media or email.

[1695] Example of a prompt

[1696] "To create a fun video of your family trip, please upload the following photos and video clips. Also, please specify the theme and length of your video."

[1697] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1698] Step 1:

[1699] The device captures and uploads media data.

[1700] Input: Photos and videos taken by the user with their device (e.g., smartphone).

[1701] Specific operation: The user takes photos of events or everyday scenes using their smartphone's camera app. After taking the photos, they launch a dedicated application, select the photos, and click the upload button.

[1702] Output: The selected media data is sent to the server.

[1703] Step 2:

[1704] The server receives and stores the media data.

[1705] Input: Media data uploaded from the device.

[1706] Specific operation: The server receives the uploaded data and saves it to high-speed storage. The saved data is registered in the database along with its metadata.

[1707] Output: Media data stored in the database.

[1708] Step 3:

[1709] The server uses image recognition AI to analyze media data.

[1710] Input: Saved media data.

[1711] Specific operation: An image recognition AI within the server analyzes stored photos and videos to identify people and important compositions.

[1712] Output: Information about identified individuals and compositions is added to the media data as metadata.

[1713] Step 4:

[1714] The server uses an emotion engine to analyze the emotions in media data.

[1715] Input: Analyzed media data.

[1716] Specific operation: The server activates the emotion engine and detects emotions from the tone of voice and facial expressions of people in the video. The detected emotion tags are linked to the media data.

[1717] Output: Media data with emotion tags attached.

[1718] Step 5:

[1719] The server selects the most suitable media based on the results of the image recognition AI and emotion engine.

[1720] Input: Media data tagged with emotion.

[1721] Specific operation: The server uses the analysis results to select the most suitable photos and video clips. Selection criteria include sentiment tags and the importance of the people involved.

[1722] Output: Selected optimal media data.

[1723] Step 6:

[1724] The user enters video generation parameters using a terminal.

[1725] Input: User's video generation parameters (e.g., video length, theme, background music, etc.).

[1726] Specific operation: The user inputs video generation parameters on the interface using a dedicated application.

[1727] Output: Parameters are sent to the server.

[1728] Step 7:

[1729] The server optimizes and suggests parameters based on sentiment analysis, using the parameters entered by the user.

[1730] Input: Video generation parameters entered by the user.

[1731] Specific operation: The server's emotion engine analyzes the user's input parameters and generates optimal suggestions. The suggested parameters are presented to the user through the application interface.

[1732] Output: Optimized video generation parameters.

[1733] Step 8:

[1734] The server uses AI-powered video editing software to automatically perform the editing.

[1735] Input: Optimized video generation parameters and selected media data.

[1736] Specific operation: The server's video editing AI automatically generates videos using selected media data and optimized parameters, adding transitions and effects. It also performs seamless video transitions and synchronizes music and video.

[1737] Output: The completed video file.

[1738] Step 9:

[1739] The server saves the generated video and creates an access link.

[1740] Input: The completed video file.

[1741] Specific operation: The completed video is saved to the server, and an access link to that video is generated. The access link is then notified to the user.

[1742] Output: The access link provided to the user.

[1743] Step 10:

[1744] Users view and share videos using access links.

[1745] Input: Access link.

[1746] Specific actions: Users use the provided access link to view and download the completed video. They can also share the video with other users via social media or email.

[1747] Output: Sharing videos with other users and viewing / downloading videos.

[1748] (Application Example 2)

[1749] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1750] Traditional content distribution services have made it difficult for users to easily and effectively edit videos and photos they have taken, and to generate and share high-quality highlight videos. In particular, the lack of emotion-based, personalized video editing made it difficult to create videos that matched the user's emotions and themes. Furthermore, the manual editing process was cumbersome, time-consuming, and laborious, which negatively impacted the user experience.

[1751] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for uploading media data captured by the user using a terminal to the server, means for analyzing the received media data by image recognition and identifying people and composition, means for selecting the optimal media data to be used for the video based on the analysis results, means for automatically editing the video on the server based on video generation parameters entered by the user, means for synchronizing the theme and background music with the generated video based on emotion analysis information, and means for saving the edited video on the server and providing the user with an access link to the video. As a result, users can automatically generate and share personalized, high-quality highlight videos based on emotions simply by uploading the media data they have captured.

[1752] "A means for users to upload media data they have captured using their device to a server" refers to a function that allows users to transfer photos and videos taken with their mobile devices to a server via the internet.

[1753] "Means for analyzing media data received by a server using image recognition to identify people and composition" refers to a technology that uses artificial intelligence and algorithms to analyze image information contained in photographs and videos received by a server to identify the subjects, people, and composition.

[1754] "A method for selecting the optimal media data to be used in a video based on analysis results" refers to a process of selecting the most appropriate photos and video clips based on the results of image recognition analysis and using them in the final video.

[1755] "A method for automatically editing videos on the server based on video generation parameters entered by the user" refers to a system in which the server automatically performs editing based on parameters such as the length, theme, and background music of the video specified by the user.

[1756] "A method for synchronizing themes and background music with generated videos based on sentiment analysis information" refers to a function that uses the user's sentiment information analyzed by the sentiment analysis engine to optimize and synchronize the video's theme and background music.

[1757] "A means of saving edited videos to a server and providing users with a link to access the videos" refers to a method of storing completed video files on a server and providing users with a link to access them.

[1758] The embodiments for carrying out the present invention will be described in detail below.

[1759] System Overview

[1760] System configuration:

[1761] The system of this invention begins with a user uploading media data (photos and videos) captured using a mobile device (e.g., a smartphone) to a server. This system consists of the following main components:

[1762] 1. Terminal:

[1763] Users use their mobile devices to photograph events and everyday scenes. The captured media data is uploaded to a server via a dedicated application.

[1764] 2. Server:

[1765] The server stores the received media data and analyzes it using image recognition AI and an emotion engine. Specifically, it analyzes images using OpenCV and analyzes emotions within the media data using DeepFace. Based on the analysis results, the most suitable media data is selected and automatically edited based on the video generation parameters entered by the user.

[1766] Specific example:

[1767] For example, a user films a family trip and uploads multiple photos and videos from their mobile device to the server. The user sets the video theme to "Fun Family Trip" and the video length to "5 minutes." The server uses image recognition AI to identify family members and important scenes, and an emotion engine analyzes emotions from voice tone and facial expressions. Based on these analysis results, the optimal media data is selected, and a video editing AI automatically generates a 5-minute highlight video by adding transition effects and background music that matches the theme.

[1768] Hardware and software

[1769] The following hardware and software will be used to implement this system.

[1770] Hardware:

[1771] Mobile devices (smartphones, etc.): Taking photos and uploading media data.

[1772] High-performance server: Performs media data analysis, storage, and video editing.

[1773] software:

[1774] OpenCV: An image recognition library used to analyze people and compositions.

[1775] DeepFace: An emotion analysis library used to analyze emotions within media data.

[1776] MoviePy is a video editing library used to generate videos based on selected media data.

[1777] Example prompts for a generative AI model:

[1778] "Analyze the following image and video data, select based on emotion, and create a highlight video that fits the theme. The theme is 'Family Trip,' and the video length should be 5 minutes."

[1779] [Video file path 1] [Video file path 2]...

[1780] Thus, the system of the present invention can automatically analyze media data captured by the user, select and edit the optimal media data using an emotion engine, and generate and share high-quality personalized videos.

[1781] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1782] Step 1:

[1783] The user uploads media data (photos and videos) captured using their device to the server. Specifically, the user transfers videos and photos they have taken to the server using a dedicated application. At this time, the server receives and stores the uploaded media data.

[1784] Input: Media data captured with the user's mobile device.

[1785] Output: Media data stored on the server.

[1786] Step 2:

[1787] The server analyzes the received media data using image recognition to identify people and composition. OpenCV is used to analyze the media data and recognize human faces and specific scenes within the image. This allows for identification, for example, of who the person in the photograph is or what the background is.

[1788] Input: Media data stored on the server.

[1789] Output: Information from the analyzed media data (people, composition, etc.).

[1790] Step 3:

[1791] The system uses an emotion engine to analyze emotions within media data. DeepFace is used to detect faces from media data (especially video frames) and determine emotions from facial expressions and voice tone. Based on these results, emotion tags are linked to the media data.

[1792] Input: Information from the analyzed media data (people, composition, etc.).

[1793] Output: Media data with added sentiment analysis information.

[1794] Step 4:

[1795] Based on the analysis results, the optimal media data is selected. Using a selection algorithm based on the sentiment analysis results and user-defined video generation parameters (e.g., theme, video length), the most appropriate media data is chosen. In this process, clips with a high proportion of positive sentiments such as "happy" and "surprise" are prioritized.

[1796] Input: Media data with added sentiment analysis information, and user video generation parameters.

[1797] Output: Selected optimal media data.

[1798] Step 5:

[1799] The server automatically performs video editing. Based on the selected media data and user-entered parameters, MoviePy is used to add transition effects, theme-appropriate background music, text clips, and more, generating a final highlight video.

[1800] Input: Selected optimal media data, user video generation parameters (e.g., theme, background music, video length).

[1801] Output: The completed highlight video.

[1802] Step 6:

[1803] The generated video is synchronized with themes and background music based on emotion analysis information. Based on information obtained from emotion tags, appropriate music and effects are synchronized for each scene in the video. This process results in emotionally rich and personalized videos.

[1804] Input: Completed highlight video, sentiment analysis information.

[1805] Output: A highlight video synchronized based on emotion analysis information.

[1806] Step 7:

[1807] The edited video is saved to the server, and the user is provided with an access link to the video. The completed video is stored as a database on the server, and by providing the user with an access link, the user can use that link to view, download, and share the video.

[1808] Input: A highlight video synchronized based on sentiment analysis information.

[1809] Output: The final video stored on the server and the access link provided to the user.

[1810] These steps allow users to easily create and share high-quality, personalized highlight videos without any hassle.

[1811] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1812] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1813] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1814] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1815] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1816] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1817] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1818] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1819] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1820] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1821] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1822] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1823] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1824] 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.

[1825] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1826] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1827] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1828] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1829] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1830] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1831] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1832] The following is further disclosed regarding the embodiments described above.

[1833] (Claim 1)

[1834] A means for users to upload media data they have captured using their device to a server,

[1835] A means for analyzing media data received by a server using image recognition to identify people and composition,

[1836] A method for selecting the optimal media data to use for video based on the analysis results,

[1837] A method for automatically performing video editing on the server based on video generation parameters entered by the user,

[1838] A system that includes means for saving edited videos to a server and providing users with access links to the videos.

[1839] (Claim 2)

[1840] The system according to claim 1, comprising means for a user to input video generation parameters using a terminal.

[1841] (Claim 3)

[1842] The system according to claim 1, comprising means for a user to view and download a completed video via an access link.

[1843] "Example 1"

[1844] (Claim 1)

[1845] A means for users to upload media data they have captured using their device to a server,

[1846] A means for analyzing media data received by a server using image recognition to identify people and composition,

[1847] A method for selecting the optimal media data to use for video based on the analysis results,

[1848] A method for automatically performing video editing on the server based on video generation parameters entered by the user,

[1849] A method for automatically adding transition effects and other effects,

[1850] A means of seamlessly synchronizing video footage and music,

[1851] A system that includes means for saving edited videos to a server and providing users with access links to the videos.

[1852] (Claim 2)

[1853] The system according to claim 1, comprising means for a user to input video generation parameters using a terminal.

[1854] (Claim 3)

[1855] The system according to claim 1, comprising means for a user to view and download a completed video via an access link.

[1856] "Application Example 1"

[1857] (Claim 1)

[1858] A means for a user to upload media data captured using an information processing device to a server,

[1859] A means for analyzing media data received by a server using image recognition to identify people and composition,

[1860] A method for selecting the optimal media data to use for video based on the analysis results,

[1861] A method for automatically performing video editing on the server based on video generation parameters entered by the user,

[1862] A means of saving the edited video to a server and providing the user with an access link to the video,

[1863] A method by which a video editing AI generates an advertising video using background music and effects entered by the user,

[1864] A means of sharing the generated advertising video through the user's social networking service or communication means.

[1865] A system that includes this.

[1866] (Claim 2)

[1867] The system according to claim 1, comprising means for a user to input video generation parameters using an information processing device.

[1868] (Claim 3)

[1869] The system according to claim 1, comprising means for a user to view and download a completed video via an access link.

[1870] "Example 2 of combining an emotion engine"

[1871] (Claim 1)

[1872] A means for users to upload media data they have captured using their device to a server,

[1873] A means for analyzing media data received by a server using image recognition to identify people and composition,

[1874] A means for analyzing user emotions from audio and images within media data based on the analysis results,

[1875] A method for selecting the optimal media data to use for video based on the analysis results,

[1876] A means for optimizing the video generation parameters entered by the user,

[1877] A method for automatically editing videos on the server based on optimized video generation parameters, adding transitions and effects, and seamlessly switching between them.

[1878] A means of saving the edited video to a server and providing the user with an access link to the video,

[1879] A system that includes means for users to share videos via social media or email.

[1880] (Claim 2)

[1881] The system according to claim 1, comprising means for a user to input video generation parameters using a terminal, and for receiving optimized parameters through sentiment analysis and reflecting them in the editing process.

[1882] (Claim 3)

[1883] The system according to claim 1, comprising means for users to view and download completed videos via access links and share them via social media or email.

[1884] "Application example 2 when combining with an emotional engine"

[1885] (Claim 1)

[1886] A means for users to upload media data they have captured using their device to a server,

[1887] A means for analyzing media data received by a server using image recognition to identify people and composition,

[1888] A method for selecting the optimal media data to use for video based on the analysis results,

[1889] A method for automatically performing video editing on the server based on video generation parameters entered by the user,

[1890] A method for synchronizing themes and background music with the generated video based on emotion analysis information,

[1891] A system that includes means for saving edited videos to a server and providing users with access links to the videos.

[1892] (Claim 2)

[1893] The system according to claim 1, comprising means for a user to input video generation parameters using a terminal.

[1894] (Claim 3)

[1895] The system according to claim 1, comprising means for a user to view and download a completed video via an access link. [Explanation of Symbols]

[1896] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to upload media data they have captured using their device to a server, A means for analyzing media data received by a server using image recognition to identify people and composition, A method for selecting the optimal media data to use for video based on the analysis results, A method for automatically performing video editing on the server based on video generation parameters entered by the user, A system that includes means for saving edited videos to a server and providing users with access links to the videos.

2. The system according to claim 1, further comprising means for a user to input video generation parameters using a terminal.

3. The system according to claim 1, comprising means for a user to view and download a completed video via an access link.

Images