system

Abstract

We provide the system. [Solution] A means by which a user transmits video data via wireless data communication, A means for analyzing transmitted video data using automated technology and identifying scene changes, Means for dividing video data into multiple parts based on identified scenes, A means for automatically applying effective video transformations between divided sections, A means of applying visual effects and inserting textual information and visual elements, A means of presenting edited video to users on a video sharing device, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When the general public edits video data on their own computing devices, they face problems such as time constraints and a lack of knowledge about editing techniques. Also, it is generally difficult to efficiently organize a large amount of video data and edit it with high quality. Furthermore, technical knowledge for widely publishing the edited video data is also required, which poses an additional barrier. Due to these problems, video production and publication are very challenging activities for many users who have no experience in creating video content.

Means for Solving the Problems

[0005] This invention provides a fully automated system that allows users to easily edit and publish video data as a solution. Specifically, it includes means for transmitting video data to a server via wireless data communication, analyzing the video data using automated technology, and identifying scene changes. Based on the identified scenes, the video data is divided into multiple parts, and effective video transformations are applied. Furthermore, visual effects are automatically applied, and text information and visual elements are inserted, resulting in editing based on the user's style selection. Finally, by providing control means for presenting and publishing the edited video via a network, even users with limited technical knowledge can easily produce and publish high-quality video content.

[0006] "User" refers to an individual or organization that operates this system and edits and publishes video data.

[0007] "Video data" refers to digital data files that hold visual information in the form of still images or videos.

[0008] "Wireless data communication" refers to a method of sending and receiving data via wireless technology without requiring physical connections such as cables.

[0009] A "server" refers to a computing device that receives and manages data sent from a user's terminal.

[0010] "Automation technology" refers to technology that executes processes through programmed procedures without requiring manual operation.

[0011] A "scene change" refers to a point in a video where a clear change, either visually or in terms of content, is perceived.

[0012] "Video conversion" refers to various processes that alter the visual representation of video.

[0013] "Visual effects" refer to techniques that enhance the visual appeal of images by modifying their color, brightness, filters, and other elements.

[0014] "Textual information" refers to information in text format that is displayed within the video.

[0015] "Visual elements" refer to the non-textual visual elements that make up video content.

[0016] "Style selection" refers to the act of users choosing the editing theme, format, or design they desire.

[0017] A "network" refers to a system of digital communication devices that are interconnected for the purpose of transmitting data to each other.

[0018] "Public release" refers to the act of distributing created video content on the internet, making it accessible to a specific or unspecified number of viewers. [Brief explanation of the drawing]

[0019] [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] It shows an emotion map where multiple emotions are mapped. [Figure 10] It shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a processor with a reference numeral (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.

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] 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).

[0026] 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."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] This invention provides an automated system that enables users to easily edit and effectively publish video data. The program processing of this system is described below in natural language.

[0041] First, the user uses their device to transmit captured video data to the system using wireless data communication. The transmitted data is received by the server, stored in storage, and at the same time, metadata about the data is extracted.

[0042] Upon receiving the video data, the server begins analyzing it using automated technology. This analysis identifies scene transitions within the video and divides it into multiple sections to provide a visually pleasing experience. Next, video transformations are automatically applied between each of the divided scenes, according to the user's selected style.

[0043] Furthermore, visual effects are applied to the video data, and text information and visual elements are inserted as needed. This results in videos that are visually engaging and effectively convey information.

[0044] After editing is complete, the server generates the edited video and provides a preview to the terminal via the network. The user can review the preview and request additional edits or adjustments if necessary. Once the user is satisfied with the final video, they can select the video's publication settings and give final instructions for release.

[0045] Upon receiving final instructions for publication, the server publishes the video on the network and provides users with identification information for access. This system enables users with limited expertise in video editing to easily and quickly create and widely distribute high-quality video content.

[0046] As a concrete example, if a user uploads footage they shot during a trip to this system and selects the cinematic style, the system will divide the footage into beautiful night scenes, apply appropriate transitions and visual effects, and then allow the user to review the finished video. This automated process significantly simplifies the video editing and publishing process.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] When a user selects the video data they wish to edit using their device and presses the upload button, the video data is sent to the server using wireless data communication technology.

[0050] Step 2:

[0051] The server stores the received video data in storage and extracts basic video information (e.g., length, resolution, frame rate) as metadata.

[0052] Step 3:

[0053] The server uses metadata and automated technology to analyze scenes within the video and detect scene transitions. This allows it to split the video into multiple clips at appropriate points.

[0054] Step 4:

[0055] The user selects their preferred editing style from the options displayed on the device's interface. This information is sent to the server and used for the editing process.

[0056] Step 5:

[0057] The server applies video transformations to each clip based on the selected editing style and automatically inserts transitions. Simultaneously, it applies appropriate visual effects to each clip and inserts text and visual elements as needed.

[0058] Step 6:

[0059] Once editing is complete, the server generates a preview of the video data and sends a link or stream data to the terminal that allows playback of the preview. This preview allows the user to check the results of the editing.

[0060] Step 7:

[0061] The user can view a preview on their device and request additional corrections or adjustments from the server as needed. The server then re-edits the video based on this feedback and provides an updated preview again.

[0062] Step 8:

[0063] Once the user is satisfied with the final content, they select the video's publication settings and send the final publication instructions to the server.

[0064] Step 9:

[0065] Based on the final instructions, the server uploads the edited video to the network and makes it public. It then provides users with identification information and a URL for the published video, allowing them to share it with others.

[0066] (Example 1)

[0067] 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."

[0068] Conventional video editing systems often require a high level of expertise and effort during the video editing process, placing a significant burden on users. Furthermore, the lack of systems that enable high-quality editing with simple operation makes it difficult for many users to achieve satisfactory video editing results.

[0069] 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.

[0070] In this invention, the server includes a device for transmitting video information via wireless communication, a device for processing scene transitions using automated analysis techniques, and a device for automatically applying effective video transformations between divided sections. This makes it possible for users to easily perform high-quality and effective video editing without requiring specialized knowledge.

[0071] "Motion image information" refers to video data that is recorded in sequence from multiple images and played back visually as movement.

[0072] "Wireless communication means" refers to technologies that transmit and receive data using radio waves or other means without the need for physical connections such as cables.

[0073] The term "device" refers to a machine or system designed to perform a specific function or purpose.

[0074] "Automated analytical technology" refers to technologies that use artificial intelligence, machine learning, and other techniques to analyze and interpret data without human intervention.

[0075] "Scene change" refers to the process by which video footage changes to different content, composition, or viewpoint.

[0076] "Division" refers to each part when the whole is divided into multiple parts.

[0077] "Video conversion" refers to the process of changing or adding visual attributes to video information.

[0078] "Visual style" refers to a specific design or method of expression selected as a display effect for moving images.

[0079] A "communication network" refers to a network system designed for data communication.

[0080] "Identification information" refers to information used to distinguish and identify a specific object from other things.

[0081] This system is an automated system that enables users to easily edit and effectively publish video and image information they have captured using wireless communication.

[0082] First, the user sends video information captured using their device to the system. The device then uses wireless communication technology to send the video information to the server. Common wireless communication protocols are used in this process.

[0083] Next, the server analyzes the received video information. Automated analysis techniques are used here. Specifically, computer vision algorithms and machine learning models are employed to detect scene changes within the video. Existing software libraries such as Python and FFmpeg are utilized for the analysis.

[0084] The received video information is divided into multiple sections based on detected scene changes, and an effective video transformation is applied to each section. The server automatically applies the transformation based on the visual style selected by the user. Users can also select a cinematic style, and transition and visual effects are added to the video based on that selection.

[0085] Furthermore, the server applies visual effects and inserts text information and visual elements as needed. For example, in the case of travel videos, the names and dates of the visited locations will be displayed within the video. The server uses a generative AI model to automate this process.

[0086] After editing is complete, the server provides a preview of the edited video to the user's device. The user can review the preview on their device and submit requests for any further editing or adjustments needed.

[0087] For example, if a user wants to edit footage they shot during a trip in a cinematic style, the system will automatically divide the footage into scenes of beautiful nightscapes and apply appropriate transitions and visual effects. An example of a prompt using the generative AI model would be, "I want to add simple and easy-to-understand transitions to the footage from my trip and display the names of the tourist spots as an overlay."

[0088] Ultimately, the server publishes the edited video information over the network via the communication network and provides users with accessible identification information. This allows users to easily edit and widely distribute high-quality video content, even without specialized knowledge.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The user saves captured video information to their device and sends it to the server using wireless communication. In this step, the device receives the video file as input and sends the data to the server via a wireless communication channel. The output of this process is the transmitted video data. Specifically, the device uploads the data using Wi-Fi or a cellular network.

[0092] Step 2:

[0093] The server receives video information from the user as input and first stores it in storage. Simultaneously, it analyzes and extracts metadata from the data (e.g., date and time of capture, location information, etc.). In this step, the received data and its metadata are output. Specifically, a Python script performs data reception and initial analysis.

[0094] Step 3:

[0095] The server uses automated analysis techniques to detect scene changes based on stored video data. Here, video data is used as input, and a scene detection algorithm is applied. The output is the detected scene boundary. Specifically, a computer vision library performs the analysis.

[0096] Step 4:

[0097] The server divides the video information into multiple segments based on the detected scene divisions. The input consists of division position information and video data, while the output is the divided video segments. Video editing tools such as FFmpeg are used in this process.

[0098] Step 5:

[0099] The server applies transition effects to the divided video segments according to the visual style selected by the user. The segment and visual style settings are input, and the segments with added transitions are output. Specifically, a script is executed to add the transition effects.

[0100] Step 6:

[0101] The server applies visual effects to the video and inserts text information and visual elements as needed. The input is the segment information to which the effects should be applied, and the output is the decorated video segment. This includes the generation of text overlays.

[0102] Step 7:

[0103] The server combines the edited video footage and generates it as a single file. Each segment with applied visual effects is the input, and the complete edited video is the output. This is where the video editing program runs.

[0104] Step 8:

[0105] The server sends the generated, edited video to the terminal and provides a preview. The terminal receives the data and displays the preview video to the user. The display of the preview on the user's terminal is considered output.

[0106] Step 9:

[0107] The user reviews the preview and requests additional edits or adjustments if necessary. The input in this process is user feedback, and the output is correction instructions. The request is sent to the server.

[0108] Step 10:

[0109] The server publishes the video and images via the communication network after receiving final approval from the user. The input is the file information configured for publication, and the output is the published video and images along with identification information. After publication, the user receives the identification information.

[0110] (Application Example 1)

[0111] 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."

[0112] Traditional video editing systems have presented challenges for general users, requiring advanced expertise and effort to edit and publish high-quality videos, making them difficult to use. Furthermore, incorporating specific themes or styles into videos is complex and not easily achieved. Therefore, there is a need for a method that allows users to easily edit videos and generate and publish visually rich content without requiring specialized knowledge.

[0113] 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.

[0114] In this invention, the server includes means for a user to transmit video data via wireless data communication, means for analyzing the transmitted video data using automated technology and identifying scene changes, means for dividing the video data into multiple parts based on the identified scenes, means for automatically applying effective video transformations between the divided parts, means for applying visual effects and inserting text information and visual elements, means for presenting the edited video with subject-related visual effects and music added, and means for disseminating the edited video data on an online platform. This makes it possible for users to easily edit videos and quickly generate and share attractive and informational content.

[0115] A "user" is defined as the entity that operates the system to edit and publish video data.

[0116] "Video data" refers to a collection of information in video format that has been filmed or collected by the user.

[0117] "Wireless data communication" is a communication technology that transmits video data to a server without using cables or other means.

[0118] "Automation technology" refers to a series of techniques that perform processing mechanically and electronically, rather than manually.

[0119] "Analysis" refers to the technical process of breaking down data, examining its contents in detail, and finding meaning in it.

[0120] "Identifying scene transitions" refers to the function of recognizing different scenes in video data.

[0121] "Visual effects" refer to digital processing techniques used to add visual impact to images.

[0122] "Textual information and visual elements" refers to text and graphics used to convey information by overlaying them onto video.

[0123] "Edited video" refers to video data that has been processed with visual effects and transformations to make it suitable for public release.

[0124] An "online platform" is a foundational system for providing services to users over the internet.

[0125] "Diffusion" refers to the act of widely disseminating information or data through online platforms.

[0126] This system is implemented by having users transmit video data captured using devices such as smartphones and computers to a server via wireless data communication technology (e.g., Wi-Fi, 4G / 5G communication). The server first uses video processing libraries such as FFmpeg to analyze the received video data and employs technology to identify different scenes. This identification divides the video into multiple scenes, preparing it for the application of visual effects and editing. In particular, it uses APIs such as OpenCV and Adobe Premiere Pro to automatically apply specific visual effects and music to each scene based on the selected theme.

[0127] The server then displays a preview of the edited video to the user via the internet. The preview function is implemented using mobile application frameworks such as React Native, allowing users to check the edited results on their smartphones and send feedback or requests as needed. The final video is shared on online platforms via the YouTube® API, Facebook API, etc., based on the user's instructions.

[0128] As a concrete example, if a user films a family trip and wants to edit it using this system to create a video with the theme "Summer Vacation," the server will apply wave sounds and a bright filter, and then present the edited result to the user. This operation is instructed to the generating AI model using the following prompt: "We have received video data of your family trip. Please edit it with a bright and cheerful theme and add warm music to scenes that highlight the family's smiles."

[0129] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0130] Step 1:

[0131] The user prepares video data captured using their device. Next, they transmit this video data to the server using wireless data communication. In this process, the input is the raw video data captured by the user, and the output is the video data received by the server. A notification is displayed on the user's device to confirm that the transmission is complete.

[0132] Step 2:

[0133] The server saves the received video data to storage and simultaneously begins analyzing the data using the FFmpeg library. The input here is the video data stored on the server, and the output is the analyzed metadata and the scene change points in the video. In this step, data processing is performed to identify different scenes within the video and to pinpoint the change points.

[0134] Step 3:

[0135] The server divides the video data into multiple parts based on scenes identified using OpenCV. The input consists of analyzed metadata and scene transition points, and the output is video data for each divided scene. The server then prepares this divided video data for effect application in the next step.

[0136] Step 4:

[0137] The server uses the Adobe Premiere Pro API to automatically apply effective transitions and visual effects between segmented video sections based on the user's chosen theme. The inputs are the video data for each segmented scene and the user-selected theme, while the output is the video data after the effects have been applied. Additionally, music corresponding to the selected theme is added as background music.

[0138] Step 5:

[0139] The server displays a preview of the edited video on the terminal via the network. The input is the video data after the effects have been applied, and the output is a video preview presented visually to the user. The user can check the results on their smartphone and submit correction requests if necessary.

[0140] Step 6:

[0141] Once the user is satisfied with the final video edit, the server uses the YouTube API and Facebook API to prepare the finished video for publication on online platforms. The input is the edited video data approved by the user, and the output is the video data ready for publication along with an accessible link. The user then confirms that the video will be shared appropriately.

[0142] 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.

[0143] This invention combines an emotion engine with a system that allows users to effectively edit and publish video data. This system analyzes user emotions to provide a deeper user experience.

[0144] First, the user uploads video data captured using their device to the system. The video data is transmitted to the server via wireless data communication and stored there. Next, the server analyzes the video data and automatically identifies scene changes. After that, the video data is divided into multiple parts, and appropriate video conversion is applied between the divided parts.

[0145] The system incorporates an emotion engine that recognizes emotions from the user's facial expressions and voice. For example, while a user is editing a video, the system monitors their emotional state in real time through the camera and microphone. Based on this information, the server adjusts the visual effects and background music in the video to match the recognized emotions.

[0146] For example, if the user has a happy expression, the system can apply a bright tone filter and insert cheerful music. On the other hand, if an emotional expression is detected, a soft transition and calm music will be applied to the video.

[0147] The emotion engine also automatically selects the appropriate editing style based on the user's emotions, ensuring overall harmony in the video. This automated editing process allows users to create high-quality, emotionally resonant videos without requiring any special editing skills.

[0148] Finally, the edited video is previewed on the server and sent to the user's device for review. Additional editing and adjustments are made as needed to ensure a satisfactory result before the video is published over the network. At this stage, the emotion engine automatically adjusts publication settings based on the user's emotional state, providing an optimal viewing experience. This allows users to share more personalized video content.

[0149] The following describes the processing flow.

[0150] Step 1:

[0151] The user selects the video data they want to edit using their device and sends it to the server via wireless data communication. The video files are transferred using a secure protocol.

[0152] Step 2:

[0153] The server stores the received video data in a database and analyzes and records metadata (e.g., resolution, frame rate, file size, etc.).

[0154] Step 3:

[0155] The camera and microphone on the user's device are activated, capturing the user's facial expressions and voice in real time. This information is sent to the emotion engine, which analyzes the user's emotional state.

[0156] Step 4:

[0157] The server begins analyzing the video data and identifies scene changes. Based on this, it divides the video into multiple clips.

[0158] Step 5:

[0159] Once the emotion engine recognizes the user's emotions, the server automatically selects a video style based on those emotions and applies appropriate transitions and effects to each clip. For example, a cheerful emotion might be accompanied by a bright filter and cheerful music.

[0160] Step 6:

[0161] The server inserts visual effects, text information, and visual elements to generate edited video data. This process results in a customized video that matches the emotions being conveyed.

[0162] Step 7:

[0163] A preview of the edited video data is generated and sent to the device in real time. The user can play the preview and check the content.

[0164] Step 8:

[0165] If the user deems it necessary, they can provide feedback to the server to make additional adjustments. For example, they can fine-tune the volume or change to different music.

[0166] Step 9:

[0167] Once the user approves the edited video, the server executes the process of publishing it to the network. At this point, the emotion engine applies publishing settings based on the user's emotions to create the optimal viewing experience.

[0168] Step 10:

[0169] The URL and identification information of the published video are provided to the user, and they are guided on how to easily share it with other users. This entire process allows users to easily create and distribute emotionally resonant video content.

[0170] (Example 2)

[0171] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0172] Users face the challenge of needing significant time and technical skills to effectively edit video data and provide personalized visual experiences. Furthermore, editing videos to reflect emotional states requires specialized knowledge, making it difficult for the average user. Therefore, there is a need to provide automated, emotion-based video editing methods to simplify the editing process.

[0173] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0174] In this invention, the server includes means for the user to transmit video data via wireless data communication, means for analyzing the transmitted video data using automated technology and identifying scene changes, and means for analyzing the user's emotions using facial recognition technology and adjusting visual effects. This makes it possible for users to quickly create high-quality videos that reflect their emotions and provide a personalized video experience without requiring complex editing skills.

[0175] A "user" is defined as the entity that operates the system and transmits and edits video data.

[0176] "Video data" refers to visual information such as videos and images taken by the user, and is data that is subject to editing.

[0177] "Wireless data communication" is a communication method that transmits and receives data using radio waves or other means, without using physical connections such as cables.

[0178] "Automation technology" refers to technologies that enable machines and software to automatically perform tasks that were previously done manually.

[0179] "Scene transitions" refer to changes in different scenes or shots within a video, and are elements that constitute the visual flow.

[0180] "Facial expression recognition technology" is a technology that analyzes a user's facial expressions through sensors and cameras to identify their emotional state.

[0181] "Visual effects" are effects used to add visual changes and embellishments to a video, thereby adjusting its impression and atmosphere.

[0182] "Editing style" refers to the techniques and forms of expression that reflect a consistent design or theme in video editing.

[0183] "Emotional information" refers to data that represents the user's emotional state and is used to adjust visual effects and sound in video editing.

[0184] A "video publishing device" is a device or platform for displaying and playing edited video so that users or others can view it.

[0185] This system edits user-recorded video data, incorporating emotional elements to provide a personalized visual experience. Users record video data using a device and upload it to a server via wireless data communication. The device can be a standard smartphone or tablet.

[0186] The server analyzes the received video data using automated technology to identify scene changes. Image analysis software can be used for this process. Additionally, video editing software is used to divide the video data into multiple segments based on the identified scenes, and effective video transformations are automatically applied between the divided segments.

[0187] To incorporate emotional elements, the server uses facial recognition technology to analyze the user's facial expressions and voice to recognize their emotions. For example, it analyzes data acquired in real time through the camera and microphone and adjusts visual effects and background music according to the user's emotions. If the emotion indicates "happiness," a bright filter can be applied to the video and cheerful music can be inserted.

[0188] Furthermore, based on emotional information, the server automatically selects an editing style for the entire video, providing a consistent visual experience. This allows users to generate high-quality videos without requiring any special editing skills.

[0189] Finally, the edited video is generated on the server and sent to the user's device as a preview. The user can review this preview and make additional adjustments as needed. The final, adjusted video can be published over the network, and the emotion engine automatically adjusts the publication settings based on the user's emotions.

[0190] An example of a prompt to a generative AI model is, "Please suggest an automated video editing method based on the user's emotions." This prompt will lead the generative AI model to derive a more sophisticated video editing method.

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The user collects video data captured using their device and uploads it to the system. A video data file is provided as input. This data is transmitted to the server using wireless data communication. The output is the video data stored on the server.

[0194] Step 2:

[0195] The server analyzes the received video data. Uploaded video data is used as input. The server uses image analysis software to detect scene changes within the data. This provides information about the points where scenes change as output.

[0196] Step 3:

[0197] The server divides the video data into multiple segments based on the identified switching points. The inputs are the switching point information obtained in step 2 and the original video data. The output is the divided segment data.

[0198] Step 4:

[0199] The server automatically applies effective video transformations between the divided segments. The input is the divided segment data from step 3, and specific video editing software is used. The output is video data with transitions and effects applied.

[0200] Step 5:

[0201] The user provides their facial expressions and voice to the system via camera and microphone. The input is the user's facial and voice data collected in real time. The server analyzes this data using an emotion recognition engine to recognize the user's emotional state. The output is the detected emotion data.

[0202] Step 6:

[0203] The server adjusts the visual effects and background music in the video based on the emotion data. The input consists of the emotion data obtained in step 5 and the edited video data. As a result, the output video reflects the user's emotions with appropriate visual effects and music.

[0204] Step 7:

[0205] The server automatically selects the overall editing style for the video, creating a harmonious and cohesive image. The input is video data adjusted according to emotion; the output is the finished video work.

[0206] Step 8:

[0207] The server sends the completed video to the terminal for preview. The user can then make further adjustments and edits based on this data. The input is the completed video data, and the output, based on the user's evaluation, is the video with final adjustments made.

[0208] Step 9:

[0209] The server publishes the final video over the network and automatically configures the appropriate viewing settings. The input is the video data adjusted in step 8, and the emotion engine handles the publication settings. The output is the video work published online.

[0210] (Application Example 2)

[0211] 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 device 14 will be referred to as the "terminal."

[0212] With the advancement of information and communication technology, many users are now routinely shooting and sharing videos. However, most users lack advanced editing skills, making it difficult to efficiently create videos that reflect their emotions and experiences. Furthermore, simple visual effects alone are insufficient to create a sufficient emotional impact on viewers. In particular, there is a need for technology that can easily transform videos into visually rich and emotionally resonant content.

[0213] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0214] In this invention, the server includes means for identifying the user's emotions using emotion analysis technology, means for analyzing transmitted video data using automation technology and identifying scene changes, and means for dividing the video data into multiple parts based on the identified scenes and the user's emotions, and automatically applying emotion-appropriate visual and audio effects between the divided parts. As a result, users can easily generate and share sophisticated and visually appealing video content that reflects their own emotions without having to acquire editing skills.

[0215] "User" refers to an individual who uses the system to capture video data and edits and shares that data.

[0216] "Wireless data communication" refers to a method of communication that transmits data without using wired connections such as cables.

[0217] "Emotion analysis technology" refers to technology that identifies a user's emotions from their facial expressions and voice.

[0218] "Automated technology" refers to technology that automatically performs analysis and processing of video data without human intervention.

[0219] "Means for identifying scene transitions" refers to a process for detecting transition points between different scenes within video data.

[0220] "Identified scenes" refer to different scenes or episodes that have been recognized and distinguished by the system.

[0221] "Visual and audio effects" refer to embellishments such as filters, music, and transition effects applied to video content to enrich the viewing experience.

[0222] A "video distribution device" refers to a display device used to present edited video data to users or other viewers.

[0223] The system for implementing this invention has the function of allowing users to emotionally edit and share their own videos. The system consists of an application that runs on a smartphone or smart glasses and a cloud-based server that processes the data.

[0224] The user's smart device captures facial expressions and voice simultaneously with video recording using its camera and microphone. This data is first uploaded to a server in the cloud. The server uses emotion analysis tools such as Google Cloud Vision API and Amazon Rekognition to analyze the user's emotions in real time. Based on the analysis results, appropriate visual effects are applied to the video using the OpenCV library, and background music is inserted using the Spotify API, etc.

[0225] The server further uses automated technology to identify scene transitions in the video data, divides it into multiple video segments based on the user's emotions and the identified scenes, and applies effects to each segment. As a result, users can create video content that reflects their own emotions, even without specialized skills.

[0226] As a concrete example, imagine a video of a family picnic. In scenes where children are happily playing, the app detects smiles and automatically applies a bright filter and cheerful music to the scene. This process happens in real time, allowing users to quickly share visually rich and emotionally impactful videos after shooting.

[0227] An example of a prompt message would be, "We are filming children having fun; please add a bright filter and upbeat music to the moments when they smile." This allows users to perform emotionally conscious editing simply by entering a simple prompt from their device.

[0228] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0229] Step 1:

[0230] The device collects video and audio data using its camera and microphone when the user takes a video. The input here is physical video and audio, and the output is digital video and audio data. The device uploads this data to a server in the cloud via wireless data communication.

[0231] Step 2:

[0232] The server receives uploaded video and audio data and analyzes the user's emotions using sentiment analysis tools such as the Google Cloud Vision API and Amazon Rekognition. The input is video and audio data, and the output is information about the user's emotions. Through this process, the server identifies the emotions the user expresses during filming in real time.

[0233] Step 3:

[0234] The server uses automated technology to analyze and identify scene changes using the transmitted video data. The input is video data, and the output is the transition point for each scene. This allows the server to divide the video into individual scenes.

[0235] Step 4:

[0236] The server uses the OpenCV library to apply appropriate visual effects to each portion of the video based on the identified scenes and the analyzed user sentiment information. The input consists of the divided video segments and sentiment information, and the output is the video data with the applied visual effects.

[0237] Step 5:

[0238] The server inserts background music and sound effects into video data using the Spotify API and other tools based on the user's emotions. The input is emotional information and the target video data, and the output is video data with added sound effects. This process ensures that the final video visually and aurally matches the emotions.

[0239] Step 6:

[0240] The server sends the edited video data to the user's terminal and provides a preview. This allows the user to review the automatically edited video and make additional edits as needed. The input is the final edited video data, and the output is the user's visual review process.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] [Second Embodiment]

[0245] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0246] 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.

[0247] 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).

[0248] 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.

[0249] 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.

[0250] 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).

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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".

[0257] This invention provides an automated system that enables users to easily edit and effectively publish video data. The program processing of this system is described below in natural language.

[0258] First, the user uses their device to transmit captured video data to the system using wireless data communication. The transmitted data is received by the server, stored in storage, and at the same time, metadata about the data is extracted.

[0259] Upon receiving the video data, the server begins analyzing it using automated technology. This analysis identifies scene transitions within the video and divides it into multiple sections to provide a visually pleasing experience. Next, video transformations are automatically applied between each of the divided scenes, according to the user's selected style.

[0260] Furthermore, visual effects are applied to the video data, and text information and visual elements are inserted as needed. This results in videos that are visually engaging and effectively convey information.

[0261] After editing is complete, the server generates the edited video and provides a preview to the terminal via the network. The user can review the preview and request additional edits or adjustments if necessary. Once the user is satisfied with the final video, they can select the video's publication settings and give final instructions for release.

[0262] Upon receiving final instructions for publication, the server publishes the video on the network and provides users with identification information for access. This system enables users with limited expertise in video editing to easily and quickly create and widely distribute high-quality video content.

[0263] As a concrete example, if a user uploads footage they shot during a trip to this system and selects the cinematic style, the system will divide the footage into beautiful night scenes, apply appropriate transitions and visual effects, and then allow the user to review the finished video. This automated process significantly simplifies the video editing and publishing process.

[0264] The following describes the processing flow.

[0265] Step 1:

[0266] When a user selects the video data they wish to edit using their device and presses the upload button, the video data is sent to the server using wireless data communication technology.

[0267] Step 2:

[0268] The server stores the received video data in storage and extracts basic video information (e.g., length, resolution, frame rate) as metadata.

[0269] Step 3:

[0270] The server uses metadata and automated technology to analyze scenes within the video and detect scene transitions. This allows it to split the video into multiple clips at appropriate points.

[0271] Step 4:

[0272] The user selects their preferred editing style from the options displayed on the device's interface. This information is sent to the server and used for the editing process.

[0273] Step 5:

[0274] The server applies video transformations to each clip based on the selected editing style and automatically inserts transitions. Simultaneously, it applies appropriate visual effects to each clip and inserts text and visual elements as needed.

[0275] Step 6:

[0276] Once editing is complete, the server generates a preview of the video data and sends a link or stream data to the terminal that allows playback of the preview. This preview allows the user to check the results of the editing.

[0277] Step 7:

[0278] The user checks the preview on the terminal and requests the server for additional modifications or adjustments if necessary. The server re-edits the video according to this feedback and provides the updated preview again.

[0279] Step 8:

[0280] Finally, when the content that the user is satisfied with is completed, the user selects the publication settings of the video and sends the final publication instruction to the server.

[0281] Step 9:

[0282] Based on the final instruction, the server uploads the edited video to the network and publishes it. The server provides the user with the identification information and URL of the published video so that the user can share it with others. <000089>>

[0283] (Example 1)

[0284] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In a conventional moving image editing system, a high level of expertise and effort are often required in the process of editing a moving image, which is a burden for users. In addition, since there is a lack of a system that can achieve high-quality editing effects with simple operations, there is a problem that it is difficult for many users to perform satisfactory moving image editing.

[0286] The specific processing by the specific processing unit 90 of the data processing device 12 in Example 1 is realized by the following means.

[0287] [[ID=]]<00oo908>In this invention, the server includes a device that transmits moving image information by wireless communication means, a device that processes scene switching using an automated analysis technology, and a device that automatically applies effective moving image conversion to divided sections. As a result, users can easily perform high-quality and effective moving image editing without requiring specialized knowledge.

[0288] "Motion image information" refers to video data that is recorded in sequence from multiple images and played back visually as movement.

[0289] "Wireless communication means" refers to technologies that transmit and receive data using radio waves or other means without the need for physical connections such as cables.

[0290] The term "device" refers to a machine or system designed to perform a specific function or purpose.

[0291] "Automated analytical technology" refers to technologies that use artificial intelligence, machine learning, and other techniques to analyze and interpret data without human intervention.

[0292] "Scene change" refers to the process by which video footage changes to different content, composition, or viewpoint.

[0293] "Division" refers to each part when the whole is divided into multiple parts.

[0294] "Video conversion" refers to the process of changing or adding visual attributes to video information.

[0295] "Visual style" refers to a specific design or method of expression selected as a display effect for moving images.

[0296] A "communication network" refers to a network system designed for data communication.

[0297] "Identification information" refers to information used to distinguish and identify a specific object from other things.

[0298] This system is an automated system that enables users to easily edit and effectively publish video and image information they have captured using wireless communication.

[0299] First, the user sends video information captured using their device to the system. The device then uses wireless communication technology to send the video information to the server. Common wireless communication protocols are used in this process.

[0300] Next, the server analyzes the received video information. Automated analysis techniques are used here. Specifically, computer vision algorithms and machine learning models are employed to detect scene changes within the video. Existing software libraries such as Python and FFmpeg are utilized for the analysis.

[0301] The received video information is divided into multiple sections based on detected scene changes, and an effective video transformation is applied to each section. The server automatically applies the transformation based on the visual style selected by the user. Users can also select a cinematic style, and transition and visual effects are added to the video based on that selection.

[0302] Furthermore, the server applies visual effects and inserts text information and visual elements as needed. For example, in the case of travel videos, the names and dates of the visited locations will be displayed within the video. The server uses a generative AI model to automate this process.

[0303] After editing is complete, the server provides a preview of the edited video to the user's device. The user can review the preview on their device and submit requests for any further editing or adjustments needed.

[0304] For example, if a user wants to edit footage they shot during a trip in a cinematic style, the system will automatically divide the footage into scenes of beautiful nightscapes and apply appropriate transitions and visual effects. An example of a prompt using the generative AI model would be, "I want to add simple and easy-to-understand transitions to the footage from my trip and display the names of the tourist spots as an overlay."

[0305] Finally, the server publishes the edited moving image information on the network through the communication network and provides the user with accessible identification information. As a result, even without specialized knowledge, users can easily edit high-quality video content and transmit it widely.

[0306] The flow of the specific process in Example 1 will be described using FIG. 11.

[0307] Step 1:

[0308] The user saves the captured moving image information in the terminal and transmits it to the server using the wireless communication function. In this step, the terminal receives the moving image file as input and sends the data to the server via the wireless communication channel. The output in this process is the transmitted moving image data. Specifically, the terminal uploads the data using Wi-Fi or the mobile phone network.

[0309] Step 2:

[0310] The server receives the moving image information received from the user as input and first saves it in storage. In parallel, it analyzes and extracts the meta information of the data (e.g., shooting date and time, location information, etc.). In this step, the received data and its meta information are output. Specifically, a Python script performs data reception and initial analysis.

[0311] Step 3:

[0312] Based on the saved moving image information, the server detects scene transitions using automated analysis techniques. Here, the moving image data is used as input, and a scene detection algorithm is applied. The output is the detected scene boundary position. Specifically, a computer vision library performs the analysis.

[0313] Step 4:

[0314] The server divides the video information into multiple segments based on the detected scene divisions. The input consists of division position information and video data, while the output is the divided video segments. Video editing tools such as FFmpeg are used in this process.

[0315] Step 5:

[0316] The server applies transition effects to the divided video segments according to the visual style selected by the user. The segment and visual style settings are input, and the segments with added transitions are output. Specifically, a script is executed to add the transition effects.

[0317] Step 6:

[0318] The server applies visual effects to the video and inserts text information and visual elements as needed. The input is the segment information to which the effects should be applied, and the output is the decorated video segment. This includes the generation of text overlays.

[0319] Step 7:

[0320] The server combines the edited video footage and generates it as a single file. Each segment with applied visual effects is the input, and the complete edited video is the output. This is where the video editing program runs.

[0321] Step 8:

[0322] The server sends the generated, edited video to the terminal and provides a preview. The terminal receives the data and displays the preview video to the user. The display of the preview on the user's terminal is considered output.

[0323] Step 9:

[0324] The user reviews the preview and requests additional edits or adjustments if necessary. The input in this process is user feedback, and the output is correction instructions. The request is sent to the server.

[0325] Step 10:

[0326] The server publishes the video and images via the communication network after receiving final approval from the user. The input is the file information configured for publication, and the output is the published video and images along with identification information. After publication, the user receives the identification information.

[0327] (Application Example 1)

[0328] 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."

[0329] Traditional video editing systems have presented challenges for general users, requiring advanced expertise and effort to edit and publish high-quality videos, making them difficult to use. Furthermore, incorporating specific themes or styles into videos is complex and not easily achieved. Therefore, there is a need for a method that allows users to easily edit videos and generate and publish visually rich content without requiring specialized knowledge.

[0330] 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.

[0331] In this invention, the server includes means for a user to transmit video data via wireless data communication, means for analyzing the transmitted video data using automated technology and identifying scene changes, means for dividing the video data into multiple parts based on the identified scenes, means for automatically applying effective video transformations between the divided parts, means for applying visual effects and inserting text information and visual elements, means for presenting the edited video with subject-related visual effects and music added, and means for disseminating the edited video data on an online platform. This makes it possible for users to easily edit videos and quickly generate and share attractive and informational content.

[0332] A "user" is defined as the entity that operates the system to edit and publish video data.

[0333] "Video data" refers to a collection of information in video format that has been filmed or collected by the user.

[0334] "Wireless data communication" is a communication technology that transmits video data to a server without using cables or other means.

[0335] "Automation technology" refers to a series of techniques that perform processing mechanically and electronically, rather than manually.

[0336] "Analysis" refers to the technical process of breaking down data, examining its contents in detail, and finding meaning in it.

[0337] "Identifying scene transitions" refers to the function of recognizing different scenes in video data.

[0338] "Visual effects" refer to digital processing techniques used to add visual impact to images.

[0339] "Textual information and visual elements" refers to text and graphics used to convey information by overlaying them onto video.

[0340] "Edited video" refers to video data that has been processed with visual effects and transformations to make it suitable for public release.

[0341] An "online platform" is a foundational system for providing services to users over the internet.

[0342] "Diffusion" refers to the act of widely disseminating information or data through online platforms.

[0343] This system is implemented by having users transmit video data captured using devices such as smartphones and computers to a server via wireless data communication technology (e.g., Wi-Fi, 4G / 5G communication). The server first uses video processing libraries such as FFmpeg to analyze the received video data and employs technology to identify different scenes. This identification divides the video into multiple scenes, preparing it for the application of visual effects and editing. In particular, it uses APIs such as OpenCV and Adobe Premiere Pro to automatically apply specific visual effects and music to each scene based on the selected theme.

[0344] The server then displays a preview of the edited video to the user via the internet. The preview function is implemented using a mobile application framework such as React Native, allowing users to check the edited results on their smartphones and send feedback or requests as needed. The final video is shared on online platforms via the YouTube API, Facebook API, etc., based on the user's instructions.

[0345] As a concrete example, if a user films a family trip and wants to edit it using this system to create a video with the theme "Summer Vacation," the server will apply wave sounds and a bright filter, and then present the edited result to the user. This operation is instructed to the generating AI model using the following prompt: "We have received video data of your family trip. Please edit it with a bright and cheerful theme and add warm music to scenes that highlight the family's smiles."

[0346] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0347] Step 1:

[0348] The user prepares video data captured using their device. Next, they transmit this video data to the server using wireless data communication. In this process, the input is the raw video data captured by the user, and the output is the video data received by the server. A notification is displayed on the user's device to confirm that the transmission is complete.

[0349] Step 2:

[0350] The server saves the received video data to storage and simultaneously begins analyzing the data using the FFmpeg library. The input here is the video data stored on the server, and the output is the analyzed metadata and the scene change points in the video. In this step, data processing is performed to identify different scenes within the video and to pinpoint the change points.

[0351] Step 3:

[0352] The server divides the video data into multiple parts based on scenes identified using OpenCV. The input consists of analyzed metadata and scene transition points, and the output is video data for each divided scene. The server then prepares this divided video data for effect application in the next step.

[0353] Step 4:

[0354] The server uses the Adobe Premiere Pro API to automatically apply effective transitions and visual effects between segmented video sections based on the user's chosen theme. The inputs are the video data for each segmented scene and the user-selected theme, while the output is the video data after the effects have been applied. Additionally, music corresponding to the selected theme is added as background music.

[0355] Step 5:

[0356] The server displays a preview of the edited video on the terminal via the network. The input is the video data after the effects have been applied, and the output is a video preview presented visually to the user. The user can check the results on their smartphone and submit correction requests if necessary.

[0357] Step 6:

[0358] Once the user is satisfied with the final video edit, the server uses the YouTube API and Facebook API to prepare the finished video for publication on online platforms. The input is the edited video data approved by the user, and the output is the video data ready for publication along with an accessible link. The user then confirms that the video will be shared appropriately.

[0359] 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.

[0360] This invention combines an emotion engine with a system that allows users to effectively edit and publish video data. This system analyzes user emotions to provide a deeper user experience.

[0361] First, the user uploads video data captured using their device to the system. The video data is transmitted to the server via wireless data communication and stored there. Next, the server analyzes the video data and automatically identifies scene changes. After that, the video data is divided into multiple parts, and appropriate video conversion is applied between the divided parts.

[0362] The system incorporates an emotion engine that recognizes emotions from the user's facial expressions and voice. For example, while a user is editing a video, the system monitors their emotional state in real time through the camera and microphone. Based on this information, the server adjusts the visual effects and background music in the video to match the recognized emotions.

[0363] For example, if the user has a happy expression, the system can apply a bright tone filter and insert cheerful music. On the other hand, if an emotional expression is detected, a soft transition and calm music will be applied to the video.

[0364] The emotion engine also automatically selects the appropriate editing style based on the user's emotions, ensuring overall harmony in the video. This automated editing process allows users to create high-quality, emotionally resonant videos without requiring any special editing skills.

[0365] Finally, the edited video is previewed on the server and sent to the user's device for review. Additional editing and adjustments are made as needed to ensure a satisfactory result before the video is published over the network. At this stage, the emotion engine automatically adjusts publication settings based on the user's emotional state, providing an optimal viewing experience. This allows users to share more personalized video content.

[0366] The following describes the processing flow.

[0367] Step 1:

[0368] The user selects the video data they want to edit using their device and sends it to the server via wireless data communication. The video files are transferred using a secure protocol.

[0369] Step 2:

[0370] The server stores the received video data in a database and analyzes and records metadata (e.g., resolution, frame rate, file size, etc.).

[0371] Step 3:

[0372] The camera and microphone on the user's device are activated, capturing the user's facial expressions and voice in real time. This information is sent to the emotion engine, which analyzes the user's emotional state.

[0373] Step 4:

[0374] The server begins analyzing the video data and identifies scene changes. Based on this, it divides the video into multiple clips.

[0375] Step 5:

[0376] Once the emotion engine recognizes the user's emotions, the server automatically selects a video style based on those emotions and applies appropriate transitions and effects to each clip. For example, a cheerful emotion might be accompanied by a bright filter and cheerful music.

[0377] Step 6:

[0378] The server inserts visual effects, text information, and visual elements to generate edited video data. This process results in a customized video that matches the emotions being conveyed.

[0379] Step 7:

[0380] A preview of the edited video data is generated and sent to the device in real time. The user can play the preview and check the content.

[0381] Step 8:

[0382] If the user deems it necessary, they can provide feedback to the server to make additional adjustments. For example, they can fine-tune the volume or change to different music.

[0383] Step 9:

[0384] Once the user approves the edited video, the server executes the process of publishing it to the network. At this point, the emotion engine applies publishing settings based on the user's emotions to create the optimal viewing experience.

[0385] Step 10:

[0386] The URL and identification information of the published video are provided to the user, and they are guided on how to easily share it with other users. This entire process allows users to easily create and distribute emotionally resonant video content.

[0387] (Example 2)

[0388] 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".

[0389] Users face the challenge of needing significant time and technical skills to effectively edit video data and provide personalized visual experiences. Furthermore, editing videos to reflect emotional states requires specialized knowledge, making it difficult for the average user. Therefore, there is a need to provide automated, emotion-based video editing methods to simplify the editing process.

[0390] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0391] In this invention, the server includes means for the user to transmit video data via wireless data communication, means for analyzing the transmitted video data using automated technology and identifying scene changes, and means for analyzing the user's emotions using facial recognition technology and adjusting visual effects. This makes it possible for users to quickly create high-quality videos that reflect their emotions and provide a personalized video experience without requiring complex editing skills.

[0392] A "user" is defined as the entity that operates the system and transmits and edits video data.

[0393] "Video data" refers to visual information such as videos and images taken by the user, and is data that is subject to editing.

[0394] "Wireless data communication" is a communication method that transmits and receives data using radio waves or other means, without using physical connections such as cables.

[0395] "Automation technology" refers to technologies that enable machines and software to automatically perform tasks that were previously done manually.

[0396] "Scene transitions" refer to changes in different scenes or shots within a video, and are elements that constitute the visual flow.

[0397] "Facial expression recognition technology" is a technology that analyzes a user's facial expressions through sensors and cameras to identify their emotional state.

[0398] "Visual effects" are effects used to add visual changes and embellishments to a video, thereby adjusting its impression and atmosphere.

[0399] "Editing style" refers to the techniques and forms of expression that reflect a consistent design or theme in video editing.

[0400] "Emotional information" refers to data that represents the user's emotional state and is used to adjust visual effects and sound in video editing.

[0401] A "video publishing device" is a device or platform for displaying and playing edited video so that users or others can view it.

[0402] This system edits user-recorded video data, incorporating emotional elements to provide a personalized visual experience. Users record video data using a device and upload it to a server via wireless data communication. The device can be a standard smartphone or tablet.

[0403] The server analyzes the received video data using automated technology to identify scene changes. Image analysis software can be used for this process. Additionally, video editing software is used to divide the video data into multiple segments based on the identified scenes, and effective video transformations are automatically applied between the divided segments.

[0404] To incorporate emotional elements, the server uses facial recognition technology to analyze the user's facial expressions and voice to recognize their emotions. For example, it analyzes data acquired in real time through the camera and microphone and adjusts visual effects and background music according to the user's emotions. If the emotion indicates "happiness," a bright filter can be applied to the video and cheerful music can be inserted.

[0405] Furthermore, based on emotional information, the server automatically selects an editing style for the entire video, providing a consistent visual experience. This allows users to generate high-quality videos without requiring any special editing skills.

[0406] Finally, the edited video is generated on the server and sent to the user's device as a preview. The user can review this preview and make additional adjustments as needed. The final, adjusted video can be published over the network, and the emotion engine automatically adjusts the publication settings based on the user's emotions.

[0407] An example of a prompt to a generative AI model is, "Please suggest an automated video editing method based on the user's emotions." This prompt will lead the generative AI model to derive a more sophisticated video editing method.

[0408] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0409] Step 1:

[0410] The user collects video data captured using their device and uploads it to the system. A video data file is provided as input. This data is transmitted to the server using wireless data communication. The output is the video data stored on the server.

[0411] Step 2:

[0412] The server analyzes the received video data. Uploaded video data is used as input. The server uses image analysis software to detect scene changes within the data. This provides information about the points where scenes change as output.

[0413] Step 3:

[0414] The server divides the video data into multiple segments based on the identified switching points. The inputs are the switching point information obtained in step 2 and the original video data. The output is the divided segment data.

[0415] Step 4:

[0416] The server automatically applies effective video transformations between the divided segments. The input is the divided segment data from step 3, and specific video editing software is used. The output is video data with transitions and effects applied.

[0417] Step 5:

[0418] The user provides their facial expressions and voice to the system via camera and microphone. The input is the user's facial and voice data collected in real time. The server analyzes this data using an emotion recognition engine to recognize the user's emotional state. The output is the detected emotion data.

[0419] Step 6:

[0420] The server adjusts the visual effects and background music in the video based on the emotion data. The input consists of the emotion data obtained in step 5 and the edited video data. As a result, the output video reflects the user's emotions with appropriate visual effects and music.

[0421] Step 7:

[0422] The server automatically selects the overall editing style for the video, creating a harmonious and cohesive image. The input is video data adjusted according to emotion; the output is the finished video work.

[0423] Step 8:

[0424] The server sends the completed video to the terminal for preview. The user can then make further adjustments and edits based on this data. The input is the completed video data, and the output, based on the user's evaluation, is the video with final adjustments made.

[0425] Step 9:

[0426] The server publishes the final video over the network and automatically configures the appropriate viewing settings. The input is the video data adjusted in step 8, and the emotion engine handles the publication settings. The output is the video work published online.

[0427] (Application Example 2)

[0428] 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 as the "terminal".

[0429] With the advancement of information and communication technology, many users are now routinely shooting and sharing videos. However, most users lack advanced editing skills, making it difficult to efficiently create videos that reflect their emotions and experiences. Furthermore, simple visual effects alone are insufficient to create a sufficient emotional impact on viewers. In particular, there is a need for technology that can easily transform videos into visually rich and emotionally resonant content.

[0430] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0431] In this invention, the server includes means for identifying the user's emotions using emotion analysis technology, means for analyzing transmitted video data using automation technology and identifying scene changes, and means for dividing the video data into multiple parts based on the identified scenes and the user's emotions, and automatically applying emotion-appropriate visual and audio effects between the divided parts. As a result, users can easily generate and share sophisticated and visually appealing video content that reflects their own emotions without having to acquire editing skills.

[0432] "User" refers to an individual who uses the system to capture video data and edits and shares that data.

[0433] "Wireless data communication" refers to a method of communication that transmits data without using wired connections such as cables.

[0434] "Emotion analysis technology" refers to technology that identifies a user's emotions from their facial expressions and voice.

[0435] "Automated technology" refers to technology that automatically performs analysis and processing of video data without human intervention.

[0436] "Means for identifying scene transitions" refers to a process for detecting transition points between different scenes within video data.

[0437] "Identified scenes" refer to different scenes or episodes that have been recognized and distinguished by the system.

[0438] "Visual and audio effects" refer to embellishments such as filters, music, and transition effects applied to video content to enrich the viewing experience.

[0439] A "video distribution device" refers to a display device used to present edited video data to users or other viewers.

[0440] The system for implementing this invention has the function of allowing users to emotionally edit and share their own videos. The system consists of an application that runs on a smartphone or smart glasses and a cloud-based server that processes the data.

[0441] The user's smart device captures facial expressions and voice simultaneously with video recording using its camera and microphone. This data is first uploaded to a server in the cloud. The server uses emotion analysis tools such as Google Cloud Vision API and Amazon Rekognition to analyze the user's emotions in real time. Based on the analysis results, appropriate visual effects are applied to the video using the OpenCV library, and background music is inserted using the Spotify API, etc.

[0442] The server further uses automated technology to identify scene transitions in the video data, divides it into multiple video segments based on the user's emotions and the identified scenes, and applies effects to each segment. As a result, users can create video content that reflects their own emotions, even without specialized skills.

[0443] As a concrete example, imagine a video of a family picnic. In scenes where children are happily playing, the app detects smiles and automatically applies a bright filter and cheerful music to the scene. This process happens in real time, allowing users to quickly share visually rich and emotionally impactful videos after shooting.

[0444] An example of a prompt message would be, "We are filming children having fun; please add a bright filter and upbeat music to the moments when they smile." This allows users to perform emotionally conscious editing simply by entering a simple prompt from their device.

[0445] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0446] Step 1:

[0447] The device collects video and audio data using its camera and microphone when the user takes a video. The input here is physical video and audio, and the output is digital video and audio data. The device uploads this data to a server in the cloud via wireless data communication.

[0448] Step 2:

[0449] The server receives uploaded video and audio data and analyzes the user's emotions using sentiment analysis tools such as the Google Cloud Vision API and Amazon Rekognition. The input is video and audio data, and the output is information about the user's emotions. Through this process, the server identifies the emotions the user expresses during filming in real time.

[0450] Step 3:

[0451] The server uses automated technology to analyze and identify scene changes using the transmitted video data. The input is video data, and the output is the transition point for each scene. This allows the server to divide the video into individual scenes.

[0452] Step 4:

[0453] The server uses the OpenCV library to apply appropriate visual effects to each portion of the video based on the identified scenes and the analyzed user sentiment information. The input consists of the divided video segments and sentiment information, and the output is the video data with the applied visual effects.

[0454] Step 5:

[0455] The server inserts background music and sound effects into video data using the Spotify API and other tools based on the user's emotions. The input is emotional information and the target video data, and the output is video data with added sound effects. This process ensures that the final video visually and aurally matches the emotions.

[0456] Step 6:

[0457] The server sends the edited video data to the user's terminal and provides a preview. This allows the user to review the automatically edited video and make additional edits as needed. The input is the final edited video data, and the output is the user's visual review process.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] [Third Embodiment]

[0462] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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).

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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".

[0474] This invention provides an automated system that enables users to easily edit and effectively publish video data. The program processing of this system is described below in natural language.

[0475] First, the user uses their device to transmit captured video data to the system using wireless data communication. The transmitted data is received by the server, stored in storage, and at the same time, metadata about the data is extracted.

[0476] Upon receiving the video data, the server begins analyzing it using automated technology. This analysis identifies scene transitions within the video and divides it into multiple sections to provide a visually pleasing experience. Next, video transformations are automatically applied between each of the divided scenes, according to the user's selected style.

[0477] Furthermore, visual effects are applied to the video data, and text information and visual elements are inserted as needed. This results in videos that are visually engaging and effectively convey information.

[0478] After editing is complete, the server generates the edited video and provides a preview to the terminal via the network. The user can review the preview and request additional edits or adjustments if necessary. Once the user is satisfied with the final video, they can select the video's publication settings and give final instructions for release.

[0479] Upon receiving final instructions for publication, the server publishes the video on the network and provides users with identification information for access. This system enables users with limited expertise in video editing to easily and quickly create and widely distribute high-quality video content.

[0480] As a concrete example, if a user uploads footage they shot during a trip to this system and selects the cinematic style, the system will divide the footage into beautiful night scenes, apply appropriate transitions and visual effects, and then allow the user to review the finished video. This automated process significantly simplifies the video editing and publishing process.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] When a user selects the video data they wish to edit using their device and presses the upload button, the video data is sent to the server using wireless data communication technology.

[0484] Step 2:

[0485] The server stores the received video data in storage and extracts basic video information (e.g., length, resolution, frame rate) as metadata.

[0486] Step 3:

[0487] The server uses metadata and automated technology to analyze scenes within the video and detect scene transitions. This allows it to split the video into multiple clips at appropriate points.

[0488] Step 4:

[0489] The user selects their preferred editing style from the options displayed on the device's interface. This information is sent to the server and used for the editing process.

[0490] Step 5:

[0491] The server applies video transformations to each clip based on the selected editing style and automatically inserts transitions. Simultaneously, it applies appropriate visual effects to each clip and inserts text and visual elements as needed.

[0492] Step 6:

[0493] Once editing is complete, the server generates a preview of the video data and sends a link or stream data to the terminal that allows playback of the preview. This preview allows the user to check the results of the editing.

[0494] Step 7:

[0495] The user can view a preview on their device and request additional corrections or adjustments from the server as needed. The server then re-edits the video based on this feedback and provides an updated preview again.

[0496] Step 8:

[0497] Once the user is satisfied with the final content, they select the video's publication settings and send the final publication instructions to the server.

[0498] Step 9:

[0499] Based on the final instructions, the server uploads the edited video to the network and makes it public. It then provides users with identification information and a URL for the published video, allowing them to share it with others.

[0500] (Example 1)

[0501] 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."

[0502] Conventional video editing systems often require a high level of expertise and effort during the video editing process, placing a significant burden on users. Furthermore, the lack of systems that enable high-quality editing with simple operation makes it difficult for many users to achieve satisfactory video editing results.

[0503] 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.

[0504] In this invention, the server includes a device for transmitting video information via wireless communication, a device for processing scene transitions using automated analysis techniques, and a device for automatically applying effective video transformations between divided sections. This makes it possible for users to easily perform high-quality and effective video editing without requiring specialized knowledge.

[0505] "Motion image information" refers to video data that is recorded in sequence from multiple images and played back visually as movement.

[0506] "Wireless communication means" refers to technologies that transmit and receive data using radio waves or other means without the need for physical connections such as cables.

[0507] The term "device" refers to a machine or system designed to perform a specific function or purpose.

[0508] "Automated analytical technology" refers to technologies that use artificial intelligence, machine learning, and other techniques to analyze and interpret data without human intervention.

[0509] "Scene change" refers to the process by which video footage changes to different content, composition, or viewpoint.

[0510] "Division" refers to each part when the whole is divided into multiple parts.

[0511] "Video conversion" refers to the process of changing or adding visual attributes to video information.

[0512] "Visual style" refers to a specific design or method of expression selected as a display effect for moving images.

[0513] A "communication network" refers to a network system designed for data communication.

[0514] "Identification information" refers to information used to distinguish and identify a specific object from other things.

[0515] This system is an automated system that enables users to easily edit and effectively publish video and image information they have captured using wireless communication.

[0516] First, the user sends video information captured using their device to the system. The device then uses wireless communication technology to send the video information to the server. Common wireless communication protocols are used in this process.

[0517] Next, the server analyzes the received video information. Automated analysis techniques are used here. Specifically, computer vision algorithms and machine learning models are employed to detect scene changes within the video. Existing software libraries such as Python and FFmpeg are utilized for the analysis.

[0518] The received video information is divided into multiple sections based on detected scene changes, and an effective video transformation is applied to each section. The server automatically applies the transformation based on the visual style selected by the user. Users can also select a cinematic style, and transition and visual effects are added to the video based on that selection.

[0519] Furthermore, the server applies visual effects and inserts text information and visual elements as needed. For example, in the case of travel videos, the names and dates of the visited locations will be displayed within the video. The server uses a generative AI model to automate this process.

[0520] After editing is complete, the server provides a preview of the edited video to the user's device. The user can review the preview on their device and submit requests for any further editing or adjustments needed.

[0521] For example, if a user wants to edit footage they shot during a trip in a cinematic style, the system will automatically divide the footage into scenes of beautiful nightscapes and apply appropriate transitions and visual effects. An example of a prompt using the generative AI model would be, "I want to add simple and easy-to-understand transitions to the footage from my trip and display the names of the tourist spots as an overlay."

[0522] Ultimately, the server publishes the edited video information over the network via the communication network and provides users with accessible identification information. This allows users to easily edit and widely distribute high-quality video content, even without specialized knowledge.

[0523] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0524] Step 1:

[0525] The user saves captured video information to their device and sends it to the server using wireless communication. In this step, the device receives the video file as input and sends the data to the server via a wireless communication channel. The output of this process is the transmitted video data. Specifically, the device uploads the data using Wi-Fi or a cellular network.

[0526] Step 2:

[0527] The server receives video information from the user as input and first stores it in storage. Simultaneously, it analyzes and extracts metadata from the data (e.g., date and time of capture, location information, etc.). In this step, the received data and its metadata are output. Specifically, a Python script performs data reception and initial analysis.

[0528] Step 3:

[0529] The server uses automated analysis techniques to detect scene changes based on stored video data. Here, video data is used as input, and a scene detection algorithm is applied. The output is the detected scene boundary. Specifically, a computer vision library performs the analysis.

[0530] Step 4:

[0531] The server divides the video information into multiple segments based on the detected scene divisions. The input consists of division position information and video data, while the output is the divided video segments. Video editing tools such as FFmpeg are used in this process.

[0532] Step 5:

[0533] The server applies transition effects to the divided video segments according to the visual style selected by the user. The segment and visual style settings are input, and the segments with added transitions are output. Specifically, a script is executed to add the transition effects.

[0534] Step 6:

[0535] The server applies visual effects to the video and inserts text information and visual elements as needed. The input is the segment information to which the effects should be applied, and the output is the decorated video segment. This includes the generation of text overlays.

[0536] Step 7:

[0537] The server combines the edited video footage and generates it as a single file. Each segment with applied visual effects is the input, and the complete edited video is the output. This is where the video editing program runs.

[0538] Step 8:

[0539] The server sends the generated, edited video to the terminal and provides a preview. The terminal receives the data and displays the preview video to the user. The display of the preview on the user's terminal is considered output.

[0540] Step 9:

[0541] The user reviews the preview and requests additional edits or adjustments if necessary. The input in this process is user feedback, and the output is correction instructions. The request is sent to the server.

[0542] Step 10:

[0543] The server publishes the video and images via the communication network after receiving final approval from the user. The input is the file information configured for publication, and the output is the published video and images along with identification information. After publication, the user receives the identification information.

[0544] (Application Example 1)

[0545] 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."

[0546] Traditional video editing systems have presented challenges for general users, requiring advanced expertise and effort to edit and publish high-quality videos, making them difficult to use. Furthermore, incorporating specific themes or styles into videos is complex and not easily achieved. Therefore, there is a need for a method that allows users to easily edit videos and generate and publish visually rich content without requiring specialized knowledge.

[0547] 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.

[0548] In this invention, the server includes means for a user to transmit video data via wireless data communication, means for analyzing the transmitted video data using automated technology and identifying scene changes, means for dividing the video data into multiple parts based on the identified scenes, means for automatically applying effective video transformations between the divided parts, means for applying visual effects and inserting text information and visual elements, means for presenting the edited video with subject-related visual effects and music added, and means for disseminating the edited video data on an online platform. This makes it possible for users to easily edit videos and quickly generate and share attractive and informational content.

[0549] A "user" is defined as the entity that operates the system to edit and publish video data.

[0550] "Video data" refers to a collection of information in video format that has been filmed or collected by the user.

[0551] "Wireless data communication" is a communication technology that transmits video data to a server without using cables or other means.

[0552] "Automation technology" refers to a series of techniques that perform processing mechanically and electronically, rather than manually.

[0553] "Analysis" refers to the technical process of breaking down data, examining its contents in detail, and finding meaning in it.

[0554] "Identifying scene transitions" refers to the function of recognizing different scenes in video data.

[0555] "Visual effects" refer to digital processing techniques used to add visual impact to images.

[0556] "Textual information and visual elements" refers to text and graphics used to convey information by overlaying them onto video.

[0557] "Edited video" refers to video data that has been processed with visual effects and transformations to make it suitable for public release.

[0558] An "online platform" is a foundational system for providing services to users over the internet.

[0559] "Diffusion" refers to the act of widely disseminating information or data through online platforms.

[0560] This system is implemented by having users transmit video data captured using devices such as smartphones and computers to a server via wireless data communication technology (e.g., Wi-Fi, 4G / 5G communication). The server first uses video processing libraries such as FFmpeg to analyze the received video data and employs technology to identify different scenes. This identification divides the video into multiple scenes, preparing it for the application of visual effects and editing. In particular, it uses APIs such as OpenCV and Adobe Premiere Pro to automatically apply specific visual effects and music to each scene based on the selected theme.

[0561] The server then displays a preview of the edited video to the user via the internet. The preview function is implemented using a mobile application framework such as React Native, allowing users to check the edited results on their smartphones and send feedback or requests as needed. The final video is shared on online platforms via the YouTube API, Facebook API, etc., based on the user's instructions.

[0562] As a concrete example, if a user films a family trip and wants to edit it using this system to create a video with the theme "Summer Vacation," the server will apply wave sounds and a bright filter, and then present the edited result to the user. This operation is instructed to the generating AI model using the following prompt: "We have received video data of your family trip. Please edit it with a bright and cheerful theme and add warm music to scenes that highlight the family's smiles."

[0563] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0564] Step 1:

[0565] The user prepares video data captured using their device. Next, they transmit this video data to the server using wireless data communication. In this process, the input is the raw video data captured by the user, and the output is the video data received by the server. A notification is displayed on the user's device to confirm that the transmission is complete.

[0566] Step 2:

[0567] The server saves the received video data to storage and simultaneously begins analyzing the data using the FFmpeg library. The input here is the video data stored on the server, and the output is the analyzed metadata and the scene change points in the video. In this step, data processing is performed to identify different scenes within the video and to pinpoint the change points.

[0568] Step 3:

[0569] The server divides the video data into multiple parts based on scenes identified using OpenCV. The input consists of analyzed metadata and scene transition points, and the output is video data for each divided scene. The server then prepares this divided video data for effect application in the next step.

[0570] Step 4:

[0571] The server uses the Adobe Premiere Pro API to automatically apply effective transitions and visual effects between segmented video sections based on the user's chosen theme. The inputs are the video data for each segmented scene and the user-selected theme, while the output is the video data after the effects have been applied. Additionally, music corresponding to the selected theme is added as background music.

[0572] Step 5:

[0573] The server displays a preview of the edited video on the terminal via the network. The input is the video data after the effects have been applied, and the output is a video preview presented visually to the user. The user can check the results on their smartphone and submit correction requests if necessary.

[0574] Step 6:

[0575] Once the user is satisfied with the final video edit, the server uses the YouTube API and Facebook API to prepare the finished video for publication on online platforms. The input is the edited video data approved by the user, and the output is the video data ready for publication along with an accessible link. The user then confirms that the video will be shared appropriately.

[0576] 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.

[0577] This invention combines an emotion engine with a system that allows users to effectively edit and publish video data. This system analyzes user emotions to provide a deeper user experience.

[0578] First, the user uploads video data captured using their device to the system. The video data is transmitted to the server via wireless data communication and stored there. Next, the server analyzes the video data and automatically identifies scene changes. After that, the video data is divided into multiple parts, and appropriate video conversion is applied between the divided parts.

[0579] The system incorporates an emotion engine that recognizes emotions from the user's facial expressions and voice. For example, while a user is editing a video, the system monitors their emotional state in real time through the camera and microphone. Based on this information, the server adjusts the visual effects and background music in the video to match the recognized emotions.

[0580] For example, if the user has a happy expression, the system can apply a bright tone filter and insert cheerful music. On the other hand, if an emotional expression is detected, a soft transition and calm music will be applied to the video.

[0581] The emotion engine also automatically selects the appropriate editing style based on the user's emotions, ensuring overall harmony in the video. This automated editing process allows users to create high-quality, emotionally resonant videos without requiring any special editing skills.

[0582] Finally, the edited video is previewed on the server and sent to the user's device for review. Additional editing and adjustments are made as needed to ensure a satisfactory result before the video is published over the network. At this stage, the emotion engine automatically adjusts publication settings based on the user's emotional state, providing an optimal viewing experience. This allows users to share more personalized video content.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] The user selects the video data they want to edit using their device and sends it to the server via wireless data communication. The video files are transferred using a secure protocol.

[0586] Step 2:

[0587] The server stores the received video data in a database and analyzes and records metadata (e.g., resolution, frame rate, file size, etc.).

[0588] Step 3:

[0589] The camera and microphone on the user's device are activated, capturing the user's facial expressions and voice in real time. This information is sent to the emotion engine, which analyzes the user's emotional state.

[0590] Step 4:

[0591] The server begins analyzing the video data and identifies scene changes. Based on this, it divides the video into multiple clips.

[0592] Step 5:

[0593] Once the emotion engine recognizes the user's emotions, the server automatically selects a video style based on those emotions and applies appropriate transitions and effects to each clip. For example, a cheerful emotion might be accompanied by a bright filter and cheerful music.

[0594] Step 6:

[0595] The server inserts visual effects, text information, and visual elements to generate edited video data. This process results in a customized video that matches the emotions being conveyed.

[0596] Step 7:

[0597] A preview of the edited video data is generated and sent to the device in real time. The user can play the preview and check the content.

[0598] Step 8:

[0599] If the user deems it necessary, they can provide feedback to the server to make additional adjustments. For example, they can fine-tune the volume or change to different music.

[0600] Step 9:

[0601] Once the user approves the edited video, the server executes the process of publishing it to the network. At this point, the emotion engine applies publishing settings based on the user's emotions to create the optimal viewing experience.

[0602] Step 10:

[0603] The URL and identification information of the published video are provided to the user, and they are guided on how to easily share it with other users. This entire process allows users to easily create and distribute emotionally resonant video content.

[0604] (Example 2)

[0605] 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."

[0606] Users face the challenge of needing significant time and technical skills to effectively edit video data and provide personalized visual experiences. Furthermore, editing videos to reflect emotional states requires specialized knowledge, making it difficult for the average user. Therefore, there is a need to provide automated, emotion-based video editing methods to simplify the editing process.

[0607] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0608] In this invention, the server includes means for the user to transmit video data via wireless data communication, means for analyzing the transmitted video data using automated technology and identifying scene changes, and means for analyzing the user's emotions using facial recognition technology and adjusting visual effects. This makes it possible for users to quickly create high-quality videos that reflect their emotions and provide a personalized video experience without requiring complex editing skills.

[0609] A "user" is defined as the entity that operates the system and transmits and edits video data.

[0610] "Video data" refers to visual information such as videos and images taken by the user, and is data that is subject to editing.

[0611] "Wireless data communication" is a communication method that transmits and receives data using radio waves or other means, without using physical connections such as cables.

[0612] "Automation technology" refers to technologies that enable machines and software to automatically perform tasks that were previously done manually.

[0613] "Scene transitions" refer to changes in different scenes or shots within a video, and are elements that constitute the visual flow.

[0614] "Facial expression recognition technology" is a technology that analyzes a user's facial expressions through sensors and cameras to identify their emotional state.

[0615] "Visual effects" are effects used to add visual changes and embellishments to a video, thereby adjusting its impression and atmosphere.

[0616] "Editing style" refers to the techniques and forms of expression that reflect a consistent design or theme in video editing.

[0617] "Emotional information" refers to data that represents the user's emotional state and is used to adjust visual effects and sound in video editing.

[0618] A "video publishing device" is a device or platform for displaying and playing edited video so that users or others can view it.

[0619] This system edits user-recorded video data, incorporating emotional elements to provide a personalized visual experience. Users record video data using a device and upload it to a server via wireless data communication. The device can be a standard smartphone or tablet.

[0620] The server analyzes the received video data using automated technology to identify scene changes. Image analysis software can be used for this process. Additionally, video editing software is used to divide the video data into multiple segments based on the identified scenes, and effective video transformations are automatically applied between the divided segments.

[0621] To incorporate emotional elements, the server uses facial recognition technology to analyze the user's facial expressions and voice to recognize their emotions. For example, it analyzes data acquired in real time through the camera and microphone and adjusts visual effects and background music according to the user's emotions. If the emotion indicates "happiness," a bright filter can be applied to the video and cheerful music can be inserted.

[0622] Furthermore, based on emotional information, the server automatically selects an editing style for the entire video, providing a consistent visual experience. This allows users to generate high-quality videos without requiring any special editing skills.

[0623] Finally, the edited video is generated on the server and sent to the user's device as a preview. The user can review this preview and make additional adjustments as needed. The final, adjusted video can be published over the network, and the emotion engine automatically adjusts the publication settings based on the user's emotions.

[0624] An example of a prompt to a generative AI model is, "Please suggest an automated video editing method based on the user's emotions." This prompt will lead the generative AI model to derive a more sophisticated video editing method.

[0625] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0626] Step 1:

[0627] The user collects video data captured using their device and uploads it to the system. A video data file is provided as input. This data is transmitted to the server using wireless data communication. The output is the video data stored on the server.

[0628] Step 2:

[0629] The server analyzes the received video data. Uploaded video data is used as input. The server uses image analysis software to detect scene changes within the data. This provides information about the points where scenes change as output.

[0630] Step 3:

[0631] The server divides the video data into multiple segments based on the identified switching points. The inputs are the switching point information obtained in step 2 and the original video data. The output is the divided segment data.

[0632] Step 4:

[0633] The server automatically applies effective video transformations between the divided segments. The input is the divided segment data from step 3, and specific video editing software is used. The output is video data with transitions and effects applied.

[0634] Step 5:

[0635] The user provides their facial expressions and voice to the system via camera and microphone. The input is the user's facial and voice data collected in real time. The server analyzes this data using an emotion recognition engine to recognize the user's emotional state. The output is the detected emotion data.

[0636] Step 6:

[0637] The server adjusts the visual effects and background music in the video based on the emotion data. The input consists of the emotion data obtained in step 5 and the edited video data. As a result, the output video reflects the user's emotions with appropriate visual effects and music.

[0638] Step 7:

[0639] The server automatically selects the overall editing style for the video, creating a harmonious and cohesive image. The input is video data adjusted according to emotion; the output is the finished video work.

[0640] Step 8:

[0641] The server sends the completed video to the terminal for preview. The user can then make further adjustments and edits based on this data. The input is the completed video data, and the output, based on the user's evaluation, is the video with final adjustments made.

[0642] Step 9:

[0643] The server publishes the final video over the network and automatically configures the appropriate viewing settings. The input is the video data adjusted in step 8, and the emotion engine handles the publication settings. The output is the video work published online.

[0644] (Application Example 2)

[0645] 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."

[0646] With the advancement of information and communication technology, many users are now routinely shooting and sharing videos. However, most users lack advanced editing skills, making it difficult to efficiently create videos that reflect their emotions and experiences. Furthermore, simple visual effects alone are insufficient to create a sufficient emotional impact on viewers. In particular, there is a need for technology that can easily transform videos into visually rich and emotionally resonant content.

[0647] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0648] In this invention, the server includes means for identifying the user's emotions using emotion analysis technology, means for analyzing transmitted video data using automation technology and identifying scene changes, and means for dividing the video data into multiple parts based on the identified scenes and the user's emotions, and automatically applying emotion-appropriate visual and audio effects between the divided parts. As a result, users can easily generate and share sophisticated and visually appealing video content that reflects their own emotions without having to acquire editing skills.

[0649] "User" refers to an individual who uses the system to capture video data and edits and shares that data.

[0650] "Wireless data communication" refers to a method of communication that transmits data without using wired connections such as cables.

[0651] "Emotion analysis technology" refers to technology that identifies a user's emotions from their facial expressions and voice.

[0652] "Automated technology" refers to technology that automatically performs analysis and processing of video data without human intervention.

[0653] "Means for identifying scene transitions" refers to a process for detecting transition points between different scenes within video data.

[0654] "Identified scenes" refer to different scenes or episodes that have been recognized and distinguished by the system.

[0655] "Visual and audio effects" refer to embellishments such as filters, music, and transition effects applied to video content to enrich the viewing experience.

[0656] A "video distribution device" refers to a display device used to present edited video data to users or other viewers.

[0657] The system for implementing this invention has the function of allowing users to emotionally edit and share their own videos. The system consists of an application that runs on a smartphone or smart glasses and a cloud-based server that processes the data.

[0658] The user's smart device captures facial expressions and voice simultaneously with video recording using its camera and microphone. This data is first uploaded to a server in the cloud. The server uses emotion analysis tools such as Google Cloud Vision API and Amazon Rekognition to analyze the user's emotions in real time. Based on the analysis results, appropriate visual effects are applied to the video using the OpenCV library, and background music is inserted using the Spotify API, etc.

[0659] The server further uses automated technology to identify scene transitions in the video data, divides it into multiple video segments based on the user's emotions and the identified scenes, and applies effects to each segment. As a result, users can create video content that reflects their own emotions, even without specialized skills.

[0660] As a concrete example, imagine a video of a family picnic. In scenes where children are happily playing, the app detects smiles and automatically applies a bright filter and cheerful music to the scene. This process happens in real time, allowing users to quickly share visually rich and emotionally impactful videos after shooting.

[0661] An example of a prompt message would be, "We are filming children having fun; please add a bright filter and upbeat music to the moments when they smile." This allows users to perform emotionally conscious editing simply by entering a simple prompt from their device.

[0662] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0663] Step 1:

[0664] The device collects video and audio data using its camera and microphone when the user takes a video. The input here is physical video and audio, and the output is digital video and audio data. The device uploads this data to a server in the cloud via wireless data communication.

[0665] Step 2:

[0666] The server receives uploaded video and audio data and analyzes the user's emotions using sentiment analysis tools such as the Google Cloud Vision API and Amazon Rekognition. The input is video and audio data, and the output is information about the user's emotions. Through this process, the server identifies the emotions the user expresses during filming in real time.

[0667] Step 3:

[0668] The server uses automated technology to analyze and identify scene changes using the transmitted video data. The input is video data, and the output is the transition point for each scene. This allows the server to divide the video into individual scenes.

[0669] Step 4:

[0670] The server uses the OpenCV library to apply appropriate visual effects to each portion of the video based on the identified scenes and the analyzed user sentiment information. The input consists of the divided video segments and sentiment information, and the output is the video data with the applied visual effects.

[0671] Step 5:

[0672] The server inserts background music and sound effects into video data using the Spotify API and other tools based on the user's emotions. The input is emotional information and the target video data, and the output is video data with added sound effects. This process ensures that the final video visually and aurally matches the emotions.

[0673] Step 6:

[0674] The server sends the edited video data to the user's terminal and provides a preview. This allows the user to review the automatically edited video and make additional edits as needed. The input is the final edited video data, and the output is the user's visual review process.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] [Fourth Embodiment]

[0679] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0680] 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.

[0681] 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).

[0682] 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.

[0683] 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.

[0684] 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).

[0685] 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.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] 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".

[0692] This invention provides an automated system that enables users to easily edit and effectively publish video data. The program processing of this system is described below in natural language.

[0693] First, the user uses their device to transmit captured video data to the system using wireless data communication. The transmitted data is received by the server, stored in storage, and at the same time, metadata about the data is extracted.

[0694] Upon receiving the video data, the server begins analyzing it using automated technology. This analysis identifies scene transitions within the video and divides it into multiple sections to provide a visually pleasing experience. Next, video transformations are automatically applied between each of the divided scenes, according to the user's selected style.

[0695] Furthermore, visual effects are applied to the video data, and text information and visual elements are inserted as needed. This results in videos that are visually engaging and effectively convey information.

[0696] After editing is complete, the server generates the edited video and provides a preview to the terminal via the network. The user can review the preview and request additional edits or adjustments if necessary. Once the user is satisfied with the final video, they can select the video's publication settings and give final instructions for release.

[0697] Upon receiving final instructions for publication, the server publishes the video on the network and provides users with identification information for access. This system enables users with limited expertise in video editing to easily and quickly create and widely distribute high-quality video content.

[0698] As a concrete example, if a user uploads footage they shot during a trip to this system and selects the cinematic style, the system will divide the footage into beautiful night scenes, apply appropriate transitions and visual effects, and then allow the user to review the finished video. This automated process significantly simplifies the video editing and publishing process.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] When a user selects the video data they wish to edit using their device and presses the upload button, the video data is sent to the server using wireless data communication technology.

[0702] Step 2:

[0703] The server stores the received video data in storage and extracts basic video information (e.g., length, resolution, frame rate) as metadata.

[0704] Step 3:

[0705] The server uses metadata and automated technology to analyze scenes within the video and detect scene transitions. This allows it to split the video into multiple clips at appropriate points.

[0706] Step 4:

[0707] The user selects their preferred editing style from the options displayed on the device's interface. This information is sent to the server and used for the editing process.

[0708] Step 5:

[0709] The server applies video transformations to each clip based on the selected editing style and automatically inserts transitions. Simultaneously, it applies appropriate visual effects to each clip and inserts text and visual elements as needed.

[0710] Step 6:

[0711] Once editing is complete, the server generates a preview of the video data and sends a link or stream data to the terminal that allows playback of the preview. This preview allows the user to check the results of the editing.

[0712] Step 7:

[0713] The user can view a preview on their device and request additional corrections or adjustments from the server as needed. The server then re-edits the video based on this feedback and provides an updated preview again.

[0714] Step 8:

[0715] Once the user is satisfied with the final content, they select the video's publication settings and send the final publication instructions to the server.

[0716] Step 9:

[0717] Based on the final instructions, the server uploads the edited video to the network and makes it public. It then provides users with identification information and a URL for the published video, allowing them to share it with others.

[0718] (Example 1)

[0719] 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".

[0720] Conventional video editing systems often require a high level of expertise and effort during the video editing process, placing a significant burden on users. Furthermore, the lack of systems that enable high-quality editing with simple operation makes it difficult for many users to achieve satisfactory video editing results.

[0721] 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.

[0722] In this invention, the server includes a device for transmitting video information via wireless communication, a device for processing scene transitions using automated analysis techniques, and a device for automatically applying effective video transformations between divided sections. This makes it possible for users to easily perform high-quality and effective video editing without requiring specialized knowledge.

[0723] "Motion image information" refers to video data that is recorded in sequence from multiple images and played back visually as movement.

[0724] "Wireless communication means" refers to technologies that transmit and receive data using radio waves or other means without the need for physical connections such as cables.

[0725] The term "device" refers to a machine or system designed to perform a specific function or purpose.

[0726] "Automated analytical technology" refers to technologies that use artificial intelligence, machine learning, and other techniques to analyze and interpret data without human intervention.

[0727] "Scene change" refers to the process by which video footage changes to different content, composition, or viewpoint.

[0728] "Division" refers to each part when the whole is divided into multiple parts.

[0729] "Video conversion" refers to the process of changing or adding visual attributes to video information.

[0730] "Visual style" refers to a specific design or method of expression selected as a display effect for moving images.

[0731] A "communication network" refers to a network system designed for data communication.

[0732] "Identification information" refers to information used to distinguish and identify a specific object from other things.

[0733] This system is an automated system that enables users to easily edit and effectively publish video and image information they have captured using wireless communication.

[0734] First, the user sends video information captured using their device to the system. The device then uses wireless communication technology to send the video information to the server. Common wireless communication protocols are used in this process.

[0735] Next, the server analyzes the received video information. Automated analysis techniques are used here. Specifically, computer vision algorithms and machine learning models are employed to detect scene changes within the video. Existing software libraries such as Python and FFmpeg are utilized for the analysis.

[0736] The received video information is divided into multiple sections based on detected scene changes, and an effective video transformation is applied to each section. The server automatically applies the transformation based on the visual style selected by the user. Users can also select a cinematic style, and transition and visual effects are added to the video based on that selection.

[0737] Furthermore, the server applies visual effects and inserts text information and visual elements as needed. For example, in the case of travel videos, the names and dates of the visited locations will be displayed within the video. The server uses a generative AI model to automate this process.

[0738] After editing is complete, the server provides a preview of the edited video to the user's device. The user can review the preview on their device and submit requests for any further editing or adjustments needed.

[0739] For example, if a user wants to edit footage they shot during a trip in a cinematic style, the system will automatically divide the footage into scenes of beautiful nightscapes and apply appropriate transitions and visual effects. An example of a prompt using the generative AI model would be, "I want to add simple and easy-to-understand transitions to the footage from my trip and display the names of the tourist spots as an overlay."

[0740] Ultimately, the server publishes the edited video information over the network via the communication network and provides users with accessible identification information. This allows users to easily edit and widely distribute high-quality video content, even without specialized knowledge.

[0741] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0742] Step 1:

[0743] The user saves captured video information to their device and sends it to the server using wireless communication. In this step, the device receives the video file as input and sends the data to the server via a wireless communication channel. The output of this process is the transmitted video data. Specifically, the device uploads the data using Wi-Fi or a cellular network.

[0744] Step 2:

[0745] The server receives video information from the user as input and first stores it in storage. Simultaneously, it analyzes and extracts metadata from the data (e.g., date and time of capture, location information, etc.). In this step, the received data and its metadata are output. Specifically, a Python script performs data reception and initial analysis.

[0746] Step 3:

[0747] The server uses automated analysis techniques to detect scene changes based on stored video data. Here, video data is used as input, and a scene detection algorithm is applied. The output is the detected scene boundary. Specifically, a computer vision library performs the analysis.

[0748] Step 4:

[0749] The server divides the video information into multiple segments based on the detected scene divisions. The input consists of division position information and video data, while the output is the divided video segments. Video editing tools such as FFmpeg are used in this process.

[0750] Step 5:

[0751] The server applies transition effects to the divided video segments according to the visual style selected by the user. The segment and visual style settings are input, and the segments with added transitions are output. Specifically, a script is executed to add the transition effects.

[0752] Step 6:

[0753] The server applies visual effects to the video and inserts text information and visual elements as needed. The input is the segment information to which the effects should be applied, and the output is the decorated video segment. This includes the generation of text overlays.

[0754] Step 7:

[0755] The server combines the edited video footage and generates it as a single file. Each segment with applied visual effects is the input, and the complete edited video is the output. This is where the video editing program runs.

[0756] Step 8:

[0757] The server sends the generated, edited video to the terminal and provides a preview. The terminal receives the data and displays the preview video to the user. The display of the preview on the user's terminal is considered output.

[0758] Step 9:

[0759] The user reviews the preview and requests additional edits or adjustments if necessary. The input in this process is user feedback, and the output is correction instructions. The request is sent to the server.

[0760] Step 10:

[0761] The server publishes the video and images via the communication network after receiving final approval from the user. The input is the file information configured for publication, and the output is the published video and images along with identification information. After publication, the user receives the identification information.

[0762] (Application Example 1)

[0763] 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".

[0764] Traditional video editing systems have presented challenges for general users, requiring advanced expertise and effort to edit and publish high-quality videos, making them difficult to use. Furthermore, incorporating specific themes or styles into videos is complex and not easily achieved. Therefore, there is a need for a method that allows users to easily edit videos and generate and publish visually rich content without requiring specialized knowledge.

[0765] 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.

[0766] In this invention, the server includes means for a user to transmit video data via wireless data communication, means for analyzing the transmitted video data using automated technology and identifying scene changes, means for dividing the video data into multiple parts based on the identified scenes, means for automatically applying effective video transformations between the divided parts, means for applying visual effects and inserting text information and visual elements, means for presenting the edited video with subject-related visual effects and music added, and means for disseminating the edited video data on an online platform. This makes it possible for users to easily edit videos and quickly generate and share attractive and informational content.

[0767] A "user" is defined as the entity that operates the system to edit and publish video data.

[0768] "Video data" refers to a collection of information in video format that has been filmed or collected by the user.

[0769] "Wireless data communication" is a communication technology that transmits video data to a server without using cables or other means.

[0770] "Automation technology" refers to a series of techniques that perform processing mechanically and electronically, rather than manually.

[0771] "Analysis" refers to the technical process of breaking down data, examining its contents in detail, and finding meaning in it.

[0772] "Identifying scene transitions" refers to the function of recognizing different scenes in video data.

[0773] "Visual effects" refer to digital processing techniques used to add visual impact to images.

[0774] "Textual information and visual elements" refers to text and graphics used to convey information by overlaying them onto video.

[0775] "Edited video" refers to video data that has been processed with visual effects and transformations to make it suitable for public release.

[0776] An "online platform" is a foundational system for providing services to users over the internet.

[0777] "Diffusion" refers to the act of widely disseminating information or data through online platforms.

[0778] This system is implemented by having users transmit video data captured using devices such as smartphones and computers to a server via wireless data communication technology (e.g., Wi-Fi, 4G / 5G communication). The server first uses video processing libraries such as FFmpeg to analyze the received video data and employs technology to identify different scenes. This identification divides the video into multiple scenes, preparing it for the application of visual effects and editing. In particular, it uses APIs such as OpenCV and Adobe Premiere Pro to automatically apply specific visual effects and music to each scene based on the selected theme.

[0779] The server then displays a preview of the edited video to the user via the internet. The preview function is implemented using a mobile application framework such as React Native, allowing users to check the edited results on their smartphones and send feedback or requests as needed. The final video is shared on online platforms via the YouTube API, Facebook API, etc., based on the user's instructions.

[0780] As a concrete example, if a user films a family trip and wants to edit it using this system to create a video with the theme "Summer Vacation," the server will apply wave sounds and a bright filter, and then present the edited result to the user. This operation is instructed to the generating AI model using the following prompt: "We have received video data of your family trip. Please edit it with a bright and cheerful theme and add warm music to scenes that highlight the family's smiles."

[0781] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0782] Step 1:

[0783] The user prepares video data captured using their device. Next, they transmit this video data to the server using wireless data communication. In this process, the input is the raw video data captured by the user, and the output is the video data received by the server. A notification is displayed on the user's device to confirm that the transmission is complete.

[0784] Step 2:

[0785] The server saves the received video data to storage and simultaneously begins analyzing the data using the FFmpeg library. The input here is the video data stored on the server, and the output is the analyzed metadata and the scene change points in the video. In this step, data processing is performed to identify different scenes within the video and to pinpoint the change points.

[0786] Step 3:

[0787] The server divides the video data into multiple parts based on scenes identified using OpenCV. The input consists of analyzed metadata and scene transition points, and the output is video data for each divided scene. The server then prepares this divided video data for effect application in the next step.

[0788] Step 4:

[0789] The server uses the Adobe Premiere Pro API to automatically apply effective transitions and visual effects between segmented video sections based on the user's chosen theme. The inputs are the video data for each segmented scene and the user-selected theme, while the output is the video data after the effects have been applied. Additionally, music corresponding to the selected theme is added as background music.

[0790] Step 5:

[0791] The server displays a preview of the edited video on the terminal via the network. The input is the video data after the effects have been applied, and the output is a video preview presented visually to the user. The user can check the results on their smartphone and submit correction requests if necessary.

[0792] Step 6:

[0793] Once the user is satisfied with the final video edit, the server uses the YouTube API and Facebook API to prepare the finished video for publication on online platforms. The input is the edited video data approved by the user, and the output is the video data ready for publication along with an accessible link. The user then confirms that the video will be shared appropriately.

[0794] 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.

[0795] This invention combines an emotion engine with a system that allows users to effectively edit and publish video data. This system analyzes user emotions to provide a deeper user experience.

[0796] First, the user uploads video data captured using their device to the system. The video data is transmitted to the server via wireless data communication and stored there. Next, the server analyzes the video data and automatically identifies scene changes. After that, the video data is divided into multiple parts, and appropriate video conversion is applied between the divided parts.

[0797] The system incorporates an emotion engine that recognizes emotions from the user's facial expressions and voice. For example, while a user is editing a video, the system monitors their emotional state in real time through the camera and microphone. Based on this information, the server adjusts the visual effects and background music in the video to match the recognized emotions.

[0798] For example, if the user has a happy expression, the system can apply a bright tone filter and insert cheerful music. On the other hand, if an emotional expression is detected, a soft transition and calm music will be applied to the video.

[0799] The emotion engine also automatically selects the appropriate editing style based on the user's emotions, ensuring overall harmony in the video. This automated editing process allows users to create high-quality, emotionally resonant videos without requiring any special editing skills.

[0800] Finally, the edited video is previewed on the server and sent to the user's device for review. Additional editing and adjustments are made as needed to ensure a satisfactory result before the video is published over the network. At this stage, the emotion engine automatically adjusts publication settings based on the user's emotional state, providing an optimal viewing experience. This allows users to share more personalized video content.

[0801] The following describes the processing flow.

[0802] Step 1:

[0803] The user selects the video data they want to edit using their device and sends it to the server via wireless data communication. The video files are transferred using a secure protocol.

[0804] Step 2:

[0805] The server stores the received video data in a database and analyzes and records metadata (e.g., resolution, frame rate, file size, etc.).

[0806] Step 3:

[0807] The camera and microphone on the user's device are activated, capturing the user's facial expressions and voice in real time. This information is sent to the emotion engine, which analyzes the user's emotional state.

[0808] Step 4:

[0809] The server begins analyzing the video data and identifies scene changes. Based on this, it divides the video into multiple clips.

[0810] Step 5:

[0811] Once the emotion engine recognizes the user's emotions, the server automatically selects a video style based on those emotions and applies appropriate transitions and effects to each clip. For example, a cheerful emotion might be accompanied by a bright filter and cheerful music.

[0812] Step 6:

[0813] The server inserts visual effects, text information, and visual elements to generate edited video data. This process results in a customized video that matches the emotions being conveyed.

[0814] Step 7:

[0815] A preview of the edited video data is generated and sent to the device in real time. The user can play the preview and check the content.

[0816] Step 8:

[0817] If the user deems it necessary, they can provide feedback to the server to make additional adjustments. For example, they can fine-tune the volume or change to different music.

[0818] Step 9:

[0819] Once the user approves the edited video, the server executes the process of publishing it to the network. At this point, the emotion engine applies publishing settings based on the user's emotions to create the optimal viewing experience.

[0820] Step 10:

[0821] The URL and identification information of the published video are provided to the user, and they are guided on how to easily share it with other users. This entire process allows users to easily create and distribute emotionally resonant video content.

[0822] (Example 2)

[0823] 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".

[0824] Users face the challenge of needing significant time and technical skills to effectively edit video data and provide personalized visual experiences. Furthermore, editing videos to reflect emotional states requires specialized knowledge, making it difficult for the average user. Therefore, there is a need to provide automated, emotion-based video editing methods to simplify the editing process.

[0825] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0826] In this invention, the server includes means for the user to transmit video data via wireless data communication, means for analyzing the transmitted video data using automated technology and identifying scene changes, and means for analyzing the user's emotions using facial recognition technology and adjusting visual effects. This makes it possible for users to quickly create high-quality videos that reflect their emotions and provide a personalized video experience without requiring complex editing skills.

[0827] A "user" is defined as the entity that operates the system and transmits and edits video data.

[0828] "Video data" refers to visual information such as videos and images taken by the user, and is data that is subject to editing.

[0829] "Wireless data communication" is a communication method that transmits and receives data using radio waves or other means, without using physical connections such as cables.

[0830] "Automation technology" refers to technologies that enable machines and software to automatically perform tasks that were previously done manually.

[0831] "Scene transitions" refer to changes in different scenes or shots within a video, and are elements that constitute the visual flow.

[0832] "Facial expression recognition technology" is a technology that analyzes a user's facial expressions through sensors and cameras to identify their emotional state.

[0833] "Visual effects" are effects used to add visual changes and embellishments to a video, thereby adjusting its impression and atmosphere.

[0834] "Editing style" refers to the techniques and forms of expression that reflect a consistent design or theme in video editing.

[0835] "Emotional information" refers to data that represents the user's emotional state and is used to adjust visual effects and sound in video editing.

[0836] A "video publishing device" is a device or platform for displaying and playing edited video so that users or others can view it.

[0837] This system edits user-recorded video data, incorporating emotional elements to provide a personalized visual experience. Users record video data using a device and upload it to a server via wireless data communication. The device can be a standard smartphone or tablet.

[0838] The server analyzes the received video data using automated technology to identify scene changes. Image analysis software can be used for this process. Additionally, video editing software is used to divide the video data into multiple segments based on the identified scenes, and effective video transformations are automatically applied between the divided segments.

[0839] To incorporate emotional elements, the server uses facial recognition technology to analyze the user's facial expressions and voice to recognize their emotions. For example, it analyzes data acquired in real time through the camera and microphone and adjusts visual effects and background music according to the user's emotions. If the emotion indicates "happiness," a bright filter can be applied to the video and cheerful music can be inserted.

[0840] Furthermore, based on emotional information, the server automatically selects an editing style for the entire video, providing a consistent visual experience. This allows users to generate high-quality videos without requiring any special editing skills.

[0841] Finally, the edited video is generated on the server and sent to the user's device as a preview. The user can review this preview and make additional adjustments as needed. The final, adjusted video can be published over the network, and the emotion engine automatically adjusts the publication settings based on the user's emotions.

[0842] An example of a prompt to a generative AI model is, "Please suggest an automated video editing method based on the user's emotions." This prompt will lead the generative AI model to derive a more sophisticated video editing method.

[0843] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0844] Step 1:

[0845] The user collects video data captured using their device and uploads it to the system. A video data file is provided as input. This data is transmitted to the server using wireless data communication. The output is the video data stored on the server.

[0846] Step 2:

[0847] The server analyzes the received video data. Uploaded video data is used as input. The server uses image analysis software to detect scene changes within the data. This provides information about the points where scenes change as output.

[0848] Step 3:

[0849] The server divides the video data into multiple segments based on the identified switching points. The inputs are the switching point information obtained in step 2 and the original video data. The output is the divided segment data.

[0850] Step 4:

[0851] The server automatically applies effective video transformations between the divided segments. The input is the divided segment data from step 3, and specific video editing software is used. The output is video data with transitions and effects applied.

[0852] Step 5:

[0853] The user provides their facial expressions and voice to the system via camera and microphone. The input is the user's facial and voice data collected in real time. The server analyzes this data using an emotion recognition engine to recognize the user's emotional state. The output is the detected emotion data.

[0854] Step 6:

[0855] The server adjusts the visual effects and background music in the video based on the emotion data. The input consists of the emotion data obtained in step 5 and the edited video data. As a result, the output video reflects the user's emotions with appropriate visual effects and music.

[0856] Step 7:

[0857] The server automatically selects the overall editing style for the video, creating a harmonious and cohesive image. The input is video data adjusted according to emotion; the output is the finished video work.

[0858] Step 8:

[0859] The server sends the completed video to the terminal for preview. The user can then make further adjustments and edits based on this data. The input is the completed video data, and the output, based on the user's evaluation, is the video with final adjustments made.

[0860] Step 9:

[0861] The server publishes the final video over the network and automatically configures the appropriate viewing settings. The input is the video data adjusted in step 8, and the emotion engine handles the publication settings. The output is the video work published online.

[0862] (Application Example 2)

[0863] 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".

[0864] With the advancement of information and communication technology, many users are now routinely shooting and sharing videos. However, most users lack advanced editing skills, making it difficult to efficiently create videos that reflect their emotions and experiences. Furthermore, simple visual effects alone are insufficient to create a sufficient emotional impact on viewers. In particular, there is a need for technology that can easily transform videos into visually rich and emotionally resonant content.

[0865] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0866] In this invention, the server includes means for identifying the user's emotions using emotion analysis technology, means for analyzing transmitted video data using automation technology and identifying scene changes, and means for dividing the video data into multiple parts based on the identified scenes and the user's emotions, and automatically applying emotion-appropriate visual and audio effects between the divided parts. As a result, users can easily generate and share sophisticated and visually appealing video content that reflects their own emotions without having to acquire editing skills.

[0867] "User" refers to an individual who uses the system to capture video data and edits and shares that data.

[0868] "Wireless data communication" refers to a method of communication that transmits data without using wired connections such as cables.

[0869] "Emotion analysis technology" refers to technology that identifies a user's emotions from their facial expressions and voice.

[0870] "Automated technology" refers to technology that automatically performs analysis and processing of video data without human intervention.

[0871] "Means for identifying scene transitions" refers to a process for detecting transition points between different scenes within video data.

[0872] "Identified scenes" refer to different scenes or episodes that have been recognized and distinguished by the system.

[0873] "Visual and audio effects" refer to embellishments such as filters, music, and transition effects applied to video content to enrich the viewing experience.

[0874] A "video distribution device" refers to a display device used to present edited video data to users or other viewers.

[0875] The system for implementing this invention has the function of allowing users to emotionally edit and share their own videos. The system consists of an application that runs on a smartphone or smart glasses and a cloud-based server that processes the data.

[0876] The user's smart device captures facial expressions and voice simultaneously with video recording using its camera and microphone. This data is first uploaded to a server in the cloud. The server uses emotion analysis tools such as Google Cloud Vision API and Amazon Rekognition to analyze the user's emotions in real time. Based on the analysis results, appropriate visual effects are applied to the video using the OpenCV library, and background music is inserted using the Spotify API, etc.

[0877] The server further uses automated technology to identify scene transitions in the video data, divides it into multiple video segments based on the user's emotions and the identified scenes, and applies effects to each segment. As a result, users can create video content that reflects their own emotions, even without specialized skills.

[0878] As a concrete example, imagine a video of a family picnic. In scenes where children are happily playing, the app detects smiles and automatically applies a bright filter and cheerful music to the scene. This process happens in real time, allowing users to quickly share visually rich and emotionally impactful videos after shooting.

[0879] An example of a prompt message would be, "We are filming children having fun; please add a bright filter and upbeat music to the moments when they smile." This allows users to perform emotionally conscious editing simply by entering a simple prompt from their device.

[0880] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0881] Step 1:

[0882] The device collects video and audio data using its camera and microphone when the user takes a video. The input here is physical video and audio, and the output is digital video and audio data. The device uploads this data to a server in the cloud via wireless data communication.

[0883] Step 2:

[0884] The server receives uploaded video and audio data and analyzes the user's emotions using sentiment analysis tools such as the Google Cloud Vision API and Amazon Rekognition. The input is video and audio data, and the output is information about the user's emotions. Through this process, the server identifies the emotions the user expresses during filming in real time.

[0885] Step 3:

[0886] The server uses automated technology to analyze and identify scene changes using the transmitted video data. The input is video data, and the output is the transition point for each scene. This allows the server to divide the video into individual scenes.

[0887] Step 4:

[0888] The server uses the OpenCV library to apply appropriate visual effects to each portion of the video based on the identified scenes and the analyzed user sentiment information. The input consists of the divided video segments and sentiment information, and the output is the video data with the applied visual effects.

[0889] Step 5:

[0890] The server inserts background music and sound effects into video data using the Spotify API and other tools based on the user's emotions. The input is emotional information and the target video data, and the output is video data with added sound effects. This process ensures that the final video visually and aurally matches the emotions.

[0891] Step 6:

[0892] The server sends the edited video data to the user's terminal and provides a preview. This allows the user to review the automatically edited video and make additional edits as needed. The input is the final edited video data, and the output is the user's visual review process.

[0893] 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.

[0894] 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.

[0895] 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 robot 414.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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."

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0914] The following is further disclosed regarding the embodiments described above.

[0915] (Claim 1)

[0916] A means by which a user transmits video data via wireless data communication,

[0917] A means for analyzing transmitted video data using automated technology and identifying scene changes,

[0918] Means for dividing video data into multiple parts based on identified scenes,

[0919] A means for automatically applying effective video transformations between divided sections,

[0920] A means of applying visual effects and inserting textual information and visual elements,

[0921] A means of presenting edited video to users on a video sharing device,

[0922] ...

[0923] A system that includes this.

[0924] (Claim 2)

[0925] A means for users to select the style of video,

[0926] The system according to claim 1, further comprising means for adapting video editing based on the selected style.

[0927] (Claim 3)

[0928] The system according to claim 1, further comprising means for publishing edited video data via a network and generating accessible identification information.

[0929] "Example 1"

[0930] (Claim 1)

[0931] A device that allows users to transmit video information via wireless communication,

[0932] A device that processes transmitted video information using automated analysis technology and detects scene changes,

[0933] A device that divides video information into multiple sections based on the detected scene,

[0934] A device that automatically applies effective video conversion between divided sections,

[0935] A device that applies visual effects and inserts textual information and visual elements,

[0936] A device that provides edited video footage to users on a display device,

[0937] A device that generates edited video footage and provides a preview to the user's device,

[0938] A device that allows users to request additional editing or adjustments,

[0939] A system that includes this.

[0940] (Claim 2)

[0941] The system according to claim 1, comprising a selection function for a user to select a visual style for a video, and adapting the editing of the video based on the selected visual style.

[0942] (Claim 3)

[0943] The system according to claim 1, which publishes edited video information via a communication network and generates accessible identification information.

[0944] "Application Example 1"

[0945] (Claim 1)

[0946] A means by which a user transmits video data via wireless data communication,

[0947] A means for analyzing transmitted video data using automated technology and identifying scene changes,

[0948] Means for dividing video data into multiple parts based on identified scenes,

[0949] A means for automatically applying effective video transformations between divided sections,

[0950] A means of applying visual effects and inserting textual information and visual elements,

[0951] A means of presenting edited footage with added visual effects and music related to the subject,

[0952] Means of disseminating edited video data on online platforms,

[0953] A system that includes this.

[0954] (Claim 2)

[0955] A means for users to select the style of video,

[0956] The system according to claim 1, further comprising means for adapting video editing based on the selected style and optimizing it in relation to a specific theme.

[0957] (Claim 3)

[0958] A means for publishing edited video data via a network and generating accessible identification information,

[0959] The system according to claim 1, further comprising means for users to directly share their edited content on social networks.

[0960] "Example 2 of combining an emotion engine"

[0961] (Claim 1)

[0962] A means by which a user transmits video data via wireless data communication,

[0963] A means for analyzing transmitted video data using automated technology and identifying scene changes,

[0964] Means for dividing video data into multiple parts based on identified scenes,

[0965] A means for automatically applying effective video transformations between divided sections,

[0966] A means of analyzing the user's emotions using facial recognition technology and adjusting the visual effects,

[0967] A means of automatically selecting the overall editing style of the video based on emotional information,

[0968] A means of presenting edited video to users on a video sharing device,

[0969] A system that includes this.

[0970] (Claim 2)

[0971] The system according to claim 1, further comprising a means for a user to select a video style, and a means for applying video editing based on the selected style.

[0972] (Claim 3)

[0973] The system according to claim 1, further comprising means for publishing edited video data via a network and generating accessible identification information.

[0974] "Application example 2 of combining emotional engines"

[0975] (Claim 1)

[0976] A means by which a user transmits video data via wireless data communication,

[0977] A means of identifying a user's emotions using emotion analysis technology,

[0978] A means for analyzing transmitted video data using automated technology and identifying scene changes,

[0979] A means for dividing video data into multiple parts based on identified scenes and the user's emotions,

[0980] A means for automatically applying emotionally appropriate visual and auditory effects between divided sections,

[0981] A means of presenting edited video to users on a video sharing device,

[0982] ...

[0983] A system that includes this.

[0984] (Claim 2)

[0985] A means for users to select the style of video,

[0986] The system according to claim 1, further comprising means for adapting video editing based on the selected style and the user's emotions.

[0987] (Claim 3)

[0988] The system according to claim 1, further comprising means for publishing edited video data via a network, adjusting publication settings based on the user's preferences, and generating accessible identification information. [Explanation of Symbols]

[0989] 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 by which a user transmits video data via wireless data communication, A means for analyzing transmitted video data using automated technology and identifying scene changes, Means for dividing video data into multiple parts based on identified scenes, A means for automatically applying effective video transformations between divided sections, A means of applying visual effects and inserting textual information and visual elements, A means of presenting edited video to users on a video sharing device, A system that includes this.

2. A means for users to select the style of video, The system according to claim 1, further comprising means for adapting video editing based on the selected style.

3. The system according to claim 1, further comprising means for publishing edited video data via a network and generating accessible identification information.

Images