system

Abstract

We provide the system. [Solution] A communication method for receiving video data, An analysis means for analyzing the content of received video data and identifying scenes, An editing method that edits videos based on analyzed scenes, based on user instructions, A transmission means for sending edited video data to the user's terminal, 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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By providing a system that enables general users without video editing technology to easily create videos according to their preferences, it is intended to solve the problems of reducing the labor and time required for video editing and realizing the generation of videos with consistent quality.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a system that includes communication means for receiving video data, analysis means for analyzing the content of the received video data and identifying scenes, editing means for editing the video based on user instructions, and means for transmitting the edited video data. As a result, users can simply give instructions in natural language, and the AI ​​agent will automatically edit the video, allowing them to easily obtain the completed video.

[0006] "Communication means" refers to a function or device for receiving video data from a user terminal or transmitting edited video data to a user terminal.

[0007] "Analysis means" refers to a function or device used to understand the content of received video data and to identify scenes within the video.

[0008] "Editing means" refers to a function or device that automatically edits a video based on the analyzed video scenes, according to instructions given by the user.

[0009] "Transmission means" refers to a function or device that transmits edited video data to the user's terminal.

[0010] A "user terminal" refers to a device capable of receiving video data and playing or saving edited videos, and generally includes smartphones and tablets.

[0011] Computer vision technology is a technique that extracts meaningful information from digital images and videos to recognize and identify objects. [Brief explanation of the drawing]

[0012] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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 to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 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 Example 2 when an 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 an emotion engine is combined.

Mode for Carrying Out the Invention

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

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

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

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

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

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

[0019] 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."

[0020] [First Embodiment]

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

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0027] 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.

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

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The system of the present invention is implemented through a process in which a user terminal, such as a smartphone, transmits video data to a server, and the server performs analysis and editing processing to provide the user with the completed video. This system includes communication means, analysis means, editing means, and transmission means.

[0034] Users use a smartphone application to select the video file they want to edit and enter editing instructions in natural language. For example, they can enter instructions such as, "Edit this into an emotionally moving one-minute video."

[0035] The terminal sends data containing the selected video file and instructions to the server. The server analyzes the received video file using an analysis tool and extracts features from each frame of the video. This identifies important scenes within the video.

[0036] Next, the server's editing system edits the video according to the user's instructions. If the instruction is "emotional," the server adds music that suits the video and applies a slow-motion effect to scenes that should be emphasized. After editing, the server encodes the video data into a predetermined file format and sends it back to the user's terminal via the transmission system.

[0037] The completed video is saved to the device and can be viewed by the user through the application. The user can also request further editing as needed. In this way, even without video editing knowledge, users can easily create professional-quality videos.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches the application on their smartphone and selects the video they want to edit. The user then enters instructions for editing the video in natural language (e.g., "I want this to be an emotionally moving one-minute video").

[0041] Step 2:

[0042] The terminal sends the video file selected by the user and the input instructions to the server. The data is then appropriately packaged and transmitted over the network.

[0043] Step 3:

[0044] The server receives the video data and user instructions and begins analyzing the video content. Using the analysis tools, it extracts features from each frame of the video and evaluates the importance of each scene.

[0045] Step 4:

[0046] The server's editing method automatically edits videos based on analyzed data and user instructions. Following the instruction to create an "emotional" video, it selects music that evokes emotion and applies appropriate visual effects.

[0047] Step 5:

[0048] The server encodes the edited video and generates a file in the format specified by the user. This process includes video format conversion and compression.

[0049] Step 6:

[0050] The server sends the generated, completed video back to the user's terminal via a transmission method. The terminal saves this video to its storage and notifies the user that the video is complete.

[0051] Step 7:

[0052] Users can review the completed video on their device and request further editing by entering new instructions as needed.

[0053] (Example 1)

[0054] 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."

[0055] Traditional video editing methods make it difficult for users without specialized knowledge to create high-quality videos, and there is a lack of intuitive ways to edit videos. Furthermore, complex software and lengthy editing processes are required, highlighting the need for efficient and user-friendly systems.

[0056] 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.

[0057] In this invention, the server includes information communication means for receiving video data, analysis means for analyzing the content of the received video data and identifying scenes, and configuration means for constructing a video based on the analyzed scenes according to linguistic instructions. This allows users to intuitively perform professional-quality video editing without requiring specialized knowledge.

[0058] "Information and communication means" refers to a communication interface for transmitting video data from a user terminal to a server.

[0059] "Analysis means" refers to technical methods for analyzing received video data and identifying scenes within the video.

[0060] "Language instructions" are instructions in which the user inputs requests regarding video editing using natural language.

[0061] "Composition means" refers to processing means for editing and composing a video based on the analyzed scene, taking into account the user's language instructions.

[0062] "Intermediation means" refers to a technical method for transmitting the configured video data to a user terminal.

[0063] "Computer vision technology" is a technique that uses computer vision to analyze the content of video and extract visual features.

[0064] The system of this invention is initiated using a user-owned device, such as a smartphone or tablet. The user can select a video file to be edited using a dedicated application and input editing instructions in natural language. For example, the user might input a prompt such as, "Edit this travel video into an inspiring one-minute video."

[0065] The terminal transmits the selected video file and the user's language instructions to the server using information and communication means. Upon receiving the video data, the server uses analysis tools and computer vision technology to analyze the video and identify each scene within it. In this process, important scenes are extracted.

[0066] Subsequently, the server uses configuration tools to perform editing based on the user's linguistic instructions. The configuration process includes selecting music using a generative AI model and adding effects to specific scenes. For example, based on instructions, it is possible to configure the server to add a slow-motion effect or play emotionally moving music in the background.

[0067] Once the editing process is complete, the server encodes the configured video into a predetermined file format and sends the encoded video to the user's terminal using an intermediary. The user can then view the received video using an application on their terminal and make further edits as needed.

[0068] Thus, even without specialized knowledge, users can achieve high-quality video editing through intuitive operation, and the system makes it easy to perform advanced editing using generative AI models.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The user launches a dedicated application on their device, selects the video file to be edited, and enters prompts related to the editing process. The entered prompts are in natural language, such as "Edit my travel video into an inspiring one-minute video." This input data is transmitted from the device to the server using information and communication technology.

[0072] Step 2:

[0073] The server receives video data and prompt messages sent from the terminal and uses analysis tools to identify scenes within the video. The video is analyzed frame by frame using computer vision technology, and important scenes are extracted. This process identifies each scene in the video, and the data is converted into the format required for the next step.

[0074] Step 3:

[0075] The server uses a generative AI model to edit the analyzed scene information in response to user prompts. Specifically, it selects appropriate music from the generative model based on instructions such as "emotional" for selected important scenes, and adds a slow-motion effect to specific scenes. At this stage, the input consists of the analyzed data and video data edited with the prompt output.

[0076] Step 4:

[0077] The server encodes the completed video into a predetermined file format (e.g., MP4). During the encoding process, the data is compressed and converted so that the video can be played correctly on various devices. The encoded video data is then transmitted to the user's device via an intermediary.

[0078] Step 5:

[0079] The terminal receives the encoded video data sent back from the server and notifies the user, making it available for viewing. At this stage, the user can check the completed video using the terminal's application and input instructions for further editing as needed. After checking the results, the user can update the prompt message and resend it to the server to make further adjustments or changes to the video.

[0080] (Application Example 1)

[0081] 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."

[0082] In today's world, there is a demand for easy-to-use video editing techniques that allow anyone to create professional-quality videos without requiring specialized knowledge. However, existing video editing tools require advanced operation and expertise, making them inaccessible to many users. It is crucial to address this issue and provide a system that enables anyone to easily create high-quality videos.

[0083] 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.

[0084] In this invention, the server includes communication means for receiving video information, analysis means for analyzing the contents of the received video information and identifying scenes, and editing means for adding music appropriate to the analyzed scenes and editing the video based on instructions from the user. This makes it possible for users to easily generate professional-quality videos without special editing skills.

[0085] "Video information" refers to a series of video data recorded in digital format, which includes visual information that changes dynamically along the time axis.

[0086] "Communication means" refers to hardware and software components for sending and receiving data over a network, and specifically those that transmit video information to a server.

[0087] "Analysis means" refers to a technique that processes received video information frame by frame, extracts visual features from it, and identifies scenes, using computer vision technology.

[0088] "Editing means" refers to software and algorithms that, based on user instructions, add appropriate music or apply scene-specific visual effects to analyzed scenes.

[0089] A "user terminal" refers to a communication device capable of receiving edited video data, and is typically a smartphone or tablet—a device directly operated by the user.

[0090] The purpose of this invention is to provide a system that allows users to easily perform high-quality video editing. Specific embodiments for carrying out the invention are shown below.

[0091] The server receives video information from the user's device via a communication method. This device is a typical smartphone or tablet and must be connected to the internet.

[0092] The received video information is processed using an analysis tool. This analysis tool utilizes computer vision technology, and specific software such as OpenCV and TENSORFLOW® can be used. Through this analysis, specific scenes within the video are identified, and features associated with those scenes are extracted.

[0093] Subsequently, the editing system edits the video based on the user's instructions. The user provides editing instructions in natural language, for example, by entering the instruction "Create an emotionally moving one-minute video" into the application. The server then selects music appropriate to the specified scene and applies necessary visual effects, such as adding slow motion. A generative AI model is used to select the music and visual effects.

[0094] The edited video information is sent to the user's terminal after format conversion and saved on the terminal. At this time, encoding software such as FFmpeg is used to convert the video data to the appropriate format.

[0095] Users can review the completed video on their device and request further editing as needed. Re-editing follows a similar process, allowing for flexible responses to diverse editing requests.

[0096] For example, a user who wants to create a travel highlight video might enter the prompt, "Create a 2-minute video about the adventure of travel, conveying the fun," and the server will edit the relevant scenes and add the most suitable soundtrack.

[0097] Example of a prompt:

[0098] "Create an adventurous 2-minute video."

[0099] "Edit it into a touching one-minute video."

[0100] This invention makes it possible to provide an environment where anyone can easily perform professional video editing, even without specialized knowledge.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The user selects video data on their device and enters prompts regarding editing. For example, the user might enter "Edit this into an emotional 1-minute video." This input is converted into a video file and natural language instructions, which are then sent to the server.

[0104] Step 2:

[0105] The terminal sends a video file and editing instructions to the server via a communication method. The server receives the data and prepares the video file for analysis. The input is the video file and prompt text, and the output is the data information to be analyzed.

[0106] Step 3:

[0107] The server uses analysis tools to analyze video data and extract visual features from each frame. Computer vision technology is used to identify important scenes and pass that information to the editing tools. The input is each frame of the video, and the output is the identified important scene information.

[0108] Step 4:

[0109] The server uses editing tools to perform edits based on user prompts. Specifically, it adds optimal music to pre-identified important scenes and applies specified visual effects. A generative AI model is used for music selection and effect addition. The input consists of important scene information and prompts, and the output is the edited video data.

[0110] Step 5:

[0111] The server uses tools like FFmpeg to convert the format of the edited video and encode it in a format playable on the user's terminal. The input is the edited video data, and the output is the encoded video file.

[0112] Step 6:

[0113] The encoded video file is sent back to the user's terminal via the transmission method. The user can review the final video on the terminal and, if necessary, give instructions for further editing. The input is the encoded video file, and the output is the final video viewable by the user.

[0114] 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.

[0115] The system of the present invention is implemented through a process in which video data is received from a user terminal and the video is optimally edited by a server equipped with an emotion engine that analyzes user instructions. This system includes communication means, analysis means, emotion engine, editing means, and transmission means.

[0116] Users can use a smartphone application to select a video they want to edit and express their mood and intentions when specifying the desired editing style in natural language. For example, they can give instructions such as, "I want to reminisce about happy memories, so I'd like it to have a bright and happy atmosphere."

[0117] The terminal sends the selected video file and user instruction data to the server. The server analyzes the received data, and first, the emotion engine extracts emotions from the user's instructions. This emotion information is then considered in the subsequent video editing process.

[0118] The server's analysis tools analyze the video content and identify important scenes. Based on the results of the emotion engine, the editing policy for the identified scenes is determined. For example, if the user indicates that they are "happy," the server will select music with an optimistic tone and edit the video to emphasize cheerful scenes.

[0119] Once editing is complete, the server encodes the video in the desired format and sends it to the user's terminal. The terminal saves the video and notifies the user of the result.

[0120] In this way, users can easily create emotionally customized videos without requiring advanced editing skills. Furthermore, the emotion engine can learn the user's preferences by referencing their past selection history, enabling it to provide even more personalized edits.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The user selects the video they want to edit using a smartphone application and inputs their editing style and intentions in natural language. For example, they might give instructions such as, "Emphasize important moments with an emotional atmosphere."

[0124] Step 2:

[0125] The terminal combines the video file selected by the user and the instruction information into a data packet and sends it to the server. Data transmission is performed securely.

[0126] Step 3:

[0127] The server analyzes the received video data and user input. First, the emotion engine recognizes emotions from the user's instructions and extracts psychological elements related to the requested editing style.

[0128] Step 4:

[0129] The server's analysis tools analyze the video content, segment each scene, and extract features. This analysis determines which scenes are important.

[0130] Step 5:

[0131] The server's editing tools initiate the video editing process based on recognized user emotions and data on key scenes. If an emotionally impactful edit is requested, the server selects emotional music and transition effects to highlight key moments.

[0132] Step 6:

[0133] The server encodes the edited video and generates a file in the user-specified format. Care is taken during this process to avoid compromising the quality of the video content.

[0134] Step 7:

[0135] The server sends the completed edited video to the user's terminal, which saves the video to its internal storage. The user is notified when the video is completed and saved, and can play it at any time.

[0136] Step 8:

[0137] Users can review the edited video on their device and, if further changes are needed, enter new instructions to request re-editing.

[0138] (Example 2)

[0139] 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".

[0140] In today's world, users are expected to generate individually customized videos based on their own emotions and intentions, without requiring specialized technical knowledge. However, traditional methods have made it difficult to accurately reflect user emotions and edit them efficiently. Solutions are needed to address the technical challenges of extracting emotions and performing appropriate video editing.

[0141] 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.

[0142] In this invention, the server includes means for receiving video data, means for analyzing the received video data and explicit instructions from the user, and means for extracting the user's emotions based on the analysis results. This enables individually customized emotion-based video editing.

[0143] "Video data" refers to digital files in electronic format that contain visual and audio information, including video files and associated metadata.

[0144] "Means" refers to the implements used to achieve a specific purpose, and in this specification, it refers to the individual functional parts or devices that constitute a system.

[0145] "Receiving" refers to the process by which a terminal or server takes in data from an external source, and is a process carried out using data communication technology.

[0146] "Analyzing" refers to the process of examining and understanding data in detail to extract specific information or patterns, and this often involves algorithms and modeling techniques.

[0147] "Emotion" refers to the subjective experiences and feelings that a user has, and indicates the psychological state generated in response to a specific input.

[0148] "Encoding" refers to the process of converting digital data according to a specific format or standard, and is a technology used to efficiently store or transmit data.

[0149] "Individually customized" means that the settings and specifications have been adjusted to suit a specific user or scenario.

[0150] An "editorial policy" refers to the operations and procedures taken regarding the processing and composition of video data, and is a set of standards determined based on specific objectives and indicators.

[0151] The system of the present invention enables emotion-based video editing through the collaborative operation of the user, terminal, and server.

[0152] The user launches a dedicated application using their smartphone or computer. From this application, they select the video data they want to edit and provide instructions in natural language regarding the emotion and editing style. For example, they might input, "I want a fun video with lots of children's smiles." This instruction is then used as a prompt.

[0153] The terminal sends the video data selected by the user and the input instructions to the server. Data transmission is performed using a secure communication protocol.

[0154] The server launches a generative AI model to analyze the received data. This model extracts emotions from user instructions and performs video data analysis. Computer vision technology is used for the analysis, specifically the OpenCV library for scene analysis. Based on the extracted emotions, the video data is processed using video editing software such as the Adobe Premiere Pro API.

[0155] Music selection and scene emphasis are made to match the emotions, and the edited video data is encoded. Encoding tools such as FFmpeg are used for this encoding process. The encoded data is then sent back to the terminal using a secure communication protocol.

[0156] The terminal saves the received video data and notifies the user when editing is complete. In this way, users can easily create customized videos that match their own emotions. The server can also learn from the user's past selection history and suggest more personalized editing strategies.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The user opens an application on their smartphone or computer and selects the video data they want to edit. Next, they provide input in natural language, specifying the editing style and mood they want to convey. This input is passed to the system as a prompt. The input data consists of the selected video file and the user's instructions.

[0160] Step 2:

[0161] The terminal sends video data and prompt messages from the user to the server. This transmission uses a secure communication protocol such as HTTPS. Input data consists of video files and prompt messages, while output is data transfer to the server.

[0162] Step 3:

[0163] The server extracts the user's sentiment by analyzing the received prompt text. This analysis uses a generative AI model. It receives prompt text as input and generates sentiment tag information as output. Sentiment tags are used as indicators in the editing process.

[0164] Step 4:

[0165] The server's analysis tool analyzes the video data frame by frame to identify key scenes and objects. Computer vision technology is used, specifically leveraging the OpenCV library. The input is video data, and the output is data of the identified important scenes.

[0166] Step 5:

[0167] The server processes the video based on extracted emotions and identified scenes. Specifically, it adds music and filters using the Adobe Premiere Pro API, among others. It also applies video transition effects to match the scenes. The input is emotion tags and important scene data, and the output is the edited video data.

[0168] Step 6:

[0169] The server encodes the edited video data. This process is performed using an encoding tool such as FFmpeg. The input is the edited video data, and the output is an encoded video file.

[0170] Step 7:

[0171] The server sends the encoded video file to the terminal. Again, the data is transferred via a secure communication protocol. The input is the encoded video file, and the output is a message indicating that transmission to the terminal is complete.

[0172] Step 8:

[0173] The terminal saves the received video file to its internal storage. After saving, it notifies the user that editing is complete and makes the video available for review. The input is the video file received from the server, and the output is the notification to the user.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0176] There is a need for a system that allows users to edit videos they have shot more easily and in a way that reflects their emotions and intentions. Conventional editing systems require users to have high skill levels, making it difficult to easily edit videos that reflect their emotions.

[0177] 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.

[0178] In this invention, the server includes communication means for receiving video data, analysis means for analyzing the content of the received video data and identifying scenes, emotion analysis means for analyzing user instructions using natural language processing technology and extracting emotions, editing means for selecting music and filters based on the emotions extracted by the emotion analysis means and editing the video, and transmission means for sending the edited video data to the user terminal. This makes it possible for users to edit videos that match their emotions and intentions without requiring specialized technical skills.

[0179] "Video data" refers to a file format that contains video information, and specifically to video content recorded by a user.

[0180] "Communication means" refers to a method or technology for receiving or transmitting data through a network.

[0181] "Analysis means" refers to a method of performing a process to analyze the content of received data and identify specific elements or patterns.

[0182] "Emotion analysis method" refers to a method of extracting emotions from user instructions using natural language processing technology.

[0183] "Editing" refers to the process of adding music or filters to a video and making visual or auditory changes based on the analysis results and sentiment analysis results.

[0184] "Transmission means" refers to a method or technology for transferring edited video data to a user's terminal in digital format.

[0185] "Natural language processing technology" is computer technology used to understand and analyze human language.

[0186] "Emotions" refer to the inner feelings and intentions that can be gleaned from the user's instructions.

[0187] In this application example, a consumer robot is implemented as a system that automatically edits videos in the home. The server is equipped with communication means to receive video data from user terminals via a network. This data is analyzed using analysis means to identify important scenes and features.

[0188] The robot uses emotion analysis techniques that leverage natural language processing technology to extract emotions from text instructed by the user. For emotion analysis, it employs a natural language processing library (e.g., spaCy). For example, if the user instructs, "I want to watch my pet's daily life in a fun way," the robot will select cheerful music and filters to create a pleasant atmosphere.

[0189] The editing process involves using a video editing library (e.g., FFMpeg) to apply music and filters selected based on sentiment analysis results to the video. The edited video is then encoded and sent to the user's device for easy viewing.

[0190] For example, if a user requests "a compilation of family holiday footage with a happy atmosphere," the emotion analysis tool will extract the emotion of "happiness," and the editing process will add upbeat music and edit the footage to highlight smiles and enjoyable scenes.

[0191] An example of a prompt message is: "Please explain in detail how to edit a video based on the emotions specified by the user. For example, use a video of a birthday party that shows many children smiling."

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The user selects the video to be edited using their device and provides instructions in natural language regarding the editing style and intent. These instructions and the video data are sent to the server. The input is the user's instructions and video data, and the output is the data transfer to the server.

[0195] Step 2:

[0196] The server acquires received video data using communication means and analyzes the video content through analysis means. The input is the received video data, and the server recognizes scenes within the video and identifies important scenes. The output is data of the important scenes.

[0197] Step 3:

[0198] The server performs sentiment analysis on user instructions using a natural language processing engine (e.g., spaCy). The input is the user's instructions, and the sentiment analysis tool extracts emotions such as joy, sadness, and excitement. The output is the analyzed sentiment data.

[0199] Step 4:

[0200] The server-based editing system uses a video editing library (e.g., FFMpeg) to determine the tone of the video based on emotional data. Input consists of data from key scenes and emotional data, and appropriate music and filters are selected and applied. The output is the edited video data.

[0201] Step 5:

[0202] The edited video data is encoded by the server and converted to the optimal format. The input is the edited video data, and the output is the encoded video file.

[0203] Step 6:

[0204] Finally, the server sends the encoded video file back to the user's terminal via a transmission method. The user can then view the result on their terminal and save the video. The input is the encoded video data, and the output is the video file sent to the user.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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".

[0221] The system of the present invention is implemented through a process in which a user terminal, such as a smartphone, transmits video data to a server, and the server performs analysis and editing processing to provide the user with the completed video. This system includes communication means, analysis means, editing means, and transmission means.

[0222] Users use a smartphone application to select the video file they want to edit and enter editing instructions in natural language. For example, they can enter instructions such as, "Edit this into an emotionally moving one-minute video."

[0223] The terminal sends data containing the selected video file and instructions to the server. The server analyzes the received video file using an analysis tool and extracts features from each frame of the video. This identifies important scenes within the video.

[0224] Next, the server's editing system edits the video according to the user's instructions. If the instruction is "emotional," the server adds music that suits the video and applies a slow-motion effect to scenes that should be emphasized. After editing, the server encodes the video data into a predetermined file format and sends it back to the user's terminal via the transmission system.

[0225] The completed video is saved to the device and can be viewed by the user through the application. The user can also request further editing as needed. In this way, even without video editing knowledge, users can easily create professional-quality videos.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The user launches the application on their smartphone and selects the video they want to edit. The user then enters instructions for editing the video in natural language (e.g., "I want this to be an emotionally moving one-minute video").

[0229] Step 2:

[0230] The terminal sends the video file selected by the user and the input instructions to the server. The data is then appropriately packaged and transmitted over the network.

[0231] Step 3:

[0232] The server receives the video data and user instructions and begins analyzing the video content. Using the analysis tools, it extracts features from each frame of the video and evaluates the importance of each scene.

[0233] Step 4:

[0234] The server's editing method automatically edits videos based on analyzed data and user instructions. Following the instruction to create an "emotional" video, it selects music that evokes emotion and applies appropriate visual effects.

[0235] Step 5:

[0236] The server encodes the edited video and generates a file in the format specified by the user. This process includes video format conversion and compression.

[0237] Step 6:

[0238] The server sends the generated, completed video back to the user's terminal via a transmission method. The terminal saves this video to its storage and notifies the user that the video is complete.

[0239] Step 7:

[0240] Users can review the completed video on their device and request further editing by entering new instructions as needed.

[0241] (Example 1)

[0242] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0243] Traditional video editing methods make it difficult for users without specialized knowledge to create high-quality videos, and there is a lack of intuitive ways to edit videos. Furthermore, complex software and lengthy editing processes are required, highlighting the need for efficient and user-friendly systems.

[0244] 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.

[0245] In this invention, the server includes information communication means for receiving video data, analysis means for analyzing the content of the received video data and identifying scenes, and configuration means for constructing a video based on the analyzed scenes according to linguistic instructions. This allows users to intuitively perform professional-quality video editing without requiring specialized knowledge.

[0246] "Information and communication means" refers to a communication interface for transmitting video data from a user terminal to a server.

[0247] "Analysis means" refers to technical methods for analyzing received video data and identifying scenes within the video.

[0248] "Language instructions" are instructions in which the user inputs requests regarding video editing using natural language.

[0249] "Composition means" refers to processing means for editing and composing a video based on the analyzed scene, taking into account the user's language instructions.

[0250] "Intermediation means" refers to a technical method for transmitting the configured video data to a user terminal.

[0251] "Computer vision technology" is a technique that uses computer vision to analyze the content of video and extract visual features.

[0252] The system of this invention is initiated using a user-owned device, such as a smartphone or tablet. The user can select a video file to be edited using a dedicated application and input editing instructions in natural language. For example, the user might input a prompt such as, "Edit this travel video into an inspiring one-minute video."

[0253] The terminal transmits the selected video file and the user's language instructions to the server using information and communication means. Upon receiving the video data, the server uses analysis tools and computer vision technology to analyze the video and identify each scene within it. In this process, important scenes are extracted.

[0254] Subsequently, the server uses configuration tools to perform editing based on the user's linguistic instructions. The configuration process includes selecting music using a generative AI model and adding effects to specific scenes. For example, based on instructions, it is possible to configure the server to add a slow-motion effect or play emotionally moving music in the background.

[0255] Once the editing process is complete, the server encodes the configured video into a predetermined file format and sends the encoded video to the user's terminal using an intermediary. The user can then view the received video using an application on their terminal and make further edits as needed.

[0256] Thus, even without specialized knowledge, users can achieve high-quality video editing through intuitive operation, and the system makes it easy to perform advanced editing using generative AI models.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The user launches a dedicated application on their device, selects the video file to be edited, and enters prompts related to the editing process. The entered prompts are in natural language, such as "Edit my travel video into an inspiring one-minute video." This input data is transmitted from the device to the server using information and communication technology.

[0260] Step 2:

[0261] The server receives video data and prompt messages sent from the terminal and uses analysis tools to identify scenes within the video. The video is analyzed frame by frame using computer vision technology, and important scenes are extracted. This process identifies each scene in the video, and the data is converted into the format required for the next step.

[0262] Step 3:

[0263] The server uses a generative AI model to edit the analyzed scene information in response to user prompts. Specifically, it selects appropriate music from the generative model based on instructions such as "emotional" for selected important scenes, and adds a slow-motion effect to specific scenes. At this stage, the input consists of the analyzed data and video data edited with the prompt output.

[0264] Step 4:

[0265] The server encodes the completed video into a predetermined file format (e.g., MP4). During the encoding process, the data is compressed and converted so that the video can be played correctly on various devices. The encoded video data is then transmitted to the user's device via an intermediary.

[0266] Step 5:

[0267] The terminal receives the encoded video data sent back from the server and notifies the user, making it available for viewing. At this stage, the user can check the completed video using the terminal's application and input instructions for further editing as needed. After checking the results, the user can update the prompt message and resend it to the server to make further adjustments or changes to the video.

[0268] (Application Example 1)

[0269] 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."

[0270] In today's world, there is a demand for easy-to-use video editing techniques that allow anyone to create professional-quality videos without requiring specialized knowledge. However, existing video editing tools require advanced operation and expertise, making them inaccessible to many users. It is crucial to address this issue and provide a system that enables anyone to easily create high-quality videos.

[0271] 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.

[0272] In this invention, the server includes communication means for receiving video information, analysis means for analyzing the contents of the received video information and identifying scenes, and editing means for adding music appropriate to the analyzed scenes and editing the video based on instructions from the user. This makes it possible for users to easily generate professional-quality videos without special editing skills.

[0273] "Video information" refers to a series of video data recorded in digital format, which includes visual information that changes dynamically along the time axis.

[0274] "Communication means" refers to hardware and software components for sending and receiving data over a network, and specifically those that transmit video information to a server.

[0275] "Analysis means" refers to a technique that processes received video information frame by frame, extracts visual features from it, and identifies scenes, using computer vision technology.

[0276] "Editing means" refers to software and algorithms that, based on user instructions, add appropriate music or apply scene-specific visual effects to analyzed scenes.

[0277] A "user terminal" refers to a communication device capable of receiving edited video data, and is typically a smartphone or tablet—a device directly operated by the user.

[0278] The purpose of this invention is to provide a system that allows users to easily perform high-quality video editing. Specific embodiments for carrying out the invention are shown below.

[0279] The server receives video information from the user's device via a communication method. This device is a typical smartphone or tablet and must be connected to the internet.

[0280] The received video information is processed using an analysis tool. This analysis tool utilizes computer vision technology, and specific software such as OpenCV and TensorFlow can be used. Through this analysis, specific scenes within the video are identified, and features associated with those scenes are extracted.

[0281] Subsequently, the editing system edits the video based on the user's instructions. The user provides editing instructions in natural language, for example, by entering the instruction "Create an emotionally moving one-minute video" into the application. The server then selects music appropriate to the specified scene and applies necessary visual effects, such as adding slow motion. A generative AI model is used to select the music and visual effects.

[0282] The edited video information is sent to the user's terminal after format conversion and saved on the terminal. At this time, encoding software such as FFmpeg is used to convert the video data to the appropriate format.

[0283] Users can review the completed video on their device and request further editing as needed. Re-editing follows a similar process, allowing for flexible responses to diverse editing requests.

[0284] As a specific example, when a user who wants to create a highlight video of a trip inputs a prompt such as "Create a 2-minute video that conveys fun with the theme of adventure in travel", the server edits the relevant scenes and adds an optimal soundtrack.

[0285] Examples of prompt sentences:

[0286] "Create an adventurous 2-minute video"

[0287] "Edit it into a moving 1-minute video"

[0288] According to this invention, it is possible to provide an environment in which anyone can easily achieve professional video editing without specialized knowledge.

[0289] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0290] Step 1:

[0291] The user selects video data on the terminal and inputs a prompt sentence regarding editing. As a specific example, the user inputs "Edit it into a moving 1-minute video". This input is converted into the form of data to be sent to the server as a video file and a natural language instruction.

[0292] Step 2:

[0293] The terminal sends the video file and the editing instruction to the server through the communication means. The server acquires the received data and prepares to perform analysis processing on the video file. The input is the video file and the prompt sentence, and the output is the data information to be analyzed.

[0294] Step 3:

[0295] The server uses analysis tools to analyze video data and extract visual features from each frame. Computer vision technology is used to identify important scenes and pass that information to the editing tools. The input is each frame of the video, and the output is the identified important scene information.

[0296] Step 4:

[0297] The server uses editing tools to perform edits based on user prompts. Specifically, it adds optimal music to pre-identified important scenes and applies specified visual effects. A generative AI model is used for music selection and effect addition. The input consists of important scene information and prompts, and the output is the edited video data.

[0298] Step 5:

[0299] The server uses tools like FFmpeg to convert the format of the edited video and encode it in a format playable on the user's terminal. The input is the edited video data, and the output is the encoded video file.

[0300] Step 6:

[0301] The encoded video file is sent back to the user's terminal via the transmission method. The user can review the final video on the terminal and, if necessary, give instructions for further editing. The input is the encoded video file, and the output is the final video viewable by the user.

[0302] 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.

[0303] The system of the present invention is implemented through a process of optimally editing a video by a server equipped with an emotion engine that receives video data from a user terminal and analyzes user instructions. This system includes communication means, analysis means, an emotion engine, editing means, and transmission means.

[0304] When the user uses the application on the smartphone to select the video to be edited and instructs the desired editing style in natural language, the user can express their mood and intention. For example, an instruction such as "I want to look back on happy memories, so please make it in a bright and happy atmosphere" can be given.

[0305] The terminal transmits the selected video file and the user's instruction data to the server. The server analyzes the received data, and first, the emotion engine extracts the emotion from the user's instruction sentence. This emotion information is considered in the subsequent video editing process.

[0306] The analysis means of the server analyzes the video content and identifies important scenes. The editing policy is determined based on the results of the emotion engine for the identified scenes. For example, when the user suggests "happy", the server selects music with an optimistic tone and performs editing to emphasize bright scenes.

[0307] The edited video is encoded in the desired format by the server and transmitted to the user terminal. The terminal saves this video and notifies the user of the result.

[0308] In this way, the user can easily create a customized video based on emotion without requiring advanced editing skills. Also, by referring to the past selection history, the emotion engine can learn the user's preferences and provide more personalized editing.

[0309] The following describes the processing flow.

[0310] Step 1:

[0311] The user selects the video they want to edit using a smartphone application and inputs their editing style and intentions in natural language. For example, they might give instructions such as, "Emphasize important moments with an emotional atmosphere."

[0312] Step 2:

[0313] The terminal combines the video file selected by the user and the instruction information into a data packet and sends it to the server. Data transmission is performed securely.

[0314] Step 3:

[0315] The server analyzes the received video data and user input. First, the emotion engine recognizes emotions from the user's instructions and extracts psychological elements related to the requested editing style.

[0316] Step 4:

[0317] The server's analysis tools analyze the video content, segment each scene, and extract features. This analysis determines which scenes are important.

[0318] Step 5:

[0319] The server's editing tools initiate the video editing process based on recognized user emotions and data on key scenes. If an emotionally impactful edit is requested, the server selects emotional music and transition effects to highlight key moments.

[0320] Step 6:

[0321] The server encodes the edited video and generates a file in the user-specified format. Care is taken during this process to avoid compromising the quality of the video content.

[0322] Step 7:

[0323] The server sends the completed edited video to the user's terminal, which saves the video to its internal storage. The user is notified when the video is completed and saved, and can play it at any time.

[0324] Step 8:

[0325] Users can review the edited video on their device and, if further changes are needed, enter new instructions to request re-editing.

[0326] (Example 2)

[0327] 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".

[0328] In today's world, users are expected to generate individually customized videos based on their own emotions and intentions, without requiring specialized technical knowledge. However, traditional methods have made it difficult to accurately reflect user emotions and edit them efficiently. Solutions are needed to address the technical challenges of extracting emotions and performing appropriate video editing.

[0329] 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.

[0330] In this invention, the server includes means for receiving video data, means for analyzing the received video data and explicit instructions from the user, and means for extracting the user's emotions based on the analysis results. This enables individually customized emotion-based video editing.

[0331] "Video data" refers to digital files in electronic format that contain visual and audio information, including video files and associated metadata.

[0332] "Means" refers to the implements used to achieve a specific purpose, and in this specification, it refers to the individual functional parts or devices that constitute a system.

[0333] "Receiving" refers to the process by which a terminal or server takes in data from an external source, and is a process carried out using data communication technology.

[0334] "Analyzing" refers to the process of examining and understanding data in detail to extract specific information or patterns, and this often involves algorithms and modeling techniques.

[0335] "Emotion" refers to the subjective experiences and feelings that a user has, and indicates the psychological state generated in response to a specific input.

[0336] "Encoding" refers to the process of converting digital data according to a specific format or standard, and is a technology used to efficiently store or transmit data.

[0337] "Individually customized" means that the settings and specifications have been adjusted to suit a specific user or scenario.

[0338] An "editorial policy" refers to the operations and procedures taken regarding the processing and composition of video data, and is a set of standards determined based on specific objectives and indicators.

[0339] The system of the present invention enables emotion-based video editing through the collaborative operation of the user, terminal, and server.

[0340] The user launches a dedicated application using their smartphone or computer. From this application, they select the video data they want to edit and provide instructions in natural language regarding the emotion and editing style. For example, they might input, "I want a fun video with lots of children's smiles." This instruction is then used as a prompt.

[0341] The terminal sends the video data selected by the user and the input instructions to the server. Data transmission is performed using a secure communication protocol.

[0342] The server launches a generative AI model to analyze the received data. This model extracts emotions from user instructions and performs video data analysis. Computer vision technology is used for the analysis, specifically the OpenCV library for scene analysis. Based on the extracted emotions, the video data is processed using video editing software such as the Adobe Premiere Pro API.

[0343] Music selection and scene emphasis are made to match the emotions, and the edited video data is encoded. Encoding tools such as FFmpeg are used for this encoding process. The encoded data is then sent back to the terminal using a secure communication protocol.

[0344] The terminal saves the received video data and notifies the user when editing is complete. In this way, users can easily create customized videos that match their own emotions. The server can also learn from the user's past selection history and suggest more personalized editing strategies.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The user opens an application on their smartphone or computer and selects the video data they want to edit. Next, they provide input in natural language, specifying the editing style and mood they want to convey. This input is passed to the system as a prompt. The input data consists of the selected video file and the user's instructions.

[0348] Step 2:

[0349] The terminal sends video data and prompt messages from the user to the server. This transmission uses a secure communication protocol such as HTTPS. Input data consists of video files and prompt messages, while output is data transfer to the server.

[0350] Step 3:

[0351] The server extracts the user's sentiment by analyzing the received prompt text. This analysis uses a generative AI model. It receives prompt text as input and generates sentiment tag information as output. Sentiment tags are used as indicators in the editing process.

[0352] Step 4:

[0353] The server's analysis tool analyzes the video data frame by frame to identify key scenes and objects. Computer vision technology is used, specifically leveraging the OpenCV library. The input is video data, and the output is data of the identified important scenes.

[0354] Step 5:

[0355] The server processes the video based on extracted emotions and identified scenes. Specifically, it adds music and filters using the Adobe Premiere Pro API, among others. It also applies video transition effects to match the scenes. The input is emotion tags and important scene data, and the output is the edited video data.

[0356] Step 6:

[0357] The server encodes the edited video data. This process is performed using an encoding tool such as FFmpeg. The input is the edited video data, and the output is an encoded video file.

[0358] Step 7:

[0359] The server sends the encoded video file to the terminal. Again, the data is transferred via a secure communication protocol. The input is the encoded video file, and the output is a message indicating that transmission to the terminal is complete.

[0360] Step 8:

[0361] The terminal saves the received video file to its internal storage. After saving, it notifies the user that editing is complete and makes the video available for review. The input is the video file received from the server, and the output is the notification to the user.

[0362] (Application Example 2)

[0363] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0364] There is a need for a system that allows users to edit videos they have shot more easily and in a way that reflects their emotions and intentions. Conventional editing systems require users to have high skill levels, making it difficult to easily edit videos that reflect their emotions.

[0365] 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.

[0366] In this invention, the server includes communication means for receiving video data, analysis means for analyzing the content of the received video data and identifying scenes, emotion analysis means for analyzing user instructions using natural language processing technology and extracting emotions, editing means for selecting music and filters based on the emotions extracted by the emotion analysis means and editing the video, and transmission means for sending the edited video data to the user terminal. This makes it possible for users to edit videos that match their emotions and intentions without requiring specialized technical skills.

[0367] "Video data" refers to a file format that contains video information, and specifically to video content recorded by a user.

[0368] "Communication means" refers to a method or technology for receiving or transmitting data through a network.

[0369] "Analysis means" refers to a method of performing a process to analyze the content of received data and identify specific elements or patterns.

[0370] "Emotion analysis method" refers to a method of extracting emotions from user instructions using natural language processing technology.

[0371] "Editing" refers to the process of adding music or filters to a video and making visual or auditory changes based on the analysis results and sentiment analysis results.

[0372] "Transmission means" refers to a method or technology for transferring edited video data to a user's terminal in digital format.

[0373] "Natural language processing technology" is computer technology used to understand and analyze human language.

[0374] "Emotions" refer to the inner feelings and intentions that can be gleaned from the user's instructions.

[0375] In this application example, a consumer robot is implemented as a system that automatically edits videos in the home. The server is equipped with communication means to receive video data from user terminals via a network. This data is analyzed using analysis means to identify important scenes and features.

[0376] The robot uses emotion analysis techniques that leverage natural language processing technology to extract emotions from text instructed by the user. For emotion analysis, it employs a natural language processing library (e.g., spaCy). For example, if the user instructs, "I want to watch my pet's daily life in a fun way," the robot will select cheerful music and filters to create a pleasant atmosphere.

[0377] The editing process involves using a video editing library (e.g., FFMpeg) to apply music and filters selected based on sentiment analysis results to the video. The edited video is then encoded and sent to the user's device for easy viewing.

[0378] For example, if a user requests "a compilation of family holiday footage with a happy atmosphere," the emotion analysis tool will extract the emotion of "happiness," and the editing process will add upbeat music and edit the footage to highlight smiles and enjoyable scenes.

[0379] An example of a prompt message is: "Please explain in detail how to edit a video based on the emotions specified by the user. For example, use a video of a birthday party that shows many children smiling."

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The user selects the video to be edited using their device and provides instructions in natural language regarding the editing style and intent. These instructions and the video data are sent to the server. The input is the user's instructions and video data, and the output is the data transfer to the server.

[0383] Step 2:

[0384] The server acquires received video data using communication means and analyzes the video content through analysis means. The input is the received video data, and the server recognizes scenes within the video and identifies important scenes. The output is data of the important scenes.

[0385] Step 3:

[0386] The server performs sentiment analysis on user instructions using a natural language processing engine (e.g., spaCy). The input is the user's instructions, and the sentiment analysis tool extracts emotions such as joy, sadness, and excitement. The output is the analyzed sentiment data.

[0387] Step 4:

[0388] The server-based editing system uses a video editing library (e.g., FFMpeg) to determine the tone of the video based on emotional data. Input consists of data from key scenes and emotional data, and appropriate music and filters are selected and applied. The output is the edited video data.

[0389] Step 5:

[0390] The edited video data is encoded by the server and converted to the optimal format. The input is the edited video data, and the output is the encoded video file.

[0391] Step 6:

[0392] Finally, the server sends the encoded video file back to the user's terminal via a transmission method. The user can then view the result on their terminal and save the video. The input is the encoded video data, and the output is the video file sent to the user.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] The system of the present invention is implemented through a process in which a user terminal, such as a smartphone, transmits video data to a server, and the server performs analysis and editing processing to provide the user with the completed video. This system includes communication means, analysis means, editing means, and transmission means.

[0410] Users use a smartphone application to select the video file they want to edit and enter editing instructions in natural language. For example, they can enter instructions such as, "Edit this into an emotionally moving one-minute video."

[0411] The terminal sends data containing the selected video file and instructions to the server. The server analyzes the received video file using an analysis tool and extracts features from each frame of the video. This identifies important scenes within the video.

[0412] Next, the server's editing system edits the video according to the user's instructions. If the instruction is "emotional," the server adds music that suits the video and applies a slow-motion effect to scenes that should be emphasized. After editing, the server encodes the video data into a predetermined file format and sends it back to the user's terminal via the transmission system.

[0413] The completed video is saved to the device and can be viewed by the user through the application. The user can also request further editing as needed. In this way, even without video editing knowledge, users can easily create professional-quality videos.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The user launches the application on their smartphone and selects the video they want to edit. The user then enters instructions for editing the video in natural language (e.g., "I want this to be an emotionally moving one-minute video").

[0417] Step 2:

[0418] The terminal sends the video file selected by the user and the input instructions to the server. The data is then appropriately packaged and transmitted over the network.

[0419] Step 3:

[0420] The server receives the video data and user instructions and begins analyzing the video content. Using the analysis tools, it extracts features from each frame of the video and evaluates the importance of each scene.

[0421] Step 4:

[0422] The server's editing method automatically edits videos based on analyzed data and user instructions. Following the instruction to create an "emotional" video, it selects music that evokes emotion and applies appropriate visual effects.

[0423] Step 5:

[0424] The server encodes the edited video and generates a file in the format specified by the user. This process includes video format conversion and compression.

[0425] Step 6:

[0426] The server sends the generated, completed video back to the user's terminal via a transmission method. The terminal saves this video to its storage and notifies the user that the video is complete.

[0427] Step 7:

[0428] Users can review the completed video on their device and request further editing by entering new instructions as needed.

[0429] (Example 1)

[0430] 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."

[0431] Traditional video editing methods make it difficult for users without specialized knowledge to create high-quality videos, and there is a lack of intuitive ways to edit videos. Furthermore, complex software and lengthy editing processes are required, highlighting the need for efficient and user-friendly systems.

[0432] 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.

[0433] In this invention, the server includes information communication means for receiving video data, analysis means for analyzing the content of the received video data and identifying scenes, and configuration means for constructing a video based on the analyzed scenes according to linguistic instructions. This allows users to intuitively perform professional-quality video editing without requiring specialized knowledge.

[0434] "Information and communication means" refers to a communication interface for transmitting video data from a user terminal to a server.

[0435] "Analysis means" refers to technical methods for analyzing received video data and identifying scenes within the video.

[0436] "Language instructions" are instructions in which the user inputs requests regarding video editing using natural language.

[0437] "Composition means" refers to processing means for editing and composing a video based on the analyzed scene, taking into account the user's language instructions.

[0438] "Intermediation means" refers to a technical method for transmitting the configured video data to a user terminal.

[0439] "Computer vision technology" is a technique that uses computer vision to analyze the content of video and extract visual features.

[0440] The system of this invention is initiated using a user-owned device, such as a smartphone or tablet. The user can select a video file to be edited using a dedicated application and input editing instructions in natural language. For example, the user might input a prompt such as, "Edit this travel video into an inspiring one-minute video."

[0441] The terminal transmits the selected video file and the user's language instructions to the server using information and communication means. Upon receiving the video data, the server uses analysis tools and computer vision technology to analyze the video and identify each scene within it. In this process, important scenes are extracted.

[0442] Subsequently, the server uses configuration tools to perform editing based on the user's linguistic instructions. The configuration process includes selecting music using a generative AI model and adding effects to specific scenes. For example, based on instructions, it is possible to configure the server to add a slow-motion effect or play emotionally moving music in the background.

[0443] Once the editing process is complete, the server encodes the configured video into a predetermined file format and sends the encoded video to the user's terminal using an intermediary. The user can then view the received video using an application on their terminal and make further edits as needed.

[0444] Thus, even without specialized knowledge, users can achieve high-quality video editing through intuitive operation, and the system makes it easy to perform advanced editing using generative AI models.

[0445] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0446] Step 1:

[0447] The user launches a dedicated application on their device, selects the video file to be edited, and enters prompts related to the editing process. The entered prompts are in natural language, such as "Edit my travel video into an inspiring one-minute video." This input data is transmitted from the device to the server using information and communication technology.

[0448] Step 2:

[0449] The server receives video data and prompt messages sent from the terminal and uses analysis tools to identify scenes within the video. The video is analyzed frame by frame using computer vision technology, and important scenes are extracted. This process identifies each scene in the video, and the data is converted into the format required for the next step.

[0450] Step 3:

[0451] The server uses a generative AI model to edit the analyzed scene information in response to user prompts. Specifically, it selects appropriate music from the generative model based on instructions such as "emotional" for selected important scenes, and adds a slow-motion effect to specific scenes. At this stage, the input consists of the analyzed data and video data edited with the prompt output.

[0452] Step 4:

[0453] The server encodes the completed video into a predetermined file format (e.g., MP4). During the encoding process, the data is compressed and converted so that the video can be played correctly on various devices. The encoded video data is then transmitted to the user's device via an intermediary.

[0454] Step 5:

[0455] The terminal receives the encoded video data sent back from the server and notifies the user, making it available for viewing. At this stage, the user can check the completed video using the terminal's application and input instructions for further editing as needed. After checking the results, the user can update the prompt message and resend it to the server to make further adjustments or changes to the video.

[0456] (Application Example 1)

[0457] 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."

[0458] In today's world, there is a demand for easy-to-use video editing techniques that allow anyone to create professional-quality videos without requiring specialized knowledge. However, existing video editing tools require advanced operation and expertise, making them inaccessible to many users. It is crucial to address this issue and provide a system that enables anyone to easily create high-quality videos.

[0459] 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.

[0460] In this invention, the server includes communication means for receiving video information, analysis means for analyzing the contents of the received video information and identifying scenes, and editing means for adding music appropriate to the analyzed scenes and editing the video based on instructions from the user. This makes it possible for users to easily generate professional-quality videos without special editing skills.

[0461] "Video information" refers to a series of video data recorded in digital format, which includes visual information that changes dynamically along the time axis.

[0462] "Communication means" refers to hardware and software components for sending and receiving data over a network, and specifically those that transmit video information to a server.

[0463] "Analysis means" refers to a technique that processes received video information frame by frame, extracts visual features from it, and identifies scenes, using computer vision technology.

[0464] "Editing means" refers to software and algorithms that, based on user instructions, add appropriate music or apply scene-specific visual effects to analyzed scenes.

[0465] A "user terminal" refers to a communication device capable of receiving edited video data, and is typically a smartphone or tablet—a device directly operated by the user.

[0466] The purpose of this invention is to provide a system that allows users to easily perform high-quality video editing. Specific embodiments for carrying out the invention are shown below.

[0467] The server receives video information from the user's device via a communication method. This device is a typical smartphone or tablet and must be connected to the internet.

[0468] The received video information is processed using an analysis tool. This analysis tool utilizes computer vision technology, and specific software such as OpenCV and TensorFlow can be used. Through this analysis, specific scenes within the video are identified, and features associated with those scenes are extracted.

[0469] Subsequently, the editing system edits the video based on the user's instructions. The user provides editing instructions in natural language, for example, by entering the instruction "Create an emotionally moving one-minute video" into the application. The server then selects music appropriate to the specified scene and applies necessary visual effects, such as adding slow motion. A generative AI model is used to select the music and visual effects.

[0470] The edited video information is sent to the user's terminal after format conversion and saved on the terminal. At this time, encoding software such as FFmpeg is used to convert the video data to the appropriate format.

[0471] Users can review the completed video on their device and request further editing as needed. Re-editing follows a similar process, allowing for flexible responses to diverse editing requests.

[0472] For example, a user who wants to create a travel highlight video might enter the prompt, "Create a 2-minute video about the adventure of travel, conveying the fun," and the server will edit the relevant scenes and add the most suitable soundtrack.

[0473] Example of a prompt:

[0474] "Create an adventurous 2-minute video."

[0475] "Edit it into a touching one-minute video."

[0476] This invention makes it possible to provide an environment where anyone can easily perform professional video editing, even without specialized knowledge.

[0477] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0478] Step 1:

[0479] The user selects video data on their device and enters prompts regarding editing. For example, the user might enter "Edit this into an emotional 1-minute video." This input is converted into a video file and natural language instructions, which are then sent to the server.

[0480] Step 2:

[0481] The terminal sends a video file and editing instructions to the server via a communication method. The server receives the data and prepares the video file for analysis. The input is the video file and prompt text, and the output is the data information to be analyzed.

[0482] Step 3:

[0483] The server uses analysis tools to analyze video data and extract visual features from each frame. Computer vision technology is used to identify important scenes and pass that information to the editing tools. The input is each frame of the video, and the output is the identified important scene information.

[0484] Step 4:

[0485] The server uses editing tools to perform edits based on user prompts. Specifically, it adds optimal music to pre-identified important scenes and applies specified visual effects. A generative AI model is used for music selection and effect addition. The input consists of important scene information and prompts, and the output is the edited video data.

[0486] Step 5:

[0487] The server uses tools like FFmpeg to convert the format of the edited video and encode it in a format playable on the user's terminal. The input is the edited video data, and the output is the encoded video file.

[0488] Step 6:

[0489] The encoded video file is sent back to the user's terminal via the transmission method. The user can review the final video on the terminal and, if necessary, give instructions for further editing. The input is the encoded video file, and the output is the final video viewable by the user.

[0490] 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.

[0491] The system of the present invention is implemented through a process in which video data is received from a user terminal and the video is optimally edited by a server equipped with an emotion engine that analyzes user instructions. This system includes communication means, analysis means, emotion engine, editing means, and transmission means.

[0492] Users can use a smartphone application to select a video they want to edit and express their mood and intentions when specifying the desired editing style in natural language. For example, they can give instructions such as, "I want to reminisce about happy memories, so I'd like it to have a bright and happy atmosphere."

[0493] The terminal sends the selected video file and user instruction data to the server. The server analyzes the received data, and first, the emotion engine extracts emotions from the user's instructions. This emotion information is then considered in the subsequent video editing process.

[0494] The server's analysis tools analyze the video content and identify important scenes. Based on the results of the emotion engine, the editing policy for the identified scenes is determined. For example, if the user indicates that they are "happy," the server will select music with an optimistic tone and edit the video to emphasize cheerful scenes.

[0495] Once editing is complete, the server encodes the video in the desired format and sends it to the user's terminal. The terminal saves the video and notifies the user of the result.

[0496] In this way, users can easily create emotionally customized videos without requiring advanced editing skills. Furthermore, the emotion engine can learn the user's preferences by referencing their past selection history, enabling it to provide even more personalized edits.

[0497] The following describes the processing flow.

[0498] Step 1:

[0499] The user selects the video they want to edit using a smartphone application and inputs their editing style and intentions in natural language. For example, they might give instructions such as, "Emphasize important moments with an emotional atmosphere."

[0500] Step 2:

[0501] The terminal combines the video file selected by the user and the instruction information into a data packet and sends it to the server. Data transmission is performed securely.

[0502] Step 3:

[0503] The server analyzes the received video data and user input. First, the emotion engine recognizes emotions from the user's instructions and extracts psychological elements related to the requested editing style.

[0504] Step 4:

[0505] The server's analysis tools analyze the video content, segment each scene, and extract features. This analysis determines which scenes are important.

[0506] Step 5:

[0507] The server's editing tools initiate the video editing process based on recognized user emotions and data on key scenes. If an emotionally impactful edit is requested, the server selects emotional music and transition effects to highlight key moments.

[0508] Step 6:

[0509] The server encodes the edited video and generates a file in the user-specified format. Care is taken during this process to avoid compromising the quality of the video content.

[0510] Step 7:

[0511] The server sends the completed edited video to the user's terminal, which saves the video to its internal storage. The user is notified when the video is completed and saved, and can play it at any time.

[0512] Step 8:

[0513] Users can review the edited video on their device and, if further changes are needed, enter new instructions to request re-editing.

[0514] (Example 2)

[0515] 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."

[0516] In today's world, users are expected to generate individually customized videos based on their own emotions and intentions, without requiring specialized technical knowledge. However, traditional methods have made it difficult to accurately reflect user emotions and edit them efficiently. Solutions are needed to address the technical challenges of extracting emotions and performing appropriate video editing.

[0517] 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.

[0518] In this invention, the server includes means for receiving video data, means for analyzing the received video data and explicit instructions from the user, and means for extracting the user's emotions based on the analysis results. This enables individually customized emotion-based video editing.

[0519] "Video data" refers to digital files in electronic format that contain visual and audio information, including video files and associated metadata.

[0520] "Means" refers to the implements used to achieve a specific purpose, and in this specification, it refers to the individual functional parts or devices that constitute a system.

[0521] "Receiving" refers to the process by which a terminal or server takes in data from an external source, and is a process carried out using data communication technology.

[0522] "Analyzing" refers to the process of examining and understanding data in detail to extract specific information or patterns, and this often involves algorithms and modeling techniques.

[0523] "Emotion" refers to the subjective experiences and feelings that a user has, and indicates the psychological state generated in response to a specific input.

[0524] "Encoding" refers to the process of converting digital data according to a specific format or standard, and is a technology used to efficiently store or transmit data.

[0525] "Individually customized" means that the settings and specifications have been adjusted to suit a specific user or scenario.

[0526] An "editorial policy" refers to the operations and procedures taken regarding the processing and composition of video data, and is a set of standards determined based on specific objectives and indicators.

[0527] The system of the present invention enables emotion-based video editing through the collaborative operation of the user, terminal, and server.

[0528] The user launches a dedicated application using their smartphone or computer. From this application, they select the video data they want to edit and provide instructions in natural language regarding the emotion and editing style. For example, they might input, "I want a fun video with lots of children's smiles." This instruction is then used as a prompt.

[0529] The terminal sends the video data selected by the user and the input instructions to the server. Data transmission is performed using a secure communication protocol.

[0530] The server launches a generative AI model to analyze the received data. This model extracts emotions from user instructions and performs video data analysis. Computer vision technology is used for the analysis, specifically the OpenCV library for scene analysis. Based on the extracted emotions, the video data is processed using video editing software such as the Adobe Premiere Pro API.

[0531] Music selection and scene emphasis are made to match the emotions, and the edited video data is encoded. Encoding tools such as FFmpeg are used for this encoding process. The encoded data is then sent back to the terminal using a secure communication protocol.

[0532] The terminal saves the received video data and notifies the user when editing is complete. In this way, users can easily create customized videos that match their own emotions. The server can also learn from the user's past selection history and suggest more personalized editing strategies.

[0533] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0534] Step 1:

[0535] The user opens an application on their smartphone or computer and selects the video data they want to edit. Next, they provide input in natural language, specifying the editing style and mood they want to convey. This input is passed to the system as a prompt. The input data consists of the selected video file and the user's instructions.

[0536] Step 2:

[0537] The terminal sends video data and prompt messages from the user to the server. This transmission uses a secure communication protocol such as HTTPS. Input data consists of video files and prompt messages, while output is data transfer to the server.

[0538] Step 3:

[0539] The server extracts the user's sentiment by analyzing the received prompt text. This analysis uses a generative AI model. It receives prompt text as input and generates sentiment tag information as output. Sentiment tags are used as indicators in the editing process.

[0540] Step 4:

[0541] The server's analysis tool analyzes the video data frame by frame to identify key scenes and objects. Computer vision technology is used, specifically leveraging the OpenCV library. The input is video data, and the output is data of the identified important scenes.

[0542] Step 5:

[0543] The server processes the video based on extracted emotions and identified scenes. Specifically, it adds music and filters using the Adobe Premiere Pro API, among others. It also applies video transition effects to match the scenes. The input is emotion tags and important scene data, and the output is the edited video data.

[0544] Step 6:

[0545] The server encodes the edited video data. This process is performed using an encoding tool such as FFmpeg. The input is the edited video data, and the output is an encoded video file.

[0546] Step 7:

[0547] The server sends the encoded video file to the terminal. Again, the data is transferred via a secure communication protocol. The input is the encoded video file, and the output is a message indicating that transmission to the terminal is complete.

[0548] Step 8:

[0549] The terminal saves the received video file to its internal storage. After saving, it notifies the user that editing is complete and makes the video available for review. The input is the video file received from the server, and the output is the notification to the user.

[0550] (Application Example 2)

[0551] 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."

[0552] There is a need for a system that allows users to edit videos they have shot more easily and in a way that reflects their emotions and intentions. Conventional editing systems require users to have high skill levels, making it difficult to easily edit videos that reflect their emotions.

[0553] 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.

[0554] In this invention, the server includes communication means for receiving video data, analysis means for analyzing the content of the received video data and identifying scenes, emotion analysis means for analyzing user instructions using natural language processing technology and extracting emotions, editing means for selecting music and filters based on the emotions extracted by the emotion analysis means and editing the video, and transmission means for sending the edited video data to the user terminal. This makes it possible for users to edit videos that match their emotions and intentions without requiring specialized technical skills.

[0555] "Video data" refers to a file format that contains video information, and specifically to video content recorded by a user.

[0556] "Communication means" refers to a method or technology for receiving or transmitting data through a network.

[0557] "Analysis means" refers to a method of performing a process to analyze the content of received data and identify specific elements or patterns.

[0558] "Emotion analysis method" refers to a method of extracting emotions from user instructions using natural language processing technology.

[0559] "Editing" refers to the process of adding music or filters to a video and making visual or auditory changes based on the analysis results and sentiment analysis results.

[0560] "Transmission means" refers to a method or technology for transferring edited video data to a user's terminal in digital format.

[0561] "Natural language processing technology" is computer technology used to understand and analyze human language.

[0562] "Emotions" refer to the inner feelings and intentions that can be gleaned from the user's instructions.

[0563] In this application example, a consumer robot is implemented as a system that automatically edits videos in the home. The server is equipped with communication means to receive video data from user terminals via a network. This data is analyzed using analysis means to identify important scenes and features.

[0564] The robot uses emotion analysis techniques that leverage natural language processing technology to extract emotions from text instructed by the user. For emotion analysis, it employs a natural language processing library (e.g., spaCy). For example, if the user instructs, "I want to watch my pet's daily life in a fun way," the robot will select cheerful music and filters to create a pleasant atmosphere.

[0565] The editing process involves using a video editing library (e.g., FFMpeg) to apply music and filters selected based on sentiment analysis results to the video. The edited video is then encoded and sent to the user's device for easy viewing.

[0566] For example, if a user requests "a compilation of family holiday footage with a happy atmosphere," the emotion analysis tool will extract the emotion of "happiness," and the editing process will add upbeat music and edit the footage to highlight smiles and enjoyable scenes.

[0567] An example of a prompt message is: "Please explain in detail how to edit a video based on the emotions specified by the user. For example, use a video of a birthday party that shows many children smiling."

[0568] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0569] Step 1:

[0570] The user selects the video to be edited using their device and provides instructions in natural language regarding the editing style and intent. These instructions and the video data are sent to the server. The input is the user's instructions and video data, and the output is the data transfer to the server.

[0571] Step 2:

[0572] The server acquires received video data using communication means and analyzes the video content through analysis means. The input is the received video data, and the server recognizes scenes within the video and identifies important scenes. The output is data of the important scenes.

[0573] Step 3:

[0574] The server performs sentiment analysis on user instructions using a natural language processing engine (e.g., spaCy). The input is the user's instructions, and the sentiment analysis tool extracts emotions such as joy, sadness, and excitement. The output is the analyzed sentiment data.

[0575] Step 4:

[0576] The server-based editing system uses a video editing library (e.g., FFMpeg) to determine the tone of the video based on emotional data. Input consists of data from key scenes and emotional data, and appropriate music and filters are selected and applied. The output is the edited video data.

[0577] Step 5:

[0578] The edited video data is encoded by the server and converted to the optimal format. The input is the edited video data, and the output is the encoded video file.

[0579] Step 6:

[0580] Finally, the server sends the encoded video file back to the user's terminal via a transmission method. The user can then view the result on their terminal and save the video. The input is the encoded video data, and the output is the video file sent to the user.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] [Fourth Embodiment]

[0585] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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).

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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".

[0598] The system of the present invention is implemented through a process in which a user terminal, such as a smartphone, transmits video data to a server, and the server performs analysis and editing processing to provide the user with the completed video. This system includes communication means, analysis means, editing means, and transmission means.

[0599] Users use a smartphone application to select the video file they want to edit and enter editing instructions in natural language. For example, they can enter instructions such as, "Edit this into an emotionally moving one-minute video."

[0600] The terminal sends data containing the selected video file and instructions to the server. The server analyzes the received video file using an analysis tool and extracts features from each frame of the video. This identifies important scenes within the video.

[0601] Next, the server's editing system edits the video according to the user's instructions. If the instruction is "emotional," the server adds music that suits the video and applies a slow-motion effect to scenes that should be emphasized. After editing, the server encodes the video data into a predetermined file format and sends it back to the user's terminal via the transmission system.

[0602] The completed video is saved to the device and can be viewed by the user through the application. The user can also request further editing as needed. In this way, even without video editing knowledge, users can easily create professional-quality videos.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The user launches the application on their smartphone and selects the video they want to edit. The user then enters instructions for editing the video in natural language (e.g., "I want this to be an emotionally moving one-minute video").

[0606] Step 2:

[0607] The terminal sends the video file selected by the user and the input instructions to the server. The data is then appropriately packaged and transmitted over the network.

[0608] Step 3:

[0609] The server receives the video data and user instructions and begins analyzing the video content. Using the analysis tools, it extracts features from each frame of the video and evaluates the importance of each scene.

[0610] Step 4:

[0611] The server's editing method automatically edits videos based on analyzed data and user instructions. Following the instruction to create an "emotional" video, it selects music that evokes emotion and applies appropriate visual effects.

[0612] Step 5:

[0613] The server encodes the edited video and generates a file in the format specified by the user. This process includes video format conversion and compression.

[0614] Step 6:

[0615] The server sends the generated, completed video back to the user's terminal via a transmission method. The terminal saves this video to its storage and notifies the user that the video is complete.

[0616] Step 7:

[0617] Users can review the completed video on their device and request further editing by entering new instructions as needed.

[0618] (Example 1)

[0619] 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".

[0620] Traditional video editing methods make it difficult for users without specialized knowledge to create high-quality videos, and there is a lack of intuitive ways to edit videos. Furthermore, complex software and lengthy editing processes are required, highlighting the need for efficient and user-friendly systems.

[0621] 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.

[0622] In this invention, the server includes information communication means for receiving video data, analysis means for analyzing the content of the received video data and identifying scenes, and configuration means for constructing a video based on the analyzed scenes according to linguistic instructions. This allows users to intuitively perform professional-quality video editing without requiring specialized knowledge.

[0623] "Information and communication means" refers to a communication interface for transmitting video data from a user terminal to a server.

[0624] "Analysis means" refers to technical methods for analyzing received video data and identifying scenes within the video.

[0625] "Language instructions" are instructions in which the user inputs requests regarding video editing using natural language.

[0626] "Composition means" refers to processing means for editing and composing a video based on the analyzed scene, taking into account the user's language instructions.

[0627] "Intermediation means" refers to a technical method for transmitting the configured video data to a user terminal.

[0628] "Computer vision technology" is a technique that uses computer vision to analyze the content of video and extract visual features.

[0629] The system of this invention is initiated using a user-owned device, such as a smartphone or tablet. The user can select a video file to be edited using a dedicated application and input editing instructions in natural language. For example, the user might input a prompt such as, "Edit this travel video into an inspiring one-minute video."

[0630] The terminal transmits the selected video file and the user's language instructions to the server using information and communication means. Upon receiving the video data, the server uses analysis tools and computer vision technology to analyze the video and identify each scene within it. In this process, important scenes are extracted.

[0631] Subsequently, the server uses configuration tools to perform editing based on the user's linguistic instructions. The configuration process includes selecting music using a generative AI model and adding effects to specific scenes. For example, based on instructions, it is possible to configure the server to add a slow-motion effect or play emotionally moving music in the background.

[0632] Once the editing process is complete, the server encodes the configured video into a predetermined file format and sends the encoded video to the user's terminal using an intermediary. The user can then view the received video using an application on their terminal and make further edits as needed.

[0633] Thus, even without specialized knowledge, users can achieve high-quality video editing through intuitive operation, and the system makes it easy to perform advanced editing using generative AI models.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The user launches a dedicated application on their device, selects the video file to be edited, and enters prompts related to the editing process. The entered prompts are in natural language, such as "Edit my travel video into an inspiring one-minute video." This input data is transmitted from the device to the server using information and communication technology.

[0637] Step 2:

[0638] The server receives video data and prompt messages sent from the terminal and uses analysis tools to identify scenes within the video. The video is analyzed frame by frame using computer vision technology, and important scenes are extracted. This process identifies each scene in the video, and the data is converted into the format required for the next step.

[0639] Step 3:

[0640] The server uses a generative AI model to edit the analyzed scene information in response to user prompts. Specifically, it selects appropriate music from the generative model based on instructions such as "emotional" for selected important scenes, and adds a slow-motion effect to specific scenes. At this stage, the input consists of the analyzed data and video data edited with the prompt output.

[0641] Step 4:

[0642] The server encodes the completed video into a predetermined file format (e.g., MP4). During the encoding process, the data is compressed and converted so that the video can be played correctly on various devices. The encoded video data is then transmitted to the user's device via an intermediary.

[0643] Step 5:

[0644] The terminal receives the encoded video data sent back from the server and notifies the user, making it available for viewing. At this stage, the user can check the completed video using the terminal's application and input instructions for further editing as needed. After checking the results, the user can update the prompt message and resend it to the server to make further adjustments or changes to the video.

[0645] (Application Example 1)

[0646] 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".

[0647] In today's world, there is a demand for easy-to-use video editing techniques that allow anyone to create professional-quality videos without requiring specialized knowledge. However, existing video editing tools require advanced operation and expertise, making them inaccessible to many users. It is crucial to address this issue and provide a system that enables anyone to easily create high-quality videos.

[0648] 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.

[0649] In this invention, the server includes communication means for receiving video information, analysis means for analyzing the contents of the received video information and identifying scenes, and editing means for adding music appropriate to the analyzed scenes and editing the video based on instructions from the user. This makes it possible for users to easily generate professional-quality videos without special editing skills.

[0650] "Video information" refers to a series of video data recorded in digital format, which includes visual information that changes dynamically along the time axis.

[0651] "Communication means" refers to hardware and software components for sending and receiving data over a network, and specifically those that transmit video information to a server.

[0652] "Analysis means" refers to a technique that processes received video information frame by frame, extracts visual features from it, and identifies scenes, using computer vision technology.

[0653] "Editing means" refers to software and algorithms that, based on user instructions, add appropriate music or apply scene-specific visual effects to analyzed scenes.

[0654] A "user terminal" refers to a communication device capable of receiving edited video data, and is typically a smartphone or tablet—a device directly operated by the user.

[0655] The purpose of this invention is to provide a system that allows users to easily perform high-quality video editing. Specific embodiments for carrying out the invention are shown below.

[0656] The server receives video information from the user's device via a communication method. This device is a typical smartphone or tablet and must be connected to the internet.

[0657] The received video information is processed using an analysis tool. This analysis tool utilizes computer vision technology, and specific software such as OpenCV and TensorFlow can be used. Through this analysis, specific scenes within the video are identified, and features associated with those scenes are extracted.

[0658] Subsequently, the editing system edits the video based on the user's instructions. The user provides editing instructions in natural language, for example, by entering the instruction "Create an emotionally moving one-minute video" into the application. The server then selects music appropriate to the specified scene and applies necessary visual effects, such as adding slow motion. A generative AI model is used to select the music and visual effects.

[0659] The edited video information is sent to the user's terminal after format conversion and saved on the terminal. At this time, encoding software such as FFmpeg is used to convert the video data to the appropriate format.

[0660] Users can review the completed video on their device and request further editing as needed. Re-editing follows a similar process, allowing for flexible responses to diverse editing requests.

[0661] For example, a user who wants to create a travel highlight video might enter the prompt, "Create a 2-minute video about the adventure of travel, conveying the fun," and the server will edit the relevant scenes and add the most suitable soundtrack.

[0662] Example of a prompt:

[0663] "Create an adventurous 2-minute video."

[0664] "Edit it into a touching one-minute video."

[0665] This invention makes it possible to provide an environment where anyone can easily perform professional video editing, even without specialized knowledge.

[0666] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0667] Step 1:

[0668] The user selects video data on their device and enters prompts regarding editing. For example, the user might enter "Edit this into an emotional 1-minute video." This input is converted into a video file and natural language instructions, which are then sent to the server.

[0669] Step 2:

[0670] The terminal sends a video file and editing instructions to the server via a communication method. The server receives the data and prepares the video file for analysis. The input is the video file and prompt text, and the output is the data information to be analyzed.

[0671] Step 3:

[0672] The server uses analysis tools to analyze video data and extract visual features from each frame. Computer vision technology is used to identify important scenes and pass that information to the editing tools. The input is each frame of the video, and the output is the identified important scene information.

[0673] Step 4:

[0674] The server uses editing tools to perform edits based on user prompts. Specifically, it adds optimal music to pre-identified important scenes and applies specified visual effects. A generative AI model is used for music selection and effect addition. The input consists of important scene information and prompts, and the output is the edited video data.

[0675] Step 5:

[0676] The server uses tools like FFmpeg to convert the format of the edited video and encode it in a format playable on the user's terminal. The input is the edited video data, and the output is the encoded video file.

[0677] Step 6:

[0678] The encoded video file is sent back to the user's terminal via the transmission method. The user can review the final video on the terminal and, if necessary, give instructions for further editing. The input is the encoded video file, and the output is the final video viewable by the user.

[0679] 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.

[0680] The system of the present invention is implemented through a process in which video data is received from a user terminal and the video is optimally edited by a server equipped with an emotion engine that analyzes user instructions. This system includes communication means, analysis means, emotion engine, editing means, and transmission means.

[0681] Users can use a smartphone application to select a video they want to edit and express their mood and intentions when specifying the desired editing style in natural language. For example, they can give instructions such as, "I want to reminisce about happy memories, so I'd like it to have a bright and happy atmosphere."

[0682] The terminal sends the selected video file and user instruction data to the server. The server analyzes the received data, and first, the emotion engine extracts emotions from the user's instructions. This emotion information is then considered in the subsequent video editing process.

[0683] The server's analysis tools analyze the video content and identify important scenes. Based on the results of the emotion engine, the editing policy for the identified scenes is determined. For example, if the user indicates that they are "happy," the server will select music with an optimistic tone and edit the video to emphasize cheerful scenes.

[0684] Once editing is complete, the server encodes the video in the desired format and sends it to the user's terminal. The terminal saves the video and notifies the user of the result.

[0685] In this way, users can easily create emotionally customized videos without requiring advanced editing skills. Furthermore, the emotion engine can learn the user's preferences by referencing their past selection history, enabling it to provide even more personalized edits.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] The user selects the video they want to edit using a smartphone application and inputs their editing style and intentions in natural language. For example, they might give instructions such as, "Emphasize important moments with an emotional atmosphere."

[0689] Step 2:

[0690] The terminal combines the video file selected by the user and the instruction information into a data packet and sends it to the server. Data transmission is performed securely.

[0691] Step 3:

[0692] The server analyzes the received video data and user input. First, the emotion engine recognizes emotions from the user's instructions and extracts psychological elements related to the requested editing style.

[0693] Step 4:

[0694] The server's analysis tools analyze the video content, segment each scene, and extract features. This analysis determines which scenes are important.

[0695] Step 5:

[0696] The server's editing tools initiate the video editing process based on recognized user emotions and data on key scenes. If an emotionally impactful edit is requested, the server selects emotional music and transition effects to highlight key moments.

[0697] Step 6:

[0698] The server encodes the edited video and generates a file in the user-specified format. Care is taken during this process to avoid compromising the quality of the video content.

[0699] Step 7:

[0700] The server sends the completed edited video to the user's terminal, which saves the video to its internal storage. The user is notified when the video is completed and saved, and can play it at any time.

[0701] Step 8:

[0702] Users can review the edited video on their device and, if further changes are needed, enter new instructions to request re-editing.

[0703] (Example 2)

[0704] 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".

[0705] In today's world, users are expected to generate individually customized videos based on their own emotions and intentions, without requiring specialized technical knowledge. However, traditional methods have made it difficult to accurately reflect user emotions and edit them efficiently. Solutions are needed to address the technical challenges of extracting emotions and performing appropriate video editing.

[0706] 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.

[0707] In this invention, the server includes means for receiving video data, means for analyzing the received video data and explicit instructions from the user, and means for extracting the user's emotions based on the analysis results. This enables individually customized emotion-based video editing.

[0708] "Video data" refers to digital files in electronic format that contain visual and audio information, including video files and associated metadata.

[0709] "Means" refers to the implements used to achieve a specific purpose, and in this specification, it refers to the individual functional parts or devices that constitute a system.

[0710] "Receiving" refers to the process by which a terminal or server takes in data from an external source, and is a process carried out using data communication technology.

[0711] "Analyzing" refers to the process of examining and understanding data in detail to extract specific information or patterns, and this often involves algorithms and modeling techniques.

[0712] "Emotion" refers to the subjective experiences and feelings that a user has, and indicates the psychological state generated in response to a specific input.

[0713] "Encoding" refers to the process of converting digital data according to a specific format or standard, and is a technology used to efficiently store or transmit data.

[0714] "Individually customized" means that the settings and specifications have been adjusted to suit a specific user or scenario.

[0715] An "editorial policy" refers to the operations and procedures taken regarding the processing and composition of video data, and is a set of standards determined based on specific objectives and indicators.

[0716] The system of the present invention enables emotion-based video editing through the collaborative operation of the user, terminal, and server.

[0717] The user launches a dedicated application using their smartphone or computer. From this application, they select the video data they want to edit and provide instructions in natural language regarding the emotion and editing style. For example, they might input, "I want a fun video with lots of children's smiles." This instruction is then used as a prompt.

[0718] The terminal sends the video data selected by the user and the input instructions to the server. Data transmission is performed using a secure communication protocol.

[0719] The server launches a generative AI model to analyze the received data. This model extracts emotions from user instructions and performs video data analysis. Computer vision technology is used for the analysis, specifically the OpenCV library for scene analysis. Based on the extracted emotions, the video data is processed using video editing software such as the Adobe Premiere Pro API.

[0720] Music selection and scene emphasis are made to match the emotions, and the edited video data is encoded. Encoding tools such as FFmpeg are used for this encoding process. The encoded data is then sent back to the terminal using a secure communication protocol.

[0721] The terminal saves the received video data and notifies the user when editing is complete. In this way, users can easily create customized videos that match their own emotions. The server can also learn from the user's past selection history and suggest more personalized editing strategies.

[0722] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0723] Step 1:

[0724] The user opens an application on their smartphone or computer and selects the video data they want to edit. Next, they provide input in natural language, specifying the editing style and mood they want to convey. This input is passed to the system as a prompt. The input data consists of the selected video file and the user's instructions.

[0725] Step 2:

[0726] The terminal sends video data and prompt messages from the user to the server. This transmission uses a secure communication protocol such as HTTPS. Input data consists of video files and prompt messages, while output is data transfer to the server.

[0727] Step 3:

[0728] The server extracts the user's sentiment by analyzing the received prompt text. This analysis uses a generative AI model. It receives prompt text as input and generates sentiment tag information as output. Sentiment tags are used as indicators in the editing process.

[0729] Step 4:

[0730] The server's analysis tool analyzes the video data frame by frame to identify key scenes and objects. Computer vision technology is used, specifically leveraging the OpenCV library. The input is video data, and the output is data of the identified important scenes.

[0731] Step 5:

[0732] The server processes the video based on extracted emotions and identified scenes. Specifically, it adds music and filters using the Adobe Premiere Pro API, among others. It also applies video transition effects to match the scenes. The input is emotion tags and important scene data, and the output is the edited video data.

[0733] Step 6:

[0734] The server encodes the edited video data. This process is performed using an encoding tool such as FFmpeg. The input is the edited video data, and the output is an encoded video file.

[0735] Step 7:

[0736] The server sends the encoded video file to the terminal. Again, the data is transferred via a secure communication protocol. The input is the encoded video file, and the output is a message indicating that transmission to the terminal is complete.

[0737] Step 8:

[0738] The terminal saves the received video file to its internal storage. After saving, it notifies the user that editing is complete and makes the video available for review. The input is the video file received from the server, and the output is the notification to the user.

[0739] (Application Example 2)

[0740] 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".

[0741] There is a need for a system that allows users to edit videos they have shot more easily and in a way that reflects their emotions and intentions. Conventional editing systems require users to have high skill levels, making it difficult to easily edit videos that reflect their emotions.

[0742] 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.

[0743] In this invention, the server includes communication means for receiving video data, analysis means for analyzing the content of the received video data and identifying scenes, emotion analysis means for analyzing user instructions using natural language processing technology and extracting emotions, editing means for selecting music and filters based on the emotions extracted by the emotion analysis means and editing the video, and transmission means for sending the edited video data to the user terminal. This makes it possible for users to edit videos that match their emotions and intentions without requiring specialized technical skills.

[0744] "Video data" refers to a file format that contains video information, and specifically to video content recorded by a user.

[0745] "Communication means" refers to a method or technology for receiving or transmitting data through a network.

[0746] "Analysis means" refers to a method of performing a process to analyze the content of received data and identify specific elements or patterns.

[0747] "Emotion analysis method" refers to a method of extracting emotions from user instructions using natural language processing technology.

[0748] "Editing" refers to the process of adding music or filters to a video and making visual or auditory changes based on the analysis results and sentiment analysis results.

[0749] "Transmission means" refers to a method or technology for transferring edited video data to a user's terminal in digital format.

[0750] "Natural language processing technology" is computer technology used to understand and analyze human language.

[0751] "Emotions" refer to the inner feelings and intentions that can be gleaned from the user's instructions.

[0752] In this application example, a consumer robot is implemented as a system that automatically edits videos in the home. The server is equipped with communication means to receive video data from user terminals via a network. This data is analyzed using analysis means to identify important scenes and features.

[0753] The robot uses emotion analysis techniques that leverage natural language processing technology to extract emotions from text instructed by the user. For emotion analysis, it employs a natural language processing library (e.g., spaCy). For example, if the user instructs, "I want to watch my pet's daily life in a fun way," the robot will select cheerful music and filters to create a pleasant atmosphere.

[0754] The editing process involves using a video editing library (e.g., FFMpeg) to apply music and filters selected based on sentiment analysis results to the video. The edited video is then encoded and sent to the user's device for easy viewing.

[0755] For example, if a user requests "a compilation of family holiday footage with a happy atmosphere," the emotion analysis tool will extract the emotion of "happiness," and the editing process will add upbeat music and edit the footage to highlight smiles and enjoyable scenes.

[0756] An example of a prompt message is: "Please explain in detail how to edit a video based on the emotions specified by the user. For example, use a video of a birthday party that shows many children smiling."

[0757] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0758] Step 1:

[0759] The user selects the video to be edited using their device and provides instructions in natural language regarding the editing style and intent. These instructions and the video data are sent to the server. The input is the user's instructions and video data, and the output is the data transfer to the server.

[0760] Step 2:

[0761] The server acquires received video data using communication means and analyzes the video content through analysis means. The input is the received video data, and the server recognizes scenes within the video and identifies important scenes. The output is data of the important scenes.

[0762] Step 3:

[0763] The server performs sentiment analysis on user instructions using a natural language processing engine (e.g., spaCy). The input is the user's instructions, and the sentiment analysis tool extracts emotions such as joy, sadness, and excitement. The output is the analyzed sentiment data.

[0764] Step 4:

[0765] The server-based editing system uses a video editing library (e.g., FFMpeg) to determine the tone of the video based on emotional data. Input consists of data from key scenes and emotional data, and appropriate music and filters are selected and applied. The output is the edited video data.

[0766] Step 5:

[0767] The edited video data is encoded by the server and converted to the optimal format. The input is the edited video data, and the output is the encoded video file.

[0768] Step 6:

[0769] Finally, the server sends the encoded video file back to the user's terminal via a transmission method. The user can then view the result on their terminal and save the video. The input is the encoded video data, and the output is the video file sent to the user.

[0770] 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.

[0771] 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.

[0772] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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."

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0791] The following is further disclosed regarding the embodiments described above.

[0792] (Claim 1)

[0793] A communication method for receiving video data,

[0794] An analysis means for analyzing the content of received video data and identifying scenes,

[0795] An editing method that edits videos based on analyzed scenes, based on user instructions,

[0796] A transmission means for sending edited video data to the user's terminal,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, wherein the analysis means analyzes the video content using computer vision technology and selects important scenes.

[0800] (Claim 3)

[0801] The system according to claim 1, wherein the editing means generates and inserts a text overlay onto the video based on user instructions.

[0802] "Example 1"

[0803] (Claim 1)

[0804] Information and communication means for receiving video data,

[0805] An analysis means for analyzing the content of received video data and identifying scenes,

[0806] A means of constructing a video based on the analyzed scenes according to linguistic instructions,

[0807] A means for transmitting the configured video data to the terminal,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, wherein the analysis means analyzes the video content using computer vision technology and selects important scenes.

[0811] (Claim 3)

[0812] The system according to claim 1, wherein the configuration means selects music based on language instructions and adds it to a video.

[0813] "Application Example 1"

[0814] (Claim 1)

[0815] A means of receiving video information,

[0816] An analysis means for analyzing the contents of received video information and identifying scenes,

[0817] An editing method that adds music appropriate to the analyzed scene based on user instructions and edits the video,

[0818] A transmission method for sending edited video information to the user's terminal,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, wherein the analysis means analyzes the content of a video using computer vision technology and selects important scenes.

[0822] (Claim 3)

[0823] The system according to claim 1, wherein the editing means adds music or slow-motion effects that match the video based on the user's instructions.

[0824] "Example 2 of combining an emotion engine"

[0825] (Claim 1)

[0826] A means of receiving video data,

[0827] A means for analyzing received video data and explicit instructions from the user,

[0828] A means of extracting user emotions based on the analysis results,

[0829] A means of processing video data based on extracted emotions,

[0830] A means for encoding processed video data and transmitting it to a user device,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, wherein the analysis means analyzes the video content using a machine learning model and selects important scenes.

[0834] (Claim 3)

[0835] The system according to claim 1, wherein the emotion extraction means refers to the user's past selection history and determines an individually customized editing policy.

[0836] "Application example 2 when combining with an emotional engine"

[0837] (Claim 1)

[0838] A communication method for receiving video data,

[0839] An analysis means for analyzing the content of received video data and identifying scenes,

[0840] A sentiment analysis method that analyzes user instructions using natural language processing technology and extracts emotions,

[0841] An editing tool that selects music and filters based on emotions extracted by emotion analysis tools and edits the video,

[0842] A transmission means for sending edited video data to the user's terminal,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the analysis means analyzes the video content using machine vision technology and selects important scenes.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the editing means controls a household automatic device to edit a video based on user instructions. [Explanation of symbols]

[0848] 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 of receiving video data, An analysis means for analyzing the content of received video data and identifying scenes, An editing method that edits videos based on analyzed scenes, based on user instructions, A transmission means for sending edited video data to the user's terminal, A system that includes this.

2. The system according to claim 1, wherein the analysis means analyzes the video content using computer vision technology and selects important scenes.

3. The system according to claim 1, wherein the editing means generates and inserts a text overlay onto the video based on user instructions.

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