System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving an unedited video; means for receiving a prompt entered by a user; means for parsing the prompt to generate editing parameters; means for applying the generated editing parameters to edit the video; and means for outputting the edited video.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, demand for video content has been rapidly increasing, but many video providers have difficulty creating high-quality video content due to the time and effort required for editing and a lack of specialized knowledge. Therefore, there is a demand for a system that allows users to easily create high-quality videos. Furthermore, if the video content provided does not match the viewer's attributes or expectations, there is also the problem of reduced viewer satisfaction. The present invention aims to solve these problems. [Means for solving the problem]

[0005] The system of the present invention includes means for receiving unedited video, means for receiving prompts entered by a user, means for analyzing the prompts to generate editing parameters, means for editing the video by applying the generated editing parameters, and means for outputting the edited video. The system may further include means for analyzing viewer attributes of content based on the prompts and feeding back the results, and means for integrating multiple editing parameters to generate video content optimal for the prompts. This allows users, even without specialized knowledge, to easily create high-quality video content optimized for viewer attributes.

[0006] "Unedited video" refers to video data in its original recorded state that has not been edited at all.

[0007] "User prompts" are instructions that a user inputs to the system, such as desired video editing and target audience.

[0008] "Editing parameters" are a set of specific settings and instructions used in video editing, obtained as a result of analyzing the prompts.

[0009] "Means for editing video" refers to functions and algorithms that perform editing processes such as cutting, adding subtitles, and adjusting audio on video based on editing parameters.

[0010] "Means for receiving video" refers to the functions and devices for importing unedited video data provided from outside into the system.

[0011] "Means for receiving a prompt" refers to a function or device for receiving and storing prompt information input by a user within the system.

[0012] "Means for parsing prompts" refers to analysis functions or algorithms for generating appropriate editing parameters based on received prompt information.

[0013] "Means for outputting video" refers to a function or device for providing the user with video data after editing.

[0014] "Means for analyzing viewer attributes" refers to functions and algorithms for analyzing viewer interests and attributes based on data such as video content and viewing history.

[0015] "Means of providing feedback" refers to the functions and processes for returning the analysis results of viewer attributes to users. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The system of the present invention allows video providers to upload unedited videos and perform prompt input to automatically generate high-quality video content. Specific program processing of this system will be explained below in natural language.

[0038] Video upload

[0039] Users upload raw video files to the system, which are then stored on the server as is. Uploading a video by a user is the first step in providing a video to the system.

[0040] Prompt Input

[0041] Users input specific prompts about their desired video editing style and target audience, such as "suitable for 30-year-old women," "style of a popular video creator," or "add English subtitles." The prompts are then sent to the server.

[0042] AI-powered analysis

[0043] When the server receives a prompt from the user, it sends it to a multimodal AI for analysis. The AI ​​generates appropriate editing parameters based on the prompt information. These editing parameters include video effects, subtitles, and audio adjustments.

[0044] Video editing

[0045] The server uses the generated editing parameters to edit the uploaded video. Specifically, it performs operations such as cutting and transitioning the video, adding subtitles, and adjusting the audio. All edits are applied automatically, allowing users to enjoy high-quality video content without any effort.

[0046] Final Output

[0047] The server generates the edited video content and provides the download link to the user. It can also provide the user with feedback on the video content based on the analysis results of viewer attributes. This allows the user to check viewer reactions and use them to improve their next content creation.

[0048] Specific examples

[0049] For example, when a user creates "educational content for children," the following steps are taken.

[0050] 1. Users upload unedited "educational materials for children" to the system.

[0051] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[0052] 3. The server analyzes the prompts and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[0053] 4. The server edits the video using the editing parameters to generate the completed educational content.

[0054] 5. The server provides the user with a download link for the edited video and provides feedback on the analysis results of viewer attributes.

[0055] In this way, the system of the present invention helps users easily create high-quality video content that is optimized for their target audience.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] A user logs into the system and accesses the video upload page, where the user selects an unedited video file and begins uploading.

[0059] Step 2:

[0060] The device sends the selected video file to the server, and once the upload is complete, the server temporarily stores the received video file.

[0061] Step 3:

[0062] The user navigates to a prompt entry page and enters specific instructions about their video editing preferences and target audience (e.g., "suitable for women in their 30s," "style of popular video creators," "add English subtitles"), and then sends the prompt to the server.

[0063] Step 4:

[0064] The server receives prompt information sent by the user and sends it to the multimodal AI for analysis. The prompt information includes the style of the video content, the target audience, and the elements that the user wants to add.

[0065] Step 5:

[0066] AI analyzes the prompt information and generates optimal editing parameters, including video cut positions, subtitles, audio adjustments, and effects.

[0067] Step 6:

[0068] The server automatically edits raw video files using AI-generated editing parameters, such as cutting the video at specified points, adding captions based on prompts, and adjusting the volume and intonation of the audio.

[0069] Step 7:

[0070] The server generates and stores the video file after the editing process is completed.

[0071] Step 8:

[0072] The server creates a download link for the edited video file and notifies the user of this link. It also provides the user with analysis results based on viewer attributes as feedback. Users can use this information to improve their content and create their next video.

[0073] Example 1

[0074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0075] In today's world, video content is an important means of disseminating information and providing entertainment. However, editing unedited videos to high quality requires specialized knowledge and time. Editing videos that accurately reflect the user's intentions is particularly difficult, and many users are unable to achieve high-quality video editing. Analyzing viewer reactions and obtaining feedback that can be used for future edits is also challenging. There is a need for a system that can solve these problems, allowing users to easily create high-quality video content and receive effective feedback.

[0076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0077] In this invention, the server includes a means for receiving unedited video, a means for receiving prompts entered by a user, a means for transmitting the prompts to a multimodal AI model, analyzing the prompts, and generating editing parameters, a means for editing the video by applying the generated editing parameters, and a means for outputting the edited video. This allows a user without specialized knowledge to automatically generate high-quality video content simply by entering prompts. Furthermore, by including a means for analyzing viewer attributes and providing feedback to the user, the user can make effective edits based on viewer reactions, thereby continuously improving the quality of the video content.

[0078] "Unedited video" refers to the original video file that has not been subjected to any specific processing or editing.

[0079] A "prompt" is a sentence or keyword entered by a user to describe their video editing needs, requirements, and specific instructions regarding the target audience.

[0080] A "multimodal AI model" is an artificial intelligence model that integrates and analyzes data of different formats and types to generate appropriate editing parameters.

[0081] "Editing parameters" refer to specific operations and setting items designated in video editing, and include, for example, cuts, effects, subtitles, and audio adjustments.

[0082] "Video editing means" refers to the technology and process for processing video files based on editing parameters and generating high-quality video content.

[0083] "Viewer attributes" refers to information about the viewer of a video, including age, gender, viewing time, viewing device, etc.

[0084] "Feedback" is information that provides users with analysis results based on viewer reactions and attributes, and is used to help them edit their next video.

[0085] The system of the present invention allows a video provider to upload unedited videos and input prompts to automatically generate high-quality video content. An embodiment of this system will be described in detail below.

[0086] Video upload

[0087] Users first upload unedited video files to the system, which then send them from their devices to the server, where they are stored in secure storage. This is typically done using HTTP / HTTPS protocols.

[0088] Prompt Input

[0089] Next, users input prompts about their video editing preferences and target audience, such as "for women in their 30s," "in the style of a popular video creator," or "add English subtitles." The prompts are then sent from the device to the server.

[0090] Prompt parsing and editing parameter generation

[0091] The server sends the received prompts to a multimodal AI model for analysis, which has the ability to integrate and analyze data of different formats and types, and generates specific editing parameters based on the prompt information.

[0092] Video editing

[0093] The server uses the generated editing parameters to edit the video. The editing process is automated and includes cutting the video, adding transitions, effects, and adjusting the audio. This is typically done using video editing software such as FFmpeg.

[0094] Outputting edited videos

[0095] Once editing is complete, the server generates a download link for the video and provides it to the user, who can then download the edited, high-quality video content from the provided link.

[0096] Audience analysis and feedback

[0097] This system also has a function for analyzing viewer attributes. The server collects information such as the viewer's age, gender, viewing time, and viewing device, and uses this information to analyze viewer attributes. The analysis results are provided to the user as feedback information, which can be used to help with the next video editing.

[0098] Specific examples

[0099] For example, if a user wants to create "educational content for kids", the following steps would occur:

[0100] 1. The user uploads unedited "educational materials for children" to the system via their browser.

[0101] 2. The user enters "For children, fun and educational style, English subtitles added" into the input field as a prompt.

[0102] 3. The server sends the prompt to the multimodal AI model and receives the analysis results.

[0103] 4. The server edits the video using the generated editing parameters. Video editing software such as FFmpeg is used.

[0104] 5. The server generates a download link for the edited video and notifies the user.

[0105] 6. The server provides the analysis results based on viewer attributes as feedback to the user.

[0106] This system not only enables users to easily generate high-quality video content optimized for their target audience without any specialized knowledge, but also provides valuable feedback based on viewer attributes.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1:

[0109] The user selects an unedited video file on their device and uploads it to the system. The uploaded video file is sent to the server using HTTP / HTTPS protocol. The server stores the received video file in secure storage.

[0110] Input: Unedited video file

[0111] Output: Video file saved on the server

[0112] Specific behavior:

[0113] 1. The user selects a video file from a browser or a dedicated application.

[0114] 2. The device sends the video file to the server.

[0115] 3. The server receives the video file and stores it on the storage server.

[0116] Step 2:

[0117] The user inputs prompts about their desired video editing style and target audience from their device and sends them to the server, which includes specific instructions.

[0118] Input: prompt statement

[0119] Output: The prompt sent to the server

[0120] Specific behavior:

[0121] 1. The user enters a prompt sentence into an input field in a browser or dedicated application.

[0122] 2. The terminal sends the entered prompt text to the server.

[0123] Step 3:

[0124] The server sends the prompt to the multimodal AI model, which analyzes the prompt and generates the necessary editing parameters, including video cuts, effects, subtitles, and audio adjustments.

[0125] Input: prompt statement

[0126] Output: Edit parameters

[0127] Specific behavior:

[0128] 1. The server retrieves the prompt sentence and sends it to the multimodal AI model.

[0129] 2. The multimodal AI model analyzes the prompt and generates editing parameters.

[0130] 3. The server receives the generated editing parameters and saves them in its internal storage.

[0131] Step 4:

[0132] The server uses the editing parameters to edit the uploaded video. Editing involves cutting the video, adding transitions, adding effects, adjusting the audio, etc. This is done using video editing software such as FFmpeg.

[0133] Input: Editing parameters, raw video file

[0134] Output: Edited video file

[0135] Specific behavior:

[0136] 1. The server opens the video file using video editing software (e.g. FFmpeg).

[0137] 2. The server performs specific editing tasks based on the editing parameters.

[0138] 3. Save the edited video file as a new file.

[0139] Step 5:

[0140] The server generates a download link for the edited video content, which is then provided to the user, who uses it to download the edited video.

[0141] Input: Edited video file

[0142] Output: Download link

[0143] Specific behavior:

[0144] 1. The server saves the edited video file in a specific directory and generates a download URL.

[0145] 2. The server notifies the user of the generated URL.

[0146] Step 6:

[0147] The server provides the user with the analysis results of viewer attributes as feedback. Viewing data is collected and analyzed to analyze viewer attributes such as age, gender, viewing time, and viewing device.

[0148] Input: Viewing data

[0149] Output: Viewer attribute analysis results

[0150] Specific behavior:

[0151] 1. The server collects viewing data.

[0152] 2. The server runs an algorithm to analyze viewer attributes.

[0153] 3. The server generates the analysis results in the form of a report and provides it to the user.

[0154] This allows users to easily create high-quality video content and obtain valuable feedback based on viewer reactions.

[0155] (Application example 1)

[0156] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0157] Modern video content creation requires efficient editing of raw material into high-quality videos. However, traditional video editing processes are time-consuming and labor-intensive, especially when editing tasks require prompt input. To address this issue, there is a growing need for systems that use voice recognition interfaces and visual display devices to enable prompt input and confirmation of editing results in real time.

[0158] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0159] In this invention, the server includes means for receiving unedited video, means for receiving prompts input by a user, means for analyzing the prompts to generate editing parameters, means for editing the video by applying the generated editing parameters, means for outputting the edited video, means for inputting prompts using a voice recognition interface, and means for checking the editing results in real time on a visual display device, thereby enabling users to easily create high-quality video content.

[0160] "Unedited video" refers to video material that has been shot by a user without any editing, in its original form.

[0161] A "prompt" is a command that a user enters in text or voice to provide specific instructions about their video editing preferences or target audience.

[0162] "Editing parameters" are specific settings and operation information for video editing that are generated by AI based on prompts.

[0163] A "voice recognition interface" is a device or function that converts a user's speech into text in real time and inputs it into a system.

[0164] A "visual display device" is a device that uses smart glasses or other display devices to visually display the content of edited video in real time.

[0165] "Real-time" refers to processing and display with no or very little delay.

[0166] "Viewer attributes" refers to characteristic information such as age, sex, and interests of users who view video content.

[0167] The "server" is a central computing device for processing the entire system, and is a device that uploads videos, analyzes prompts, generates editing parameters, and provides edited videos.

[0168] A system for implementing the present invention is configured as follows.

[0169] Overall system configuration

[0170] 1. Video Upload:

[0171] Users use smart glasses or other visual display devices to capture unedited videos, which are then automatically uploaded to a server.

[0172] 2. Prompt input:

[0173] Using the voice recognition interface on the smart glasses, users can speak prompts related to editing preferences and target audiences, such as "Fun and educational for kids, with English subtitles." This speech is converted into text in real time and sent to the server as prompts.

[0174] 3. AI-powered prompt analysis and editing parameter generation:

[0175] The server uses a generative AI model to analyze the received prompts. This model generates optimal editing parameters based on the input prompts, including color adjustments, adding effects, and subtitle placement.

[0176] 4. Video editing:

[0177] The server applies the generated editing parameters to automatically edit the uploaded raw video, using a video editing library such as MoviePy to add transitions, captions, and adjust the audio.

[0178] 5. Visual confirmation of edit results:

[0179] Once editing is complete, the server streams the edited video in real time to a visual display device such as smart glasses, where the user can view it and, if desired, voice further prompts to request additional edits.

[0180] 6. Final output and feedback:

[0181] If the user is satisfied with the edited results, the server saves the edited video as the final output and provides a download link. It also provides the user with analysis results of viewer attributes as feedback to help them create content next time.

[0182] Specific examples

[0183] For example, consider entering the prompt "Fun and educational for kids, with English subtitles."

[0184] Video upload:

[0185] Users take photos of unedited "educational materials for children" using smart glasses and upload them to the server.

[0186] Prompt Input:

[0187] Using the voice recognition interface, the user can say, "Kids-oriented, fun and educational style, English subtitles added."

[0188] AI-powered prompt analysis and editing parameter generation:

[0189] Based on the prompts received, the server generates editing parameters such as optimal colors and character effects for children, fun background music, and the addition of English subtitles.

[0190] Video editing:

[0191] The server uses editing parameters and utilizes the "MoviePy" library to automatically perform video cuts, transitions, add subtitles, adjust audio, and more.

[0192] Visual confirmation of edit results:

[0193] Through the smart glasses, the user can view the edited video in real time and input additional editing instructions by voice if necessary.

[0194] Final output and feedback:

[0195] The server will then publish the final educational content and provide feedback on viewer attributes.

[0196] In this way, the system is designed to enable users to easily create high-quality video content optimized for their target audience.

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] Video recording and uploading

[0200] A user wearing the smart glasses shoots unedited video, and after shooting is complete, the software in the smart glasses automatically uploads the video file to a server.

[0201] Input: Unedited video files captured by smart glasses.

[0202] Output: Raw video files saved on the server.

[0203] Step 2:

[0204] Speech-to-text prompts

[0205] Using a voice recognition interface built into the smart glasses, users can speak their editorial preferences and prompts regarding target audiences, which are converted into text in real time and sent to the server.

[0206] Input: Voice prompts.

[0207] Output: Prompt data in text format.

[0208] Step 3:

[0209] Prompt parsing and editing parameter generation

[0210] The server receives the text prompts and uses a generative AI model to analyze them and generate editing parameters, which determine appropriate settings for captions, effects, audio adjustments, and more.

[0211] Input: Prompt data in text format.

[0212] Output: Edit parameters.

[0213] Step 4:

[0214] Video editing

[0215] The server uses the generated editing parameters to edit the uploaded raw video, using a video editing library such as MoviePy to add transitions, insert subtitles, synthesize music, and more.

[0216] Input: raw video file, editing parameters.

[0217] Output: Edited video file.

[0218] Step 5:

[0219] Real-time visual confirmation of edit results

[0220] The edited video is streamed in real time to a visual display device such as smart glasses, where the user can view it and provide additional voice-activated editing instructions as needed.

[0221] Input: Edited video file.

[0222] Output: Visual confirmation and further instructions for the user.

[0223] Step 6:

[0224] Final output and feedback provided

[0225] If the user is satisfied with the edited results, the server will save the edited video and provide a download link to the user. At the same time, feedback based on the analysis of viewer attributes will be provided, providing useful information for future content creation.

[0226] Input: Edited video files, viewer demographic data.

[0227] Output: Final edited video, download link, feedback.

[0228] The above processing steps realize a system that allows a user to easily generate high-quality video content and check and modify it in real time through a visual display device.

[0229] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0230] The system of the present invention allows video providers to upload unedited videos, and automatically generates high-quality video content by combining prompt input and emotion recognition using an emotion engine. The specific program processing of this system is explained below in natural language.

[0231] Video upload

[0232] After logging in to the system, users upload unedited video files. The uploaded video files are stored on the server as is. This is the first step in providing videos to the system.

[0233] Prompt Input

[0234] Users input specific prompts about their video editing preferences and target audience, such as "suitable for women in their 30s," "style of popular video creators," "add English subtitles," etc. This prompt information is sent to the server.

[0235] Emotion recognition by emotion engine

[0236] The device uses an emotion engine to recognize the user's emotions when inputting prompts. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, etc., and sends the results to the server.

[0237] AI-powered analysis

[0238] The server receives prompt information from the user and emotion recognition results from the emotion engine, and analyzes them using multimodal AI. The AI ​​generates editing parameters based on this information. These editing parameters include video effects, subtitles, audio adjustments, and effects.

[0239] Video editing

[0240] The server automatically edits the uploaded video file using the generated editing parameters. Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. Furthermore, the server fine-tunes the editing parameters based on the user's emotions to achieve the optimal editing results.

[0241] Final Output

[0242] The server generates the edited video content and provides the download link to the user. It also provides the user with analysis results on viewer attributes as feedback, allowing the user to check viewer reactions and use them to improve their next content creation.

[0243] Specific examples

[0244] For example, when a user creates "educational content for children," the following steps are taken.

[0245] 1. Users upload unedited "educational materials for children" to the system.

[0246] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[0247] 3. The device's emotion engine analyzes the user's emotions when entering the prompt and sends the emotion data to the server.

[0248] 4. The server analyzes the prompt information and emotional data and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[0249] 5. The server edits the video using the editing parameters to generate the completed educational content.

[0250] 6. The server provides the user with a download link for the edited video and provides analysis results on viewer attributes as feedback.

[0251] In this way, the system of the present invention helps users easily create high-quality, emotion-based, optimized video content.

[0252] The processing flow will be explained below.

[0253] Step 1:

[0254] The user logs into the system, accesses the upload page, selects the unedited video file, and clicks the "Upload" button.

[0255] Step 2:

[0256] The device sends the selected video file to the server, where it is temporarily stored.

[0257] Step 3:

[0258] Users access the prompt input page and enter specific instructions for video editing (e.g., "Suitable for a 30-year-old woman," "Style of a popular video creator," "Add English subtitles"), then click the "Submit" button.

[0259] Step 4:

[0260] The device's emotion engine analyzes data such as the user's facial expression and tone of voice when entering prompts, and the analyzed emotion data is sent to the server.

[0261] Step 5:

[0262] The server receives prompt information and emotional data sent by the user, which is then sent to a multimodal AI for analysis.

[0263] Step 6:

[0264] AI analyzes the prompt information and emotional data to generate appropriate editing parameters, including video cut positions, subtitles, audio adjustments, and effects.

[0265] Step 7:

[0266] The server then automatically edits the raw video file using the generated editing parameters, such as cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio.

[0267] Step 8:

[0268] The server generates a video file after the editing process is completed and stores it on the server.

[0269] Step 9:

[0270] The server generates a download link for the edited video file and notifies the user of this link.

[0271] Step 10:

[0272] The server provides the user with analysis results based on viewer attributes as feedback, allowing the user to use this information to improve their content and create their next video.

[0273] Example 2

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

[0275] In recent years, with the spread of video sharing platforms, there has been a growing need for many users to easily edit and publish their own videos. However, video editing requires specialized knowledge and skills, making it a difficult task for many users. Furthermore, in order to provide optimal content to viewers, editing must take into account the viewer's attributes and emotions, but this is extremely difficult to do manually. To solve these challenges, a system is needed that allows anyone to easily create high-quality video content.

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

[0277] In this invention, the server includes means for receiving unedited video, means for receiving prompts entered by a user, means for analyzing the prompts and generating editing parameters, means for editing the video by applying the generated editing parameters, means for outputting the edited video, means for recognizing the user's emotions, means for analyzing the recognized emotions and reflecting them in the editing parameters, and means for analyzing viewer attributes and feeding the results back to the user. This enables even users without specialized knowledge to generate high-quality video content that reflects viewer attributes and user emotions.

[0278] "Unedited video" refers to video data in its original state without any editing or processing.

[0279] A "prompt" refers to a sentence or keyword that a user enters regarding a specific instruction or request.

[0280] "Editing parameters" refers to data that includes specific editing instructions, such as where to cut a video, adding subtitles, adjusting the audio, etc., that are generated based on prompts and user emotion data.

[0281] "Means for editing videos" refers to a series of processes for automatically processing and correcting video files based on the generated editing parameters.

[0282] "Means for recognizing emotions" refers to technologies and devices that collect and analyze data such as a user's facial expressions and tone of voice to identify the user's emotional state.

[0283] "Viewer attributes" refers to characteristic information such as the age, gender, and interests of the video viewer.

[0284] "Means for analyzing viewer attributes" refers to technologies and algorithms that analyze viewer characteristics and use the results to understand viewer interests and demands.

[0285] "Means for analyzing the user's emotions and reflecting them in the editing parameters" refers to a process for adjusting the editing parameters based on the recognized emotion data to perform optimal video editing.

[0286] The present invention relates to a system for automatically generating high-quality video content based on a user's prompt input and emotion data after receiving unedited video. Specific embodiments of the present invention will be described below.

[0287] A user logs in to the system and uploads an unedited video file, which is then stored on the server and used for subsequent editing.

[0288] Next, the user inputs prompts, including specific requests for editing the video and instructions about the target audience. For example, they might input prompts such as "suitable for women in their 30s," "style of a popular video creator," or "add English subtitles." The prompt information is sent to the server, where it is analyzed and stored in a database.

[0289] The device uses a camera and microphone to capture the user's facial expressions and tone of voice as they input prompts. The captured data is analyzed by an emotion engine to generate user emotion data, which is then sent to the server.

[0290] The server integrates the received prompt information and emotional data and performs analysis using a multimodal generative AI model (e.g., a natural language processing model or an image analysis model). The AI ​​model generates editing parameters based on the input data. These editing parameters include video cut positions, adding subtitles, adjusting audio, etc. The generated editing parameters are stored in a database.

[0291] The server then begins editing the uploaded video file based on the generated editing parameters. This editing process is performed using video editing software (e.g., Adobe Premiere Pro API or FFmpeg). Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. Additionally, the editing parameters are fine-tuned as needed to reflect the user's emotional data.

[0292] Finally, the server generates the edited video and provides the user with a download link. The server also analyzes viewer attributes and provides the results as feedback to the user, allowing the user to check viewer reactions and use them to improve their next content creation.

[0293] As a specific example, if a user wants to create "educational content for children," he or she uploads unedited "educational materials for children" and enters "kids-oriented, fun learning style, add English subtitles" into the prompt. The device's emotion engine analyzes the user's emotion when entering the prompt and sends the emotion data to the server. The server analyzes the prompt information and emotion data, generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles, and edits the video. The server provides the user with a download link for the final edited educational content and provides feedback on the analysis results of viewer attributes.

[0294] In this way, the system of the present invention allows users to easily create high-quality, emotion-optimized video content without requiring specialized knowledge.

[0295] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0296] Step 1:

[0297] A user logs in to the system and uploads an unedited video file. This inputs the unedited video file and sends it to the server. The server saves the received video file in the file system and registers the path of the saved location in the database. This process ensures that the input video file is managed appropriately.

[0298] Input: Unedited video file

[0299] Data processing: Receive and save files, register save paths in the database

[0300] Output: Path to the saved video file

[0301] Specific behavior:

[0302] The user uploads a video file using a browser.

[0303] The server receives the file and stores it in the file system.

[0304] The server registers the location where the video file is saved in a database.

[0305] Step 2:

[0306] The user inputs prompts about their specific video editing preferences and target audience. The prompts are sent to the server, which analyzes them and stores them in a database. This process ensures that the editing instructions based on the user's preferences are properly recorded.

[0307] Input: User prompt text

[0308] Data processing: receiving and analyzing prompts, saving them to a database

[0309] Output: prompt data as analysis results

[0310] Specific behavior:

[0311] The user fills in the prompts in the input form and clicks "Submit."

[0312] The server receives the prompt information and parses its contents.

[0313] The server stores the prompt information in a database.

[0314] Step 3:

[0315] The device uses a camera and microphone to capture the user's facial expressions and tone of voice when the user enters prompts. This data is analyzed by the emotion engine, which generates the user's emotion data and sends it to the server. This process reflects the user's emotion in the edits.

[0316] Input: User's facial expression data and voice data

[0317] Data processing: Analysis using emotion engine, generation of emotion data

[0318] Output: Emotion data as the analysis result

[0319] Specific behavior:

[0320] The device uses a camera and microphone to capture the user's facial expressions and voice.

[0321] The device uses an emotion engine to analyze the data and generate emotion data.

[0322] The terminal transmits the generated emotion data to the server.

[0323] Step 4:

[0324] The server integrates the received prompt information and emotion data and analyzes them using a generative AI model. Editing parameters are generated as a result of the analysis and stored in a database. This process results in optimized editing instructions based on the prompt and emotion.

[0325] Input: prompt information, emotion data

[0326] Data processing: Analysis using generative AI models and generation of editing parameters

[0327] Output: Generated edit parameters

[0328] Specific behavior:

[0329] The server integrates the prompt information and the emotion data.

[0330] The server performs the analysis using the generated AI model.

[0331] The server stores the generated editing parameters in a database.

[0332] Step 5:

[0333] The server then uses video editing software to edit the video based on the generated editing parameters. This editing process includes cutting video, adding subtitles, and adjusting the audio. The editing parameters are also fine-tuned based on the user's emotional data. Finally, the completed edited video is generated.

[0334] Input: Editing parameters, raw video file

[0335] Data processing: Editing process using video editing software

[0336] Output: Edited video file

[0337] Specific behavior:

[0338] The server starts the video editing software based on the editing parameters.

[0339] The server determines where to cut the video, adds subtitles, and adjusts the audio.

[0340] The server fine-tunes the editing parameters by reflecting the user's emotional data.

[0341] Step 6:

[0342] The server generates the edited video and provides the download link to the user. It also analyzes viewer attributes and provides the results as feedback to the user, allowing the user to use this information to improve their next content creation.

[0343] Input: Edited video file

[0344] Data processing: generating download links, analyzing viewer attributes

[0345] Output: Download link, viewer demographic feedback

[0346] Specific behavior:

[0347] The server generates the final edited video.

[0348] The server creates a download link for the generated video.

[0349] The server analyzes viewer attributes and displays the results on the user's dashboard.

[0350] (Application example 2)

[0351] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0352] Conventional video editing systems require manual editing by users and have difficulty generating optimal content based on viewer emotions. Effective video editing is particularly challenging due to a lack of centralized methods for optimizing prompt information and editing parameters. Furthermore, there is a need to provide even higher-quality content by integrating viewer attribute analysis and emotional data into the editing process.

[0353] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving unedited video, means for receiving prompts entered by the user, means for collecting user emotion data using an emotion recognition engine, means for optimizing editing parameters using the emotion data, and means for editing video by applying the generated editing parameters. This enables the automatic generation of high-quality, optimized video content based on user emotion and prompt information. In addition, by feeding back the analysis results of viewer attributes, the results can be used in subsequent content creation.

[0354] "Unedited video" refers to a video file in its original state, shot by a user without any editing.

[0355] "Prompt" refers to text information that a user enters about their video editing preferences and target audience.

[0356] "Editing parameters" refer to specific settings that specify effects, cuts, subtitles, audio adjustments, etc. for specific parts of a video.

[0357] An "emotion recognition engine" refers to a software module that analyzes a user's emotional state from facial expressions, tone of voice, etc.

[0358] "Emotion data" refers to information about a user's emotional state analyzed by an emotion recognition engine.

[0359] "Viewer attributes" refers to characteristic information such as age, gender, region, and interests of the user demographic who watches video content.

[0360] "Feedback" refers to the results of editing video content and analysis of viewer attributes that the system provides to users.

[0361] "Optimization" refers to the process of adjusting the editing parameters of video content based on prompt information and emotional data to create a form that best suits the user's intentions.

[0362] "Server" refers to a computer system that receives video, analyzes prompts and emotional data, generates editing parameters, edits the video, and outputs it.

[0363] The system of the present invention allows users to upload unedited videos they have taken, and automatically generates high-quality video content by combining prompt input and emotion recognition using an emotion engine. This system is implemented in the following steps:

[0364] Video upload

[0365] After logging in to the system, users upload unedited video files, which are then stored on the server as is. This process is the first step in providing videos to the system.

[0366] Prompt Input

[0367] The user inputs prompts, such as specific requests for editing the video or specific target audiences, such as "for teenagers," "strengthen the entertainment element," or "add French subtitles." This prompt information is then sent to the server.

[0368] Emotion recognition by emotion engine

[0369] The device uses an emotion engine to recognize the user's emotions when entering prompts. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, etc., and sends the results to the server. Specifically, the device's camera captures the user's facial expressions, and the emotion recognition module analyzes the data.

[0370] AI-powered analysis

[0371] The server receives prompt information from the user and emotion recognition results from the emotion engine, and analyzes this information using a generative AI model. As a result of the analysis, editing parameters are generated, including video effects, cuts, subtitles, and audio adjustments.

[0372] Video editing

[0373] The server automatically edits the uploaded video file using the generated editing parameters. Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. The server also fine-tunes the editing parameters based on the user's emotional data to achieve optimal editing results.

[0374] Final Output

[0375] The server generates the edited video content and provides the download link to the user. It also provides the user with analysis results on viewer attributes as feedback, allowing the user to check viewer reactions and use them to improve their next content creation.

[0376] Specific examples

[0377] For example, when a user creates "educational content for children," the following steps are taken.

[0378] 1. Users upload unedited "educational materials for children" to the system.

[0379] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[0380] 3. The device's emotion engine analyzes the user's emotions when entering the prompt and sends the emotion data to the server.

[0381] 4. The server analyzes the prompt information and emotional data and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[0382] 5. The server edits the video using the editing parameters to generate the completed educational content.

[0383] 6. The server provides the user with a download link for the edited video and provides analysis results on viewer attributes as feedback.

[0384] In this way, the system of the present invention helps users easily create high-quality, emotion-based, optimized video content.

[0385] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0386] Step 1: Upload your video

[0387] After logging in to the system, the user uploads an unedited video file. As input, the unedited video file and the user's identification information are required. As output, the video file is saved on the server and an appropriate video ID is generated. Specifically, by selecting the video file and pressing the upload button, the file is sent to the server.

[0388] Step 2: Prompt Input

[0389] The user inputs specific instructions regarding their video editing preferences and target audience as prompts. Text information regarding the editing preferences and target audience is required as input. The prompt information is sent to the server as output. Specifically, the user enters the prompt text in the input field and presses the send button to send the data to the server.

[0390] Step 3: Emotion Recognition

[0391] The device uses an emotion engine to recognize the user's emotions when entering prompts. The input requires the user's facial expression data and voice data. The output is analyzed emotion data and sent to the server. Specifically, the user's facial expressions and voice are captured through a camera and microphone, and analyzed by the emotion recognition engine.

[0392] Step 4: AI analysis

[0393] The server receives prompt information from the user and the emotion recognition results from the emotion engine, and analyzes them using a generative AI model. Prompt information and emotion data are required as input. Editing parameters are generated as output. Specifically, the prompt text and emotion data are input into the AI ​​model, and editing parameters such as color, effects, subtitles, and audio adjustments are generated.

[0394] Step 5: Edit your video

[0395] The server automatically edits the uploaded video file using the generated editing parameters. The input requires an unedited video file and the editing parameters. The output is an edited video file. Specifically, based on the parameters generated by the AI, it automatically performs operations such as cutting the video, adding effects, inserting subtitles, and adjusting the audio.

[0396] Step 6: Final output

[0397] The server generates the edited video content and provides the download link to the user. The edited video file and user identification information are required as input. The download link is generated as output. Analysis results on viewer attributes are also provided as feedback. Specifically, the download link and analysis report are sent to the user via email or notification.

[0398] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0399] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0400] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0401] [Second embodiment]

[0402] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0403] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0404] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0405] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0406] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0407] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0408] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0409] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0410] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0411] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0412] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0413] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0414] The system of the present invention allows video providers to upload unedited videos and perform prompt input to automatically generate high-quality video content. Specific program processing of this system will be explained below in natural language.

[0415] Video upload

[0416] Users upload raw video files to the system, which are then stored on the server as is. Uploading a video by a user is the first step in providing a video to the system.

[0417] Prompt Input

[0418] Users input specific prompts about their desired video editing style and target audience, such as "suitable for 30-year-old women," "style of a popular video creator," or "add English subtitles." The prompts are then sent to the server.

[0419] AI-powered analysis

[0420] When the server receives a prompt from the user, it sends it to a multimodal AI for analysis. The AI ​​generates appropriate editing parameters based on the prompt information. These editing parameters include video effects, subtitles, and audio adjustments.

[0421] Video editing

[0422] The server uses the generated editing parameters to edit the uploaded video. Specifically, it performs operations such as cutting and transitioning the video, adding subtitles, and adjusting the audio. All edits are applied automatically, allowing users to enjoy high-quality video content without any effort.

[0423] Final Output

[0424] The server generates the edited video content and provides the download link to the user. It can also provide the user with feedback on the video content based on the analysis results of viewer attributes. This allows the user to check viewer reactions and use them to improve their next content creation.

[0425] Specific examples

[0426] For example, when a user creates "educational content for children," the following steps are taken.

[0427] 1. Users upload unedited "educational materials for children" to the system.

[0428] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[0429] 3. The server analyzes the prompts and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[0430] 4. The server edits the video using the editing parameters to generate the completed educational content.

[0431] 5. The server provides the user with a download link for the edited video and provides feedback on the analysis results of viewer attributes.

[0432] In this way, the system of the present invention helps users easily create high-quality video content that is optimized for their target audience.

[0433] The processing flow will be explained below.

[0434] Step 1:

[0435] A user logs into the system and accesses the video upload page, where the user selects an unedited video file and begins uploading.

[0436] Step 2:

[0437] The device sends the selected video file to the server, and once the upload is complete, the server temporarily stores the received video file.

[0438] Step 3:

[0439] The user navigates to a prompt entry page and enters specific instructions about their video editing preferences and target audience (e.g., "suitable for women in their 30s," "style of popular video creators," "add English subtitles"), and then sends the prompt to the server.

[0440] Step 4:

[0441] The server receives prompt information sent by the user and sends it to the multimodal AI for analysis. The prompt information includes the style of the video content, the target audience, and the elements that the user wants to add.

[0442] Step 5:

[0443] AI analyzes the prompt information and generates optimal editing parameters, including video cut positions, subtitles, audio adjustments, and effects.

[0444] Step 6:

[0445] The server automatically edits raw video files using AI-generated editing parameters, such as cutting the video at specified points, adding captions based on prompts, and adjusting the volume and intonation of the audio.

[0446] Step 7:

[0447] The server generates and stores the video file after the editing process is completed.

[0448] Step 8:

[0449] The server creates a download link for the edited video file and notifies the user of this link. It also provides the user with analysis results based on viewer attributes as feedback. Users can use this information to improve their content and create their next video.

[0450] Example 1

[0451] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0452] In today's world, video content is an important means of disseminating information and providing entertainment. However, editing unedited videos to high quality requires specialized knowledge and time. Editing videos that accurately reflect the user's intentions is particularly difficult, and many users are unable to achieve high-quality video editing. Analyzing viewer reactions and obtaining feedback that can be used for future edits is also challenging. There is a need for a system that can solve these problems, allowing users to easily create high-quality video content and receive effective feedback.

[0453] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0454] In this invention, the server includes a means for receiving unedited video, a means for receiving prompts entered by a user, a means for transmitting the prompts to a multimodal AI model, analyzing the prompts, and generating editing parameters, a means for editing the video by applying the generated editing parameters, and a means for outputting the edited video. This allows a user without specialized knowledge to automatically generate high-quality video content simply by entering prompts. Furthermore, by including a means for analyzing viewer attributes and providing feedback to the user, the user can make effective edits based on viewer reactions, thereby continuously improving the quality of the video content.

[0455] "Unedited video" refers to the original video file that has not been subjected to any specific processing or editing.

[0456] A "prompt" is a sentence or keyword entered by a user to describe their video editing needs, requirements, and specific instructions regarding the target audience.

[0457] A "multimodal AI model" is an artificial intelligence model that integrates and analyzes data of different formats and types to generate appropriate editing parameters.

[0458] "Editing parameters" refer to specific operations and setting items designated in video editing, and include, for example, cuts, effects, subtitles, and audio adjustments.

[0459] "Video editing means" refers to the technology and process for processing video files based on editing parameters and generating high-quality video content.

[0460] "Viewer attributes" refers to information about the viewer of a video, including age, gender, viewing time, viewing device, etc.

[0461] "Feedback" is information that provides users with analysis results based on viewer reactions and attributes, and is used to help them edit their next video.

[0462] The system of the present invention allows a video provider to upload unedited videos and input prompts to automatically generate high-quality video content. An embodiment of this system will be described in detail below.

[0463] Video upload

[0464] Users first upload unedited video files to the system, which then send them from their devices to the server, where they are stored in secure storage. This is typically done using HTTP / HTTPS protocols.

[0465] Prompt Input

[0466] Next, users input prompts about their video editing preferences and target audience, such as "for women in their 30s," "in the style of a popular video creator," or "add English subtitles." The prompts are then sent from the device to the server.

[0467] Prompt parsing and editing parameter generation

[0468] The server sends the received prompts to a multimodal AI model for analysis, which has the ability to integrate and analyze data of different formats and types, and generates specific editing parameters based on the prompt information.

[0469] Video editing

[0470] The server uses the generated editing parameters to edit the video. The editing process is automated and includes cutting the video, adding transitions, effects, and adjusting the audio. This is typically done using video editing software such as FFmpeg.

[0471] Outputting edited videos

[0472] Once editing is complete, the server generates a download link for the video and provides it to the user, who can then download the edited, high-quality video content from the provided link.

[0473] Audience analysis and feedback

[0474] This system also has a function for analyzing viewer attributes. The server collects information such as the viewer's age, gender, viewing time, and viewing device, and uses this information to analyze viewer attributes. The analysis results are provided to the user as feedback information, which can be used to help with the next video editing.

[0475] Specific examples

[0476] For example, if a user wants to create "educational content for kids", the following steps would occur:

[0477] 1. The user uploads unedited "educational materials for children" to the system via their browser.

[0478] 2. The user enters "For children, fun and educational style, English subtitles added" into the input field as a prompt.

[0479] 3. The server sends the prompt to the multimodal AI model and receives the analysis results.

[0480] 4. The server edits the video using the generated editing parameters. Video editing software such as FFmpeg is used.

[0481] 5. The server generates a download link for the edited video and notifies the user.

[0482] 6. The server provides the analysis results based on viewer attributes as feedback to the user.

[0483] This system not only enables users to easily generate high-quality video content optimized for their target audience without any specialized knowledge, but also provides valuable feedback based on viewer attributes.

[0484] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0485] Step 1:

[0486] The user selects an unedited video file on their device and uploads it to the system. The uploaded video file is sent to the server using HTTP / HTTPS protocol. The server stores the received video file in secure storage.

[0487] Input: Unedited video file

[0488] Output: Video file saved on the server

[0489] Specific behavior:

[0490] 1. The user selects a video file from a browser or a dedicated application.

[0491] 2. The device sends the video file to the server.

[0492] 3. The server receives the video file and stores it on the storage server.

[0493] Step 2:

[0494] The user inputs prompts about their desired video editing style and target audience from their device and sends them to the server, which includes specific instructions.

[0495] Input: prompt statement

[0496] Output: The prompt sent to the server

[0497] Specific behavior:

[0498] 1. The user enters a prompt sentence into an input field in a browser or dedicated application.

[0499] 2. The terminal sends the entered prompt text to the server.

[0500] Step 3:

[0501] The server sends the prompt to the multimodal AI model, which analyzes the prompt and generates the necessary editing parameters, including video cuts, effects, subtitles, and audio adjustments.

[0502] Input: prompt statement

[0503] Output: Edit parameters

[0504] Specific behavior:

[0505] 1. The server retrieves the prompt sentence and sends it to the multimodal AI model.

[0506] 2. The multimodal AI model analyzes the prompt and generates editing parameters.

[0507] 3. The server receives the generated editing parameters and saves them in its internal storage.

[0508] Step 4:

[0509] The server uses the editing parameters to edit the uploaded video. Editing involves cutting the video, adding transitions, adding effects, adjusting the audio, etc. This is done using video editing software such as FFmpeg.

[0510] Input: Editing parameters, raw video file

[0511] Output: Edited video file

[0512] Specific behavior:

[0513] 1. The server opens the video file using video editing software (e.g. FFmpeg).

[0514] 2. The server performs specific editing tasks based on the editing parameters.

[0515] 3. Save the edited video file as a new file.

[0516] Step 5:

[0517] The server generates a download link for the edited video content, which is then provided to the user, who uses it to download the edited video.

[0518] Input: Edited video file

[0519] Output: Download link

[0520] Specific behavior:

[0521] 1. The server saves the edited video file in a specific directory and generates a download URL.

[0522] 2. The server notifies the user of the generated URL.

[0523] Step 6:

[0524] The server provides the user with the analysis results of viewer attributes as feedback. Viewing data is collected and analyzed to analyze viewer attributes such as age, gender, viewing time, and viewing device.

[0525] Input: Viewing data

[0526] Output: Viewer attribute analysis results

[0527] Specific behavior:

[0528] 1. The server collects viewing data.

[0529] 2. The server runs an algorithm to analyze viewer attributes.

[0530] 3. The server generates the analysis results in the form of a report and provides it to the user.

[0531] This allows users to easily create high-quality video content and obtain valuable feedback based on viewer reactions.

[0532] (Application example 1)

[0533] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0534] Modern video content creation requires efficient editing of raw material into high-quality videos. However, traditional video editing processes are time-consuming and labor-intensive, especially when editing tasks require prompt input. To address this issue, there is a growing need for systems that use voice recognition interfaces and visual display devices to enable prompt input and confirmation of editing results in real time.

[0535] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0536] In this invention, the server includes means for receiving unedited video, means for receiving prompts input by a user, means for analyzing the prompts to generate editing parameters, means for editing the video by applying the generated editing parameters, means for outputting the edited video, means for inputting prompts using a voice recognition interface, and means for checking the editing results in real time on a visual display device, thereby enabling users to easily create high-quality video content.

[0537] "Unedited video" refers to video material that has been shot by a user without any editing, in its original form.

[0538] A "prompt" is a command that a user enters in text or voice to provide specific instructions about their video editing preferences or target audience.

[0539] "Editing parameters" are specific settings and operation information for video editing that are generated by AI based on prompts.

[0540] A "voice recognition interface" is a device or function that converts a user's speech into text in real time and inputs it into a system.

[0541] A "visual display device" is a device that uses smart glasses or other display devices to visually display the content of edited video in real time.

[0542] "Real-time" refers to processing and display with no or very little delay.

[0543] "Viewer attributes" refers to characteristic information such as age, sex, and interests of users who view video content.

[0544] The "server" is a central computing device for processing the entire system, and is a device that uploads videos, analyzes prompts, generates editing parameters, and provides edited videos.

[0545] A system for implementing the present invention is configured as follows.

[0546] Overall system configuration

[0547] 1. Video Upload:

[0548] Users use smart glasses or other visual display devices to capture unedited videos, which are then automatically uploaded to a server.

[0549] 2. Prompt input:

[0550] Using the voice recognition interface on the smart glasses, users can speak prompts related to editing preferences and target audiences, such as "Fun and educational for kids, with English subtitles." This speech is converted into text in real time and sent to the server as prompts.

[0551] 3. AI-powered prompt analysis and editing parameter generation:

[0552] The server uses a generative AI model to analyze the received prompts. This model generates optimal editing parameters based on the input prompts, including color adjustments, adding effects, and subtitle placement.

[0553] 4. Video editing:

[0554] The server applies the generated editing parameters to automatically edit the uploaded raw video, using a video editing library such as MoviePy to add transitions, captions, and adjust the audio.

[0555] 5. Visual confirmation of edit results:

[0556] Once editing is complete, the server streams the edited video in real time to a visual display device such as smart glasses, where the user can view it and, if desired, voice further prompts to request additional edits.

[0557] 6. Final output and feedback:

[0558] If the user is satisfied with the edited results, the server saves the edited video as the final output and provides a download link. It also provides the user with analysis results of viewer attributes as feedback to help them create content next time.

[0559] Specific examples

[0560] For example, consider entering the prompt "Fun and educational for kids, with English subtitles."

[0561] Video upload:

[0562] Users take photos of unedited "educational materials for children" using smart glasses and upload them to the server.

[0563] Prompt Input:

[0564] Using the voice recognition interface, the user can say, "Kids-oriented, fun and educational style, English subtitles added."

[0565] AI-powered prompt analysis and editing parameter generation:

[0566] Based on the prompts received, the server generates editing parameters such as optimal colors and character effects for children, fun background music, and the addition of English subtitles.

[0567] Video editing:

[0568] The server uses editing parameters and utilizes the "MoviePy" library to automatically perform video cuts, transitions, add subtitles, adjust audio, and more.

[0569] Visual confirmation of edit results:

[0570] Through the smart glasses, the user can view the edited video in real time and input additional editing instructions by voice if necessary.

[0571] Final output and feedback:

[0572] The server will then publish the final educational content and provide feedback on viewer attributes.

[0573] In this way, the system is designed to enable users to easily create high-quality video content optimized for their target audience.

[0574] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0575] Step 1:

[0576] Video recording and uploading

[0577] A user wearing the smart glasses shoots unedited video, and after shooting is complete, the software in the smart glasses automatically uploads the video file to a server.

[0578] Input: Unedited video files captured by smart glasses.

[0579] Output: Raw video files saved on the server.

[0580] Step 2:

[0581] Speech-to-text prompts

[0582] Using a voice recognition interface built into the smart glasses, users can speak their editorial preferences and prompts regarding target audiences, which are converted into text in real time and sent to the server.

[0583] Input: Voice prompts.

[0584] Output: Prompt data in text format.

[0585] Step 3:

[0586] Prompt parsing and editing parameter generation

[0587] The server receives the text prompts and uses a generative AI model to analyze them and generate editing parameters, which determine appropriate settings for captions, effects, audio adjustments, and more.

[0588] Input: Prompt data in text format.

[0589] Output: Edit parameters.

[0590] Step 4:

[0591] Video editing

[0592] The server uses the generated editing parameters to edit the uploaded raw video, using a video editing library such as MoviePy to add transitions, insert subtitles, synthesize music, and more.

[0593] Input: raw video file, editing parameters.

[0594] Output: Edited video file.

[0595] Step 5:

[0596] Real-time visual confirmation of edit results

[0597] The edited video is streamed in real time to a visual display device such as smart glasses, where the user can view it and provide additional voice-activated editing instructions as needed.

[0598] Input: Edited video file.

[0599] Output: Visual confirmation and further instructions for the user.

[0600] Step 6:

[0601] Final output and feedback provided

[0602] If the user is satisfied with the edited results, the server will save the edited video and provide a download link to the user. At the same time, feedback based on the analysis of viewer attributes will be provided, providing useful information for future content creation.

[0603] Input: Edited video files, viewer demographic data.

[0604] Output: Final edited video, download link, feedback.

[0605] The above processing steps realize a system that allows a user to easily generate high-quality video content and check and modify it in real time through a visual display device.

[0606] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0607] The system of the present invention allows video providers to upload unedited videos, and automatically generates high-quality video content by combining prompt input and emotion recognition using an emotion engine. The specific program processing of this system is explained below in natural language.

[0608] Video upload

[0609] After logging in to the system, users upload unedited video files. The uploaded video files are stored on the server as is. This is the first step in providing videos to the system.

[0610] Prompt Input

[0611] Users input specific prompts about their video editing preferences and target audience, such as "suitable for women in their 30s," "style of popular video creators," "add English subtitles," etc. This prompt information is sent to the server.

[0612] Emotion recognition by emotion engine

[0613] The device uses an emotion engine to recognize the user's emotions when inputting prompts. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, etc., and sends the results to the server.

[0614] AI-powered analysis

[0615] The server receives prompt information from the user and emotion recognition results from the emotion engine, and analyzes them using multimodal AI. The AI ​​generates editing parameters based on this information. These editing parameters include video effects, subtitles, audio adjustments, and effects.

[0616] Video editing

[0617] The server automatically edits the uploaded video file using the generated editing parameters. Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. Furthermore, the server fine-tunes the editing parameters based on the user's emotions to achieve the optimal editing results.

[0618] Final Output

[0619] The server generates the edited video content and provides the download link to the user. It also provides the user with analysis results on viewer attributes as feedback, allowing the user to check viewer reactions and use them to improve their next content creation.

[0620] Specific examples

[0621] For example, when a user creates "educational content for children," the following steps are taken.

[0622] 1. Users upload unedited "educational materials for children" to the system.

[0623] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[0624] 3. The device's emotion engine analyzes the user's emotions when entering the prompt and sends the emotion data to the server.

[0625] 4. The server analyzes the prompt information and emotional data and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[0626] 5. The server edits the video using the editing parameters to generate the completed educational content.

[0627] 6. The server provides the user with a download link for the edited video and provides analysis results on viewer attributes as feedback.

[0628] In this way, the system of the present invention helps users easily create high-quality, emotion-based, optimized video content.

[0629] The processing flow will be explained below.

[0630] Step 1:

[0631] The user logs into the system, accesses the upload page, selects the unedited video file, and clicks the "Upload" button.

[0632] Step 2:

[0633] The device sends the selected video file to the server, where it is temporarily stored.

[0634] Step 3:

[0635] Users access the prompt input page and enter specific instructions for video editing (e.g., "Suitable for a 30-year-old woman," "Style of a popular video creator," "Add English subtitles"), then click the "Submit" button.

[0636] Step 4:

[0637] The device's emotion engine analyzes data such as the user's facial expression and tone of voice when entering prompts, and the analyzed emotion data is sent to the server.

[0638] Step 5:

[0639] The server receives prompt information and emotional data sent by the user, which is then sent to a multimodal AI for analysis.

[0640] Step 6:

[0641] AI analyzes the prompt information and emotional data to generate appropriate editing parameters, including video cut positions, subtitles, audio adjustments, and effects.

[0642] Step 7:

[0643] The server then automatically edits the raw video file using the generated editing parameters, such as cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio.

[0644] Step 8:

[0645] The server generates a video file after the editing process is completed and stores it on the server.

[0646] Step 9:

[0647] The server generates a download link for the edited video file and notifies the user of this link.

[0648] Step 10:

[0649] The server provides the user with analysis results based on viewer attributes as feedback, allowing the user to use this information to improve their content and create their next video.

[0650] Example 2

[0651] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0652] In recent years, with the spread of video sharing platforms, there has been a growing need for many users to easily edit and publish their own videos. However, video editing requires specialized knowledge and skills, making it a difficult task for many users. Furthermore, in order to provide optimal content to viewers, editing must take into account the viewer's attributes and emotions, but this is extremely difficult to do manually. To solve these challenges, a system is needed that allows anyone to easily create high-quality video content.

[0653] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0654] In this invention, the server includes means for receiving unedited video, means for receiving prompts entered by a user, means for analyzing the prompts and generating editing parameters, means for editing the video by applying the generated editing parameters, means for outputting the edited video, means for recognizing the user's emotions, means for analyzing the recognized emotions and reflecting them in the editing parameters, and means for analyzing viewer attributes and feeding the results back to the user. This enables even users without specialized knowledge to generate high-quality video content that reflects viewer attributes and user emotions.

[0655] "Unedited video" refers to video data in its original state without any editing or processing.

[0656] A "prompt" refers to a sentence or keyword that a user enters regarding a specific instruction or request.

[0657] "Editing parameters" refers to data that includes specific editing instructions, such as where to cut a video, adding subtitles, adjusting the audio, etc., that are generated based on prompts and user emotion data.

[0658] "Means for editing videos" refers to a series of processes for automatically processing and correcting video files based on the generated editing parameters.

[0659] "Means for recognizing emotions" refers to technologies and devices that collect and analyze data such as a user's facial expressions and tone of voice to identify the user's emotional state.

[0660] "Viewer attributes" refers to characteristic information such as the age, gender, and interests of the video viewer.

[0661] "Means for analyzing viewer attributes" refers to technologies and algorithms that analyze viewer characteristics and use the results to understand viewer interests and demands.

[0662] "Means for analyzing the user's emotions and reflecting them in the editing parameters" refers to a process for adjusting the editing parameters based on the recognized emotion data to perform optimal video editing.

[0663] The present invention relates to a system for automatically generating high-quality video content based on a user's prompt input and emotion data after receiving unedited video. Specific embodiments of the present invention will be described below.

[0664] A user logs in to the system and uploads an unedited video file, which is then stored on the server and used for subsequent editing.

[0665] Next, the user inputs prompts, including specific requests for editing the video and instructions about the target audience. For example, they might input prompts such as "suitable for women in their 30s," "style of a popular video creator," or "add English subtitles." The prompt information is sent to the server, where it is analyzed and stored in a database.

[0666] The device uses a camera and microphone to capture the user's facial expressions and tone of voice as they input prompts. The captured data is analyzed by an emotion engine to generate user emotion data, which is then sent to the server.

[0667] The server integrates the received prompt information and emotional data and performs analysis using a multimodal generative AI model (e.g., a natural language processing model or an image analysis model). The AI ​​model generates editing parameters based on the input data. These editing parameters include video cut positions, adding subtitles, adjusting audio, etc. The generated editing parameters are stored in a database.

[0668] The server then begins editing the uploaded video file based on the generated editing parameters. This editing process is performed using video editing software (e.g., Adobe Premiere Pro API or FFmpeg). Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. Additionally, the editing parameters are fine-tuned as needed to reflect the user's emotional data.

[0669] Finally, the server generates the edited video and provides the user with a download link. The server also analyzes viewer attributes and provides the results as feedback to the user, allowing the user to check viewer reactions and use them to improve their next content creation.

[0670] As a specific example, if a user wants to create "educational content for children," he or she uploads unedited "educational materials for children" and enters "kids-oriented, fun learning style, add English subtitles" into the prompt. The device's emotion engine analyzes the user's emotion when entering the prompt and sends the emotion data to the server. The server analyzes the prompt information and emotion data, generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles, and edits the video. The server provides the user with a download link for the final edited educational content and provides feedback on the analysis results of viewer attributes.

[0671] In this way, the system of the present invention allows users to easily create high-quality, emotion-optimized video content without requiring specialized knowledge.

[0672] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0673] Step 1:

[0674] A user logs in to the system and uploads an unedited video file. This inputs the unedited video file and sends it to the server. The server saves the received video file in the file system and registers the path of the saved location in the database. This process ensures that the input video file is managed appropriately.

[0675] Input: Unedited video file

[0676] Data processing: Receive and save files, register save paths in the database

[0677] Output: Path to the saved video file

[0678] Specific behavior:

[0679] The user uploads a video file using a browser.

[0680] The server receives the file and stores it in the file system.

[0681] The server registers the location where the video file is saved in a database.

[0682] Step 2:

[0683] The user inputs prompts about their specific video editing preferences and target audience. The prompts are sent to the server, which analyzes them and stores them in a database. This process ensures that the editing instructions based on the user's preferences are properly recorded.

[0684] Input: User prompt text

[0685] Data processing: receiving and analyzing prompts, saving them to a database

[0686] Output: prompt data as analysis results

[0687] Specific behavior:

[0688] The user fills in the prompts in the input form and clicks "Submit."

[0689] The server receives the prompt information and parses its contents.

[0690] The server stores the prompt information in a database.

[0691] Step 3:

[0692] The device uses a camera and microphone to capture the user's facial expressions and tone of voice when the user enters prompts. This data is analyzed by the emotion engine, which generates the user's emotion data and sends it to the server. This process reflects the user's emotion in the edits.

[0693] Input: User's facial expression data and voice data

[0694] Data processing: Analysis using emotion engine, generation of emotion data

[0695] Output: Emotion data as the analysis result

[0696] Specific behavior:

[0697] The device uses a camera and microphone to capture the user's facial expressions and voice.

[0698] The device uses an emotion engine to analyze the data and generate emotion data.

[0699] The terminal transmits the generated emotion data to the server.

[0700] Step 4:

[0701] The server integrates the received prompt information and emotion data and analyzes them using a generative AI model. Editing parameters are generated as a result of the analysis and stored in a database. This process results in optimized editing instructions based on the prompt and emotion.

[0702] Input: prompt information, emotion data

[0703] Data processing: Analysis using generative AI models and generation of editing parameters

[0704] Output: Generated edit parameters

[0705] Specific behavior:

[0706] The server integrates the prompt information and the emotion data.

[0707] The server performs the analysis using the generated AI model.

[0708] The server stores the generated editing parameters in a database.

[0709] Step 5:

[0710] The server then uses video editing software to edit the video based on the generated editing parameters. This editing process includes cutting video, adding subtitles, and adjusting the audio. The editing parameters are also fine-tuned based on the user's emotional data. Finally, the completed edited video is generated.

[0711] Input: Editing parameters, raw video file

[0712] Data processing: Editing process using video editing software

[0713] Output: Edited video file

[0714] Specific behavior:

[0715] The server starts the video editing software based on the editing parameters.

[0716] The server determines where to cut the video, adds subtitles, and adjusts the audio.

[0717] The server fine-tunes the editing parameters by reflecting the user's emotional data.

[0718] Step 6:

[0719] The server generates the edited video and provides the download link to the user. It also analyzes viewer attributes and provides the results as feedback to the user, allowing the user to use this information to improve their next content creation.

[0720] Input: Edited video file

[0721] Data processing: generating download links, analyzing viewer attributes

[0722] Output: Download link, viewer demographic feedback

[0723] Specific behavior:

[0724] The server generates the final edited video.

[0725] The server creates a download link for the generated video.

[0726] The server analyzes viewer attributes and displays the results on the user's dashboard.

[0727] (Application example 2)

[0728] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0729] Conventional video editing systems require manual editing by users and have difficulty generating optimal content based on viewer emotions. Effective video editing is particularly challenging due to a lack of centralized methods for optimizing prompt information and editing parameters. Furthermore, there is a need to provide even higher-quality content by integrating viewer attribute analysis and emotional data into the editing process.

[0730] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving unedited video, means for receiving prompts entered by the user, means for collecting user emotion data using an emotion recognition engine, means for optimizing editing parameters using the emotion data, and means for editing video by applying the generated editing parameters. This enables the automatic generation of high-quality, optimized video content based on user emotion and prompt information. In addition, by feeding back the analysis results of viewer attributes, the results can be used in subsequent content creation.

[0731] "Unedited video" refers to a video file in its original state, shot by a user without any editing.

[0732] "Prompt" refers to text information that a user enters about their video editing preferences and target audience.

[0733] "Editing parameters" refer to specific settings that specify effects, cuts, subtitles, audio adjustments, etc. for specific parts of a video.

[0734] An "emotion recognition engine" refers to a software module that analyzes a user's emotional state from facial expressions, tone of voice, etc.

[0735] "Emotion data" refers to information about a user's emotional state analyzed by an emotion recognition engine.

[0736] "Viewer attributes" refers to characteristic information such as age, gender, region, and interests of the user demographic who watches video content.

[0737] "Feedback" refers to the results of editing video content and analysis of viewer attributes that the system provides to users.

[0738] "Optimization" refers to the process of adjusting the editing parameters of video content based on prompt information and emotional data to create a form that best suits the user's intentions.

[0739] "Server" refers to a computer system that receives video, analyzes prompts and emotional data, generates editing parameters, edits the video, and outputs it.

[0740] The system of the present invention allows users to upload unedited videos they have taken, and automatically generates high-quality video content by combining prompt input and emotion recognition using an emotion engine. This system is implemented in the following steps:

[0741] Video upload

[0742] After logging in to the system, users upload unedited video files, which are then stored on the server as is. This process is the first step in providing videos to the system.

[0743] Prompt Input

[0744] The user inputs prompts, such as specific requests for editing the video or specific target audiences, such as "for teenagers," "strengthen the entertainment element," or "add French subtitles." This prompt information is then sent to the server.

[0745] Emotion recognition by emotion engine

[0746] The device uses an emotion engine to recognize the user's emotions when entering prompts. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, etc., and sends the results to the server. Specifically, the device's camera captures the user's facial expressions, and the emotion recognition module analyzes the data.

[0747] AI-powered analysis

[0748] The server receives prompt information from the user and emotion recognition results from the emotion engine, and analyzes this information using a generative AI model. As a result of the analysis, editing parameters are generated, including video effects, cuts, subtitles, and audio adjustments.

[0749] Video editing

[0750] The server automatically edits the uploaded video file using the generated editing parameters. Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. The server also fine-tunes the editing parameters based on the user's emotional data to achieve optimal editing results.

[0751] Final Output

[0752] The server generates the edited video content and provides the download link to the user. It also provides the user with analysis results on viewer attributes as feedback, allowing the user to check viewer reactions and use them to improve their next content creation.

[0753] Specific examples

[0754] For example, when a user creates "educational content for children," the following steps are taken.

[0755] 1. Users upload unedited "educational materials for children" to the system.

[0756] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[0757] 3. The device's emotion engine analyzes the user's emotions when entering the prompt and sends the emotion data to the server.

[0758] 4. The server analyzes the prompt information and emotional data and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[0759] 5. The server edits the video using the editing parameters to generate the completed educational content.

[0760] 6. The server provides the user with a download link for the edited video and provides analysis results on viewer attributes as feedback.

[0761] In this way, the system of the present invention helps users easily create high-quality, emotion-based, optimized video content.

[0762] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0763] Step 1: Upload your video

[0764] After logging in to the system, the user uploads an unedited video file. As input, the unedited video file and the user's identification information are required. As output, the video file is saved on the server and an appropriate video ID is generated. Specifically, by selecting the video file and pressing the upload button, the file is sent to the server.

[0765] Step 2: Prompt Input

[0766] The user inputs specific instructions regarding their video editing preferences and target audience as prompts. Text information regarding the editing preferences and target audience is required as input. The prompt information is sent to the server as output. Specifically, the user enters the prompt text in the input field and presses the send button to send the data to the server.

[0767] Step 3: Emotion Recognition

[0768] The device uses an emotion engine to recognize the user's emotions when entering prompts. The input requires the user's facial expression data and voice data. The output is analyzed emotion data and sent to the server. Specifically, the user's facial expressions and voice are captured through a camera and microphone, and analyzed by the emotion recognition engine.

[0769] Step 4: AI analysis

[0770] The server receives prompt information from the user and the emotion recognition results from the emotion engine, and analyzes them using a generative AI model. Prompt information and emotion data are required as input. Editing parameters are generated as output. Specifically, the prompt text and emotion data are input into the AI ​​model, and editing parameters such as color, effects, subtitles, and audio adjustments are generated.

[0771] Step 5: Edit your video

[0772] The server automatically edits the uploaded video file using the generated editing parameters. The input requires an unedited video file and the editing parameters. The output is an edited video file. Specifically, based on the parameters generated by the AI, it automatically performs operations such as cutting the video, adding effects, inserting subtitles, and adjusting the audio.

[0773] Step 6: Final output

[0774] The server generates the edited video content and provides the download link to the user. The edited video file and user identification information are required as input. The download link is generated as output. Analysis results on viewer attributes are also provided as feedback. Specifically, the download link and analysis report are sent to the user via email or notification.

[0775] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0776] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0777] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0778] [Third embodiment]

[0779] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0780] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0781] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0782] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0783] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0784] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0785] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0786] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0787] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0788] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0789] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0790] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0791] The system of the present invention allows video providers to upload unedited videos and perform prompt input to automatically generate high-quality video content. Specific program processing of this system will be explained below in natural language.

[0792] Video upload

[0793] Users upload raw video files to the system, which are then stored on the server as is. Uploading a video by a user is the first step in providing a video to the system.

[0794] Prompt Input

[0795] Users input specific prompts about their desired video editing style and target audience, such as "suitable for 30-year-old women," "style of a popular video creator," or "add English subtitles." The prompts are then sent to the server.

[0796] AI-powered analysis

[0797] When the server receives a prompt from the user, it sends it to a multimodal AI for analysis. The AI ​​generates appropriate editing parameters based on the prompt information. These editing parameters include video effects, subtitles, and audio adjustments.

[0798] Video editing

[0799] The server uses the generated editing parameters to edit the uploaded video. Specifically, it performs operations such as cutting and transitioning the video, adding subtitles, and adjusting the audio. All edits are applied automatically, allowing users to enjoy high-quality video content without any effort.

[0800] Final Output

[0801] The server generates the edited video content and provides the download link to the user. It can also provide the user with feedback on the video content based on the analysis results of viewer attributes. This allows the user to check viewer reactions and use them to improve their next content creation.

[0802] Specific examples

[0803] For example, when a user creates "educational content for children," the following steps are taken.

[0804] 1. Users upload unedited "educational materials for children" to the system.

[0805] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[0806] 3. The server analyzes the prompts and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[0807] 4. The server edits the video using the editing parameters to generate the completed educational content.

[0808] 5. The server provides the user with a download link for the edited video and provides feedback on the analysis results of viewer attributes.

[0809] In this way, the system of the present invention helps users easily create high-quality video content that is optimized for their target audience.

[0810] The processing flow will be explained below.

[0811] Step 1:

[0812] A user logs into the system and accesses the video upload page, where the user selects an unedited video file and begins uploading.

[0813] Step 2:

[0814] The device sends the selected video file to the server, and once the upload is complete, the server temporarily stores the received video file.

[0815] Step 3:

[0816] The user navigates to a prompt entry page and enters specific instructions about their video editing preferences and target audience (e.g., "suitable for women in their 30s," "style of popular video creators," "add English subtitles"), and then sends the prompt to the server.

[0817] Step 4:

[0818] The server receives prompt information sent by the user and sends it to the multimodal AI for analysis. The prompt information includes the style of the video content, the target audience, and the elements that the user wants to add.

[0819] Step 5:

[0820] AI analyzes the prompt information and generates optimal editing parameters, including video cut positions, subtitles, audio adjustments, and effects.

[0821] Step 6:

[0822] The server automatically edits raw video files using AI-generated editing parameters, such as cutting the video at specified points, adding captions based on prompts, and adjusting the volume and intonation of the audio.

[0823] Step 7:

[0824] The server generates and stores the video file after the editing process is completed.

[0825] Step 8:

[0826] The server creates a download link for the edited video file and notifies the user of this link. It also provides the user with analysis results based on viewer attributes as feedback. Users can use this information to improve their content and create their next video.

[0827] Example 1

[0828] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0829] In today's world, video content is an important means of disseminating information and providing entertainment. However, editing unedited videos to high quality requires specialized knowledge and time. Editing videos that accurately reflect the user's intentions is particularly difficult, and many users are unable to achieve high-quality video editing. Analyzing viewer reactions and obtaining feedback that can be used for future edits is also challenging. There is a need for a system that can solve these problems, allowing users to easily create high-quality video content and receive effective feedback.

[0830] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0831] In this invention, the server includes a means for receiving unedited video, a means for receiving prompts entered by a user, a means for transmitting the prompts to a multimodal AI model, analyzing the prompts, and generating editing parameters, a means for editing the video by applying the generated editing parameters, and a means for outputting the edited video. This allows a user without specialized knowledge to automatically generate high-quality video content simply by entering prompts. Furthermore, by including a means for analyzing viewer attributes and providing feedback to the user, the user can make effective edits based on viewer reactions, thereby continuously improving the quality of the video content.

[0832] "Unedited video" refers to the original video file that has not been subjected to any specific processing or editing.

[0833] A "prompt" is a sentence or keyword entered by a user to describe their video editing needs, requirements, and specific instructions regarding the target audience.

[0834] A "multimodal AI model" is an artificial intelligence model that integrates and analyzes data of different formats and types to generate appropriate editing parameters.

[0835] "Editing parameters" refer to specific operations and setting items designated in video editing, and include, for example, cuts, effects, subtitles, and audio adjustments.

[0836] "Video editing means" refers to the technology and process for processing video files based on editing parameters and generating high-quality video content.

[0837] "Viewer attributes" refers to information about the viewer of a video, including age, gender, viewing time, viewing device, etc.

[0838] "Feedback" is information that provides users with analysis results based on viewer reactions and attributes, and is used to help them edit their next video.

[0839] The system of the present invention allows a video provider to upload unedited videos and input prompts to automatically generate high-quality video content. An embodiment of this system will be described in detail below.

[0840] Video upload

[0841] Users first upload unedited video files to the system, which then send them from their devices to the server, where they are stored in secure storage. This is typically done using HTTP / HTTPS protocols.

[0842] Prompt Input

[0843] Next, users input prompts about their video editing preferences and target audience, such as "for women in their 30s," "in the style of a popular video creator," or "add English subtitles." The prompts are then sent from the device to the server.

[0844] Prompt parsing and editing parameter generation

[0845] The server sends the received prompts to a multimodal AI model for analysis, which has the ability to integrate and analyze data of different formats and types, and generates specific editing parameters based on the prompt information.

[0846] Video editing

[0847] The server uses the generated editing parameters to edit the video. The editing process is automated and includes cutting the video, adding transitions, effects, and adjusting the audio. This is typically done using video editing software such as FFmpeg.

[0848] Outputting edited videos

[0849] Once editing is complete, the server generates a download link for the video and provides it to the user, who can then download the edited, high-quality video content from the provided link.

[0850] Audience analysis and feedback

[0851] This system also has a function for analyzing viewer attributes. The server collects information such as the viewer's age, gender, viewing time, and viewing device, and uses this information to analyze viewer attributes. The analysis results are provided to the user as feedback information, which can be used to help with the next video editing.

[0852] Specific examples

[0853] For example, if a user wants to create "educational content for kids", the following steps would occur:

[0854] 1. The user uploads unedited "educational materials for children" to the system via their browser.

[0855] 2. The user enters "For children, fun and educational style, English subtitles added" into the input field as a prompt.

[0856] 3. The server sends the prompt to the multimodal AI model and receives the analysis results.

[0857] 4. The server edits the video using the generated editing parameters. Video editing software such as FFmpeg is used.

[0858] 5. The server generates a download link for the edited video and notifies the user.

[0859] 6. The server provides the analysis results based on viewer attributes as feedback to the user.

[0860] This system not only enables users to easily generate high-quality video content optimized for their target audience without any specialized knowledge, but also provides valuable feedback based on viewer attributes.

[0861] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0862] Step 1:

[0863] The user selects an unedited video file on their device and uploads it to the system. The uploaded video file is sent to the server using HTTP / HTTPS protocol. The server stores the received video file in secure storage.

[0864] Input: Unedited video file

[0865] Output: Video file saved on the server

[0866] Specific behavior:

[0867] 1. The user selects a video file from a browser or a dedicated application.

[0868] 2. The device sends the video file to the server.

[0869] 3. The server receives the video file and stores it on the storage server.

[0870] Step 2:

[0871] The user inputs prompts about their desired video editing style and target audience from their device and sends them to the server, which includes specific instructions.

[0872] Input: prompt statement

[0873] Output: The prompt sent to the server

[0874] Specific behavior:

[0875] 1. The user enters a prompt sentence into an input field in a browser or dedicated application.

[0876] 2. The terminal sends the entered prompt text to the server.

[0877] Step 3:

[0878] The server sends the prompt to the multimodal AI model, which analyzes the prompt and generates the necessary editing parameters, including video cuts, effects, subtitles, and audio adjustments.

[0879] Input: prompt statement

[0880] Output: Edit parameters

[0881] Specific behavior:

[0882] 1. The server retrieves the prompt sentence and sends it to the multimodal AI model.

[0883] 2. The multimodal AI model analyzes the prompt and generates editing parameters.

[0884] 3. The server receives the generated editing parameters and saves them in its internal storage.

[0885] Step 4:

[0886] The server uses the editing parameters to edit the uploaded video. Editing involves cutting the video, adding transitions, adding effects, adjusting the audio, etc. This is done using video editing software such as FFmpeg.

[0887] Input: Editing parameters, raw video file

[0888] Output: Edited video file

[0889] Specific behavior:

[0890] 1. The server opens the video file using video editing software (e.g. FFmpeg).

[0891] 2. The server performs specific editing tasks based on the editing parameters.

[0892] 3. Save the edited video file as a new file.

[0893] Step 5:

[0894] The server generates a download link for the edited video content, which is then provided to the user, who uses it to download the edited video.

[0895] Input: Edited video file

[0896] Output: Download link

[0897] Specific behavior:

[0898] 1. The server saves the edited video file in a specific directory and generates a download URL.

[0899] 2. The server notifies the user of the generated URL.

[0900] Step 6:

[0901] The server provides the user with the analysis results of viewer attributes as feedback. Viewing data is collected and analyzed to analyze viewer attributes such as age, gender, viewing time, and viewing device.

[0902] Input: Viewing data

[0903] Output: Viewer attribute analysis results

[0904] Specific behavior:

[0905] 1. The server collects viewing data.

[0906] 2. The server runs an algorithm to analyze viewer attributes.

[0907] 3. The server generates the analysis results in the form of a report and provides it to the user.

[0908] This allows users to easily create high-quality video content and obtain valuable feedback based on viewer reactions.

[0909] (Application example 1)

[0910] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0911] Modern video content creation requires efficient editing of raw material into high-quality videos. However, traditional video editing processes are time-consuming and labor-intensive, especially when editing tasks require prompt input. To address this issue, there is a growing need for systems that use voice recognition interfaces and visual display devices to enable prompt input and confirmation of editing results in real time.

[0912] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0913] In this invention, the server includes means for receiving unedited video, means for receiving prompts input by a user, means for analyzing the prompts to generate editing parameters, means for editing the video by applying the generated editing parameters, means for outputting the edited video, means for inputting prompts using a voice recognition interface, and means for checking the editing results in real time on a visual display device, thereby enabling users to easily create high-quality video content.

[0914] "Unedited video" refers to video material that has been shot by a user without any editing, in its original form.

[0915] A "prompt" is a command that a user enters in text or voice to provide specific instructions about their video editing preferences or target audience.

[0916] "Editing parameters" are specific settings and operation information for video editing that are generated by AI based on prompts.

[0917] A "voice recognition interface" is a device or function that converts a user's speech into text in real time and inputs it into a system.

[0918] A "visual display device" is a device that uses smart glasses or other display devices to visually display the content of edited video in real time.

[0919] "Real-time" refers to processing and display with no or very little delay.

[0920] "Viewer attributes" refers to characteristic information such as age, sex, and interests of users who view video content.

[0921] The "server" is a central computing device for processing the entire system, and is a device that uploads videos, analyzes prompts, generates editing parameters, and provides edited videos.

[0922] A system for implementing the present invention is configured as follows.

[0923] Overall system configuration

[0924] 1. Video Upload:

[0925] Users use smart glasses or other visual display devices to capture unedited videos, which are then automatically uploaded to a server.

[0926] 2. Prompt input:

[0927] Using the voice recognition interface on the smart glasses, users can speak prompts related to editing preferences and target audiences, such as "Fun and educational for kids, with English subtitles." This speech is converted into text in real time and sent to the server as prompts.

[0928] 3. AI-powered prompt analysis and editing parameter generation:

[0929] The server uses a generative AI model to analyze the received prompts. This model generates optimal editing parameters based on the input prompts, including color adjustments, adding effects, and subtitle placement.

[0930] 4. Video editing:

[0931] The server applies the generated editing parameters to automatically edit the uploaded raw video, using a video editing library such as MoviePy to add transitions, captions, and adjust the audio.

[0932] 5. Visual confirmation of edit results:

[0933] Once editing is complete, the server streams the edited video in real time to a visual display device such as smart glasses, where the user can view it and, if desired, voice further prompts to request additional edits.

[0934] 6. Final output and feedback:

[0935] If the user is satisfied with the edited results, the server saves the edited video as the final output and provides a download link. It also provides the user with analysis results of viewer attributes as feedback to help them create content next time.

[0936] Specific examples

[0937] For example, consider entering the prompt "Fun and educational for kids, with English subtitles."

[0938] Video upload:

[0939] Users take photos of unedited "educational materials for children" using smart glasses and upload them to the server.

[0940] Prompt Input:

[0941] Using the voice recognition interface, the user can say, "Kids-oriented, fun and educational style, English subtitles added."

[0942] AI-powered prompt analysis and editing parameter generation:

[0943] Based on the prompts received, the server generates editing parameters such as optimal colors and character effects for children, fun background music, and the addition of English subtitles.

[0944] Video editing:

[0945] The server uses editing parameters and utilizes the "MoviePy" library to automatically perform video cuts, transitions, add subtitles, adjust audio, and more.

[0946] Visual confirmation of edit results:

[0947] Through the smart glasses, the user can view the edited video in real time and input additional editing instructions by voice if necessary.

[0948] Final output and feedback:

[0949] The server will then publish the final educational content and provide feedback on viewer attributes.

[0950] In this way, the system is designed to enable users to easily create high-quality video content optimized for their target audience.

[0951] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0952] Step 1:

[0953] Video recording and uploading

[0954] A user wearing the smart glasses shoots unedited video, and after shooting is complete, the software in the smart glasses automatically uploads the video file to a server.

[0955] Input: Unedited video files captured by smart glasses.

[0956] Output: Raw video files saved on the server.

[0957] Step 2:

[0958] Speech-to-text prompts

[0959] Using a voice recognition interface built into the smart glasses, users can speak their editorial preferences and prompts regarding target audiences, which are converted into text in real time and sent to the server.

[0960] Input: Voice prompts.

[0961] Output: Prompt data in text format.

[0962] Step 3:

[0963] Prompt parsing and editing parameter generation

[0964] The server receives the text prompts and uses a generative AI model to analyze them and generate editing parameters, which determine appropriate settings for captions, effects, audio adjustments, and more.

[0965] Input: Prompt data in text format.

[0966] Output: Edit parameters.

[0967] Step 4:

[0968] Video editing

[0969] The server uses the generated editing parameters to edit the uploaded raw video, using a video editing library such as MoviePy to add transitions, insert subtitles, synthesize music, and more.

[0970] Input: raw video file, editing parameters.

[0971] Output: Edited video file.

[0972] Step 5:

[0973] Real-time visual confirmation of edit results

[0974] The edited video is streamed in real time to a visual display device such as smart glasses, where the user can view it and provide additional voice-activated editing instructions as needed.

[0975] Input: Edited video file.

[0976] Output: Visual confirmation and further instructions for the user.

[0977] Step 6:

[0978] Final output and feedback provided

[0979] If the user is satisfied with the edited results, the server will save the edited video and provide a download link to the user. At the same time, feedback based on the analysis of viewer attributes will be provided, providing useful information for future content creation.

[0980] Input: Edited video files, viewer demographic data.

[0981] Output: Final edited video, download link, feedback.

[0982] The above processing steps realize a system that allows a user to easily generate high-quality video content and check and modify it in real time through a visual display device.

[0983] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0984] The system of the present invention allows video providers to upload unedited videos, and automatically generates high-quality video content by combining prompt input and emotion recognition using an emotion engine. The specific program processing of this system is explained below in natural language.

[0985] Video upload

[0986] After logging in to the system, users upload unedited video files. The uploaded video files are stored on the server as is. This is the first step in providing videos to the system.

[0987] Prompt Input

[0988] Users input specific prompts about their video editing preferences and target audience, such as "suitable for women in their 30s," "style of popular video creators," "add English subtitles," etc. This prompt information is sent to the server.

[0989] Emotion recognition by emotion engine

[0990] The device uses an emotion engine to recognize the user's emotions when inputting prompts. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, etc., and sends the results to the server.

[0991] AI-powered analysis

[0992] The server receives prompt information from the user and emotion recognition results from the emotion engine, and analyzes them using multimodal AI. The AI ​​generates editing parameters based on this information. These editing parameters include video effects, subtitles, audio adjustments, and effects.

[0993] Video editing

[0994] The server automatically edits the uploaded video file using the generated editing parameters. Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. Furthermore, the server fine-tunes the editing parameters based on the user's emotions to achieve the optimal editing results.

[0995] Final Output

[0996] The server generates the edited video content and provides the download link to the user. It also provides the user with analysis results on viewer attributes as feedback, allowing the user to check viewer reactions and use them to improve their next content creation.

[0997] Specific examples

[0998] For example, when a user creates "educational content for children," the following steps are taken.

[0999] 1. Users upload unedited "educational materials for children" to the system.

[1000] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[1001] 3. The device's emotion engine analyzes the user's emotions when entering the prompt and sends the emotion data to the server.

[1002] 4. The server analyzes the prompt information and emotional data and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[1003] 5. The server edits the video using the editing parameters to generate the completed educational content.

[1004] 6. The server provides the user with a download link for the edited video and provides analysis results on viewer attributes as feedback.

[1005] In this way, the system of the present invention helps users easily create high-quality, emotion-based, optimized video content.

[1006] The processing flow will be explained below.

[1007] Step 1:

[1008] The user logs into the system, accesses the upload page, selects the unedited video file, and clicks the "Upload" button.

[1009] Step 2:

[1010] The device sends the selected video file to the server, where it is temporarily stored.

[1011] Step 3:

[1012] Users access the prompt input page and enter specific instructions for video editing (e.g., "Suitable for a 30-year-old woman," "Style of a popular video creator," "Add English subtitles"), then click the "Submit" button.

[1013] Step 4:

[1014] The device's emotion engine analyzes data such as the user's facial expression and tone of voice when entering prompts, and the analyzed emotion data is sent to the server.

[1015] Step 5:

[1016] The server receives prompt information and emotional data sent by the user, which is then sent to a multimodal AI for analysis.

[1017] Step 6:

[1018] AI analyzes the prompt information and emotional data to generate appropriate editing parameters, including video cut positions, subtitles, audio adjustments, and effects.

[1019] Step 7:

[1020] The server then automatically edits the raw video file using the generated editing parameters, such as cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio.

[1021] Step 8:

[1022] The server generates a video file after the editing process is completed and stores it on the server.

[1023] Step 9:

[1024] The server generates a download link for the edited video file and notifies the user of this link.

[1025] Step 10:

[1026] The server provides the user with analysis results based on viewer attributes as feedback, allowing the user to use this information to improve their content and create their next video.

[1027] Example 2

[1028] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1029] In recent years, with the spread of video sharing platforms, there has been a growing need for many users to easily edit and publish their own videos. However, video editing requires specialized knowledge and skills, making it a difficult task for many users. Furthermore, in order to provide optimal content to viewers, editing must take into account the viewer's attributes and emotions, but this is extremely difficult to do manually. To solve these challenges, a system is needed that allows anyone to easily create high-quality video content.

[1030] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1031] In this invention, the server includes means for receiving unedited video, means for receiving prompts entered by a user, means for analyzing the prompts and generating editing parameters, means for editing the video by applying the generated editing parameters, means for outputting the edited video, means for recognizing the user's emotions, means for analyzing the recognized emotions and reflecting them in the editing parameters, and means for analyzing viewer attributes and feeding the results back to the user. This enables even users without specialized knowledge to generate high-quality video content that reflects viewer attributes and user emotions.

[1032] "Unedited video" refers to video data in its original state without any editing or processing.

[1033] A "prompt" refers to a sentence or keyword that a user enters regarding a specific instruction or request.

[1034] "Editing parameters" refers to data that includes specific editing instructions, such as where to cut a video, adding subtitles, adjusting the audio, etc., that are generated based on prompts and user emotion data.

[1035] "Means for editing videos" refers to a series of processes for automatically processing and correcting video files based on the generated editing parameters.

[1036] "Means for recognizing emotions" refers to technologies and devices that collect and analyze data such as a user's facial expressions and tone of voice to identify the user's emotional state.

[1037] "Viewer attributes" refers to characteristic information such as the age, gender, and interests of the video viewer.

[1038] "Means for analyzing viewer attributes" refers to technologies and algorithms that analyze viewer characteristics and use the results to understand viewer interests and demands.

[1039] "Means for analyzing the user's emotions and reflecting them in the editing parameters" refers to a process for adjusting the editing parameters based on the recognized emotion data to perform optimal video editing.

[1040] The present invention relates to a system for automatically generating high-quality video content based on a user's prompt input and emotion data after receiving unedited video. Specific embodiments of the present invention will be described below.

[1041] A user logs in to the system and uploads an unedited video file, which is then stored on the server and used for subsequent editing.

[1042] Next, the user inputs prompts, including specific requests for editing the video and instructions about the target audience. For example, they might input prompts such as "suitable for women in their 30s," "style of a popular video creator," or "add English subtitles." The prompt information is sent to the server, where it is analyzed and stored in a database.

[1043] The device uses a camera and microphone to capture the user's facial expressions and tone of voice as they input prompts. The captured data is analyzed by an emotion engine to generate user emotion data, which is then sent to the server.

[1044] The server integrates the received prompt information and emotional data and performs analysis using a multimodal generative AI model (e.g., a natural language processing model or an image analysis model). The AI ​​model generates editing parameters based on the input data. These editing parameters include video cut positions, adding subtitles, adjusting audio, etc. The generated editing parameters are stored in a database.

[1045] The server then begins editing the uploaded video file based on the generated editing parameters. This editing process is performed using video editing software (e.g., Adobe Premiere Pro API or FFmpeg). Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. Additionally, the editing parameters are fine-tuned as needed to reflect the user's emotional data.

[1046] Finally, the server generates the edited video and provides the user with a download link. The server also analyzes viewer attributes and provides the results as feedback to the user, allowing the user to check viewer reactions and use them to improve their next content creation.

[1047] As a specific example, if a user wants to create "educational content for children," he or she uploads unedited "educational materials for children" and enters "kids-oriented, fun learning style, add English subtitles" into the prompt. The device's emotion engine analyzes the user's emotion when entering the prompt and sends the emotion data to the server. The server analyzes the prompt information and emotion data, generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles, and edits the video. The server provides the user with a download link for the final edited educational content and provides feedback on the analysis results of viewer attributes.

[1048] In this way, the system of the present invention allows users to easily create high-quality, emotion-optimized video content without requiring specialized knowledge.

[1049] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1050] Step 1:

[1051] A user logs in to the system and uploads an unedited video file. This inputs the unedited video file and sends it to the server. The server saves the received video file in the file system and registers the path of the saved location in the database. This process ensures that the input video file is managed appropriately.

[1052] Input: Unedited video file

[1053] Data processing: Receive and save files, register save paths in the database

[1054] Output: Path to the saved video file

[1055] Specific behavior:

[1056] The user uploads a video file using a browser.

[1057] The server receives the file and stores it in the file system.

[1058] The server registers the location where the video file is saved in a database.

[1059] Step 2:

[1060] The user inputs prompts about their specific video editing preferences and target audience. The prompts are sent to the server, which analyzes them and stores them in a database. This process ensures that the editing instructions based on the user's preferences are properly recorded.

[1061] Input: User prompt text

[1062] Data processing: receiving and analyzing prompts, saving them to a database

[1063] Output: prompt data as analysis results

[1064] Specific behavior:

[1065] The user fills in the prompts in the input form and clicks "Submit."

[1066] The server receives the prompt information and parses its contents.

[1067] The server stores the prompt information in a database.

[1068] Step 3:

[1069] The device uses a camera and microphone to capture the user's facial expressions and tone of voice when the user enters prompts. This data is analyzed by the emotion engine, which generates the user's emotion data and sends it to the server. This process reflects the user's emotion in the edits.

[1070] Input: User's facial expression data and voice data

[1071] Data processing: Analysis using emotion engine, generation of emotion data

[1072] Output: Emotion data as the analysis result

[1073] Specific behavior:

[1074] The device uses a camera and microphone to capture the user's facial expressions and voice.

[1075] The device uses an emotion engine to analyze the data and generate emotion data.

[1076] The terminal transmits the generated emotion data to the server.

[1077] Step 4:

[1078] The server integrates the received prompt information and emotion data and analyzes them using a generative AI model. Editing parameters are generated as a result of the analysis and stored in a database. This process results in optimized editing instructions based on the prompt and emotion.

[1079] Input: prompt information, emotion data

[1080] Data processing: Analysis using generative AI models and generation of editing parameters

[1081] Output: Generated edit parameters

[1082] Specific behavior:

[1083] The server integrates the prompt information and the emotion data.

[1084] The server performs the analysis using the generated AI model.

[1085] The server stores the generated editing parameters in a database.

[1086] Step 5:

[1087] The server then uses video editing software to edit the video based on the generated editing parameters. This editing process includes cutting video, adding subtitles, and adjusting the audio. The editing parameters are also fine-tuned based on the user's emotional data. Finally, the completed edited video is generated.

[1088] Input: Editing parameters, raw video file

[1089] Data processing: Editing process using video editing software

[1090] Output: Edited video file

[1091] Specific behavior:

[1092] The server starts the video editing software based on the editing parameters.

[1093] The server determines where to cut the video, adds subtitles, and adjusts the audio.

[1094] The server fine-tunes the editing parameters by reflecting the user's emotional data.

[1095] Step 6:

[1096] The server generates the edited video and provides the download link to the user. It also analyzes viewer attributes and provides the results as feedback to the user, allowing the user to use this information to improve their next content creation.

[1097] Input: Edited video file

[1098] Data processing: generating download links, analyzing viewer attributes

[1099] Output: Download link, viewer demographic feedback

[1100] Specific behavior:

[1101] The server generates the final edited video.

[1102] The server creates a download link for the generated video.

[1103] The server analyzes viewer attributes and displays the results on the user's dashboard.

[1104] (Application example 2)

[1105] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1106] Conventional video editing systems require manual editing by users and have difficulty generating optimal content based on viewer emotions. Effective video editing is particularly challenging due to a lack of centralized methods for optimizing prompt information and editing parameters. Furthermore, there is a need to provide even higher-quality content by integrating viewer attribute analysis and emotional data into the editing process.

[1107] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving unedited video, means for receiving prompts entered by the user, means for collecting user emotion data using an emotion recognition engine, means for optimizing editing parameters using the emotion data, and means for editing video by applying the generated editing parameters. This enables the automatic generation of high-quality, optimized video content based on user emotion and prompt information. In addition, by feeding back the analysis results of viewer attributes, the results can be used in subsequent content creation.

[1108] "Unedited video" refers to a video file in its original state, shot by a user without any editing.

[1109] "Prompt" refers to text information that a user enters about their video editing preferences and target audience.

[1110] "Editing parameters" refer to specific settings that specify effects, cuts, subtitles, audio adjustments, etc. for specific parts of a video.

[1111] An "emotion recognition engine" refers to a software module that analyzes a user's emotional state from facial expressions, tone of voice, etc.

[1112] "Emotion data" refers to information about a user's emotional state analyzed by an emotion recognition engine.

[1113] "Viewer attributes" refers to characteristic information such as age, gender, region, and interests of the user demographic who watches video content.

[1114] "Feedback" refers to the results of editing video content and analysis of viewer attributes that the system provides to users.

[1115] "Optimization" refers to the process of adjusting the editing parameters of video content based on prompt information and emotional data to create a form that best suits the user's intentions.

[1116] "Server" refers to a computer system that receives video, analyzes prompts and emotional data, generates editing parameters, edits the video, and outputs it.

[1117] The system of the present invention allows users to upload unedited videos they have taken, and automatically generates high-quality video content by combining prompt input and emotion recognition using an emotion engine. This system is implemented in the following steps:

[1118] Video upload

[1119] After logging in to the system, users upload unedited video files, which are then stored on the server as is. This process is the first step in providing videos to the system.

[1120] Prompt Input

[1121] The user inputs prompts, such as specific requests for editing the video or specific target audiences, such as "for teenagers," "strengthen the entertainment element," or "add French subtitles." This prompt information is then sent to the server.

[1122] Emotion recognition by emotion engine

[1123] The device uses an emotion engine to recognize the user's emotions when entering prompts. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, etc., and sends the results to the server. Specifically, the device's camera captures the user's facial expressions, and the emotion recognition module analyzes the data.

[1124] AI-powered analysis

[1125] The server receives prompt information from the user and emotion recognition results from the emotion engine, and analyzes this information using a generative AI model. As a result of the analysis, editing parameters are generated, including video effects, cuts, subtitles, and audio adjustments.

[1126] Video editing

[1127] The server automatically edits the uploaded video file using the generated editing parameters. Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. The server also fine-tunes the editing parameters based on the user's emotional data to achieve optimal editing results.

[1128] Final Output

[1129] The server generates the edited video content and provides the download link to the user. It also provides the user with analysis results on viewer attributes as feedback, allowing the user to check viewer reactions and use them to improve their next content creation.

[1130] Specific examples

[1131] For example, when a user creates "educational content for children," the following steps are taken.

[1132] 1. Users upload unedited "educational materials for children" to the system.

[1133] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[1134] 3. The device's emotion engine analyzes the user's emotions when entering the prompt and sends the emotion data to the server.

[1135] 4. The server analyzes the prompt information and emotional data and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[1136] 5. The server edits the video using the editing parameters to generate the completed educational content.

[1137] 6. The server provides the user with a download link for the edited video and provides analysis results on viewer attributes as feedback.

[1138] In this way, the system of the present invention helps users easily create high-quality, emotion-based, optimized video content.

[1139] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1140] Step 1: Upload your video

[1141] After logging in to the system, the user uploads an unedited video file. As input, the unedited video file and the user's identification information are required. As output, the video file is saved on the server and an appropriate video ID is generated. Specifically, by selecting the video file and pressing the upload button, the file is sent to the server.

[1142] Step 2: Prompt Input

[1143] The user inputs specific instructions regarding their video editing preferences and target audience as prompts. Text information regarding the editing preferences and target audience is required as input. The prompt information is sent to the server as output. Specifically, the user enters the prompt text in the input field and presses the send button to send the data to the server.

[1144] Step 3: Emotion Recognition

[1145] The device uses an emotion engine to recognize the user's emotions when entering prompts. The input requires the user's facial expression data and voice data. The output is analyzed emotion data and sent to the server. Specifically, the user's facial expressions and voice are captured through a camera and microphone, and analyzed by the emotion recognition engine.

[1146] Step 4: AI analysis

[1147] The server receives prompt information from the user and the emotion recognition results from the emotion engine, and analyzes them using a generative AI model. Prompt information and emotion data are required as input. Editing parameters are generated as output. Specifically, the prompt text and emotion data are input into the AI ​​model, and editing parameters such as color, effects, subtitles, and audio adjustments are generated.

[1148] Step 5: Edit your video

[1149] The server automatically edits the uploaded video file using the generated editing parameters. The input requires an unedited video file and the editing parameters. The output is an edited video file. Specifically, based on the parameters generated by the AI, it automatically performs operations such as cutting the video, adding effects, inserting subtitles, and adjusting the audio.

[1150] Step 6: Final output

[1151] The server generates the edited video content and provides the download link to the user. The edited video file and user identification information are required as input. The download link is generated as output. Analysis results on viewer attributes are also provided as feedback. Specifically, the download link and analysis report are sent to the user via email or notification.

[1152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1153] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1154] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1155] [Fourth embodiment]

[1156] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1160] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1163] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1165] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1167] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1168] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1169] The system of the present invention allows video providers to upload unedited videos and perform prompt input to automatically generate high-quality video content. Specific program processing of this system will be explained below in natural language.

[1170] Video upload

[1171] Users upload raw video files to the system, which are then stored on the server as is. Uploading a video by a user is the first step in providing a video to the system.

[1172] Prompt Input

[1173] Users input specific prompts about their desired video editing style and target audience, such as "suitable for 30-year-old women," "style of a popular video creator," or "add English subtitles." The prompts are then sent to the server.

[1174] AI-powered analysis

[1175] When the server receives a prompt from the user, it sends it to a multimodal AI for analysis. The AI ​​generates appropriate editing parameters based on the prompt information. These editing parameters include video effects, subtitles, and audio adjustments.

[1176] Video editing

[1177] The server uses the generated editing parameters to edit the uploaded video. Specifically, it performs operations such as cutting and transitioning the video, adding subtitles, and adjusting the audio. All edits are applied automatically, allowing users to enjoy high-quality video content without any effort.

[1178] Final Output

[1179] The server generates the edited video content and provides the download link to the user. It can also provide the user with feedback on the video content based on the analysis results of viewer attributes. This allows the user to check viewer reactions and use them to improve their next content creation.

[1180] Specific examples

[1181] For example, when a user creates "educational content for children," the following steps are taken.

[1182] 1. Users upload unedited "educational materials for children" to the system.

[1183] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[1184] 3. The server analyzes the prompts and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[1185] 4. The server edits the video using the editing parameters to generate the completed educational content.

[1186] 5. The server provides the user with a download link for the edited video and provides feedback on the analysis results of viewer attributes.

[1187] In this way, the system of the present invention helps users easily create high-quality video content that is optimized for their target audience.

[1188] The processing flow will be explained below.

[1189] Step 1:

[1190] A user logs into the system and accesses the video upload page, where the user selects an unedited video file and begins uploading.

[1191] Step 2:

[1192] The device sends the selected video file to the server, and once the upload is complete, the server temporarily stores the received video file.

[1193] Step 3:

[1194] The user navigates to a prompt entry page and enters specific instructions about their video editing preferences and target audience (e.g., "suitable for women in their 30s," "style of popular video creators," "add English subtitles"), and then sends the prompt to the server.

[1195] Step 4:

[1196] The server receives prompt information sent by the user and sends it to the multimodal AI for analysis. The prompt information includes the style of the video content, the target audience, and the elements that the user wants to add.

[1197] Step 5:

[1198] AI analyzes the prompt information and generates optimal editing parameters, including video cut positions, subtitles, audio adjustments, and effects.

[1199] Step 6:

[1200] The server automatically edits raw video files using AI-generated editing parameters, such as cutting the video at specified points, adding captions based on prompts, and adjusting the volume and intonation of the audio.

[1201] Step 7:

[1202] The server generates and stores the video file after the editing process is completed.

[1203] Step 8:

[1204] The server creates a download link for the edited video file and notifies the user of this link. It also provides the user with analysis results based on viewer attributes as feedback. Users can use this information to improve their content and create their next video.

[1205] Example 1

[1206] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1207] In today's world, video content is an important means of disseminating information and providing entertainment. However, editing unedited videos to high quality requires specialized knowledge and time. Editing videos that accurately reflect the user's intentions is particularly difficult, and many users are unable to achieve high-quality video editing. Analyzing viewer reactions and obtaining feedback that can be used for future edits is also challenging. There is a need for a system that can solve these problems, allowing users to easily create high-quality video content and receive effective feedback.

[1208] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1209] In this invention, the server includes a means for receiving unedited video, a means for receiving prompts entered by a user, a means for transmitting the prompts to a multimodal AI model, analyzing the prompts, and generating editing parameters, a means for editing the video by applying the generated editing parameters, and a means for outputting the edited video. This allows a user without specialized knowledge to automatically generate high-quality video content simply by entering prompts. Furthermore, by including a means for analyzing viewer attributes and providing feedback to the user, the user can make effective edits based on viewer reactions, thereby continuously improving the quality of the video content.

[1210] "Unedited video" refers to the original video file that has not been subjected to any specific processing or editing.

[1211] A "prompt" is a sentence or keyword entered by a user to describe their video editing needs, requirements, and specific instructions regarding the target audience.

[1212] A "multimodal AI model" is an artificial intelligence model that integrates and analyzes data of different formats and types to generate appropriate editing parameters.

[1213] "Editing parameters" refer to specific operations and setting items designated in video editing, and include, for example, cuts, effects, subtitles, and audio adjustments.

[1214] "Video editing means" refers to the technology and process for processing video files based on editing parameters and generating high-quality video content.

[1215] "Viewer attributes" refers to information about the viewer of a video, including age, gender, viewing time, viewing device, etc.

[1216] "Feedback" is information that provides users with analysis results based on viewer reactions and attributes, and is used to help them edit their next video.

[1217] The system of the present invention allows a video provider to upload unedited videos and input prompts to automatically generate high-quality video content. An embodiment of this system will be described in detail below.

[1218] Video upload

[1219] Users first upload unedited video files to the system, which then send them from their devices to the server, where they are stored in secure storage. This is typically done using HTTP / HTTPS protocols.

[1220] Prompt Input

[1221] Next, users input prompts about their video editing preferences and target audience, such as "for women in their 30s," "in the style of a popular video creator," or "add English subtitles." The prompts are then sent from the device to the server.

[1222] Prompt parsing and editing parameter generation

[1223] The server sends the received prompts to a multimodal AI model for analysis, which has the ability to integrate and analyze data of different formats and types, and generates specific editing parameters based on the prompt information.

[1224] Video editing

[1225] The server uses the generated editing parameters to edit the video. The editing process is automated and includes cutting the video, adding transitions, effects, and adjusting the audio. This is typically done using video editing software such as FFmpeg.

[1226] Outputting edited videos

[1227] Once editing is complete, the server generates a download link for the video and provides it to the user, who can then download the edited, high-quality video content from the provided link.

[1228] Audience analysis and feedback

[1229] This system also has a function for analyzing viewer attributes. The server collects information such as the viewer's age, gender, viewing time, and viewing device, and uses this information to analyze viewer attributes. The analysis results are provided to the user as feedback information, which can be used to help with the next video editing.

[1230] Specific examples

[1231] For example, if a user wants to create "educational content for kids", the following steps would occur:

[1232] 1. The user uploads unedited "educational materials for children" to the system via their browser.

[1233] 2. The user enters "For children, fun and educational style, English subtitles added" into the input field as a prompt.

[1234] 3. The server sends the prompt to the multimodal AI model and receives the analysis results.

[1235] 4. The server edits the video using the generated editing parameters. Video editing software such as FFmpeg is used.

[1236] 5. The server generates a download link for the edited video and notifies the user.

[1237] 6. The server provides the analysis results based on viewer attributes as feedback to the user.

[1238] This system not only enables users to easily generate high-quality video content optimized for their target audience without any specialized knowledge, but also provides valuable feedback based on viewer attributes.

[1239] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1240] Step 1:

[1241] The user selects an unedited video file on their device and uploads it to the system. The uploaded video file is sent to the server using HTTP / HTTPS protocol. The server stores the received video file in secure storage.

[1242] Input: Unedited video file

[1243] Output: Video file saved on the server

[1244] Specific behavior:

[1245] 1. The user selects a video file from a browser or a dedicated application.

[1246] 2. The device sends the video file to the server.

[1247] 3. The server receives the video file and stores it on the storage server.

[1248] Step 2:

[1249] The user inputs prompts about their desired video editing style and target audience from their device and sends them to the server, which includes specific instructions.

[1250] Input: prompt statement

[1251] Output: The prompt sent to the server

[1252] Specific behavior:

[1253] 1. The user enters a prompt sentence into an input field in a browser or dedicated application.

[1254] 2. The terminal sends the entered prompt text to the server.

[1255] Step 3:

[1256] The server sends the prompt to the multimodal AI model, which analyzes the prompt and generates the necessary editing parameters, including video cuts, effects, subtitles, and audio adjustments.

[1257] Input: prompt statement

[1258] Output: Edit parameters

[1259] Specific behavior:

[1260] 1. The server retrieves the prompt sentence and sends it to the multimodal AI model.

[1261] 2. The multimodal AI model analyzes the prompt and generates editing parameters.

[1262] 3. The server receives the generated editing parameters and saves them in its internal storage.

[1263] Step 4:

[1264] The server uses the editing parameters to edit the uploaded video. Editing involves cutting the video, adding transitions, adding effects, adjusting the audio, etc. This is done using video editing software such as FFmpeg.

[1265] Input: Editing parameters, raw video file

[1266] Output: Edited video file

[1267] Specific behavior:

[1268] 1. The server opens the video file using video editing software (e.g. FFmpeg).

[1269] 2. The server performs specific editing tasks based on the editing parameters.

[1270] 3. Save the edited video file as a new file.

[1271] Step 5:

[1272] The server generates a download link for the edited video content, which is then provided to the user, who uses it to download the edited video.

[1273] Input: Edited video file

[1274] Output: Download link

[1275] Specific behavior:

[1276] 1. The server saves the edited video file in a specific directory and generates a download URL.

[1277] 2. The server notifies the user of the generated URL.

[1278] Step 6:

[1279] The server provides the user with the analysis results of viewer attributes as feedback. Viewing data is collected and analyzed to analyze viewer attributes such as age, gender, viewing time, and viewing device.

[1280] Input: Viewing data

[1281] Output: Viewer attribute analysis results

[1282] Specific behavior:

[1283] 1. The server collects viewing data.

[1284] 2. The server runs an algorithm to analyze viewer attributes.

[1285] 3. The server generates the analysis results in the form of a report and provides it to the user.

[1286] This allows users to easily create high-quality video content and obtain valuable feedback based on viewer reactions.

[1287] (Application example 1)

[1288] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1289] Modern video content creation requires efficient editing of raw material into high-quality videos. However, traditional video editing processes are time-consuming and labor-intensive, especially when editing tasks require prompt input. To address this issue, there is a growing need for systems that use voice recognition interfaces and visual display devices to enable prompt input and confirmation of editing results in real time.

[1290] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1291] In this invention, the server includes means for receiving unedited video, means for receiving prompts input by a user, means for analyzing the prompts to generate editing parameters, means for editing the video by applying the generated editing parameters, means for outputting the edited video, means for inputting prompts using a voice recognition interface, and means for checking the editing results in real time on a visual display device, thereby enabling users to easily create high-quality video content.

[1292] "Unedited video" refers to video material that has been shot by a user without any editing, in its original form.

[1293] A "prompt" is a command that a user enters in text or voice to provide specific instructions about their video editing preferences or target audience.

[1294] "Editing parameters" are specific settings and operation information for video editing that are generated by AI based on prompts.

[1295] A "voice recognition interface" is a device or function that converts a user's speech into text in real time and inputs it into a system.

[1296] A "visual display device" is a device that uses smart glasses or other display devices to visually display the content of edited video in real time.

[1297] "Real-time" refers to processing and display with no or very little delay.

[1298] "Viewer attributes" refers to characteristic information such as age, sex, and interests of users who view video content.

[1299] The "server" is a central computing device for processing the entire system, and is a device that uploads videos, analyzes prompts, generates editing parameters, and provides edited videos.

[1300] A system for implementing the present invention is configured as follows.

[1301] Overall system configuration

[1302] 1. Video Upload:

[1303] Users use smart glasses or other visual display devices to capture unedited videos, which are then automatically uploaded to a server.

[1304] 2. Prompt input:

[1305] Using the voice recognition interface on the smart glasses, users can speak prompts related to editing preferences and target audiences, such as "Fun and educational for kids, with English subtitles." This speech is converted into text in real time and sent to the server as prompts.

[1306] 3. AI-powered prompt analysis and editing parameter generation:

[1307] The server uses a generative AI model to analyze the received prompts. This model generates optimal editing parameters based on the input prompts, including color adjustments, adding effects, and subtitle placement.

[1308] 4. Video editing:

[1309] The server applies the generated editing parameters to automatically edit the uploaded raw video, using a video editing library such as MoviePy to add transitions, captions, and adjust the audio.

[1310] 5. Visual confirmation of edit results:

[1311] Once editing is complete, the server streams the edited video in real time to a visual display device such as smart glasses, where the user can view it and, if desired, voice further prompts to request additional edits.

[1312] 6. Final output and feedback:

[1313] If the user is satisfied with the edited results, the server saves the edited video as the final output and provides a download link. It also provides the user with analysis results of viewer attributes as feedback to help them create content next time.

[1314] Specific examples

[1315] For example, consider entering the prompt "Fun and educational for kids, with English subtitles."

[1316] Video upload:

[1317] Users take photos of unedited "educational materials for children" using smart glasses and upload them to the server.

[1318] Prompt Input:

[1319] Using the voice recognition interface, the user can say, "Kids-oriented, fun and educational style, English subtitles added."

[1320] AI-powered prompt analysis and editing parameter generation:

[1321] Based on the prompts received, the server generates editing parameters such as optimal colors and character effects for children, fun background music, and the addition of English subtitles.

[1322] Video editing:

[1323] The server uses editing parameters and utilizes the "MoviePy" library to automatically perform video cuts, transitions, add subtitles, adjust audio, and more.

[1324] Visual confirmation of edit results:

[1325] Through the smart glasses, the user can view the edited video in real time and input additional editing instructions by voice if necessary.

[1326] Final output and feedback:

[1327] The server will then publish the final educational content and provide feedback on viewer attributes.

[1328] In this way, the system is designed to enable users to easily create high-quality video content optimized for their target audience.

[1329] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1330] Step 1:

[1331] Video recording and uploading

[1332] A user wearing the smart glasses shoots unedited video, and after shooting is complete, the software in the smart glasses automatically uploads the video file to a server.

[1333] Input: Unedited video files captured by smart glasses.

[1334] Output: Raw video files saved on the server.

[1335] Step 2:

[1336] Speech-to-text prompts

[1337] Using a voice recognition interface built into the smart glasses, users can speak their editorial preferences and prompts regarding target audiences, which are converted into text in real time and sent to the server.

[1338] Input: Voice prompts.

[1339] Output: Prompt data in text format.

[1340] Step 3:

[1341] Prompt parsing and editing parameter generation

[1342] The server receives the text prompts and uses a generative AI model to analyze them and generate editing parameters, which determine appropriate settings for captions, effects, audio adjustments, and more.

[1343] Input: Prompt data in text format.

[1344] Output: Edit parameters.

[1345] Step 4:

[1346] Video editing

[1347] The server uses the generated editing parameters to edit the uploaded raw video, using a video editing library such as MoviePy to add transitions, insert subtitles, synthesize music, and more.

[1348] Input: raw video file, editing parameters.

[1349] Output: Edited video file.

[1350] Step 5:

[1351] Real-time visual confirmation of edit results

[1352] The edited video is streamed in real time to a visual display device such as smart glasses, where the user can view it and provide additional voice-activated editing instructions as needed.

[1353] Input: Edited video file.

[1354] Output: Visual confirmation and further instructions for the user.

[1355] Step 6:

[1356] Final output and feedback provided

[1357] If the user is satisfied with the edited results, the server will save the edited video and provide a download link to the user. At the same time, feedback based on the analysis of viewer attributes will be provided, providing useful information for future content creation.

[1358] Input: Edited video files, viewer demographic data.

[1359] Output: Final edited video, download link, feedback.

[1360] The above processing steps realize a system that allows a user to easily generate high-quality video content and check and modify it in real time through a visual display device.

[1361] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1362] The system of the present invention allows video providers to upload unedited videos, and automatically generates high-quality video content by combining prompt input and emotion recognition using an emotion engine. The specific program processing of this system is explained below in natural language.

[1363] Video upload

[1364] After logging in to the system, users upload unedited video files. The uploaded video files are stored on the server as is. This is the first step in providing videos to the system.

[1365] Prompt Input

[1366] Users input specific prompts about their video editing preferences and target audience, such as "suitable for women in their 30s," "style of popular video creators," "add English subtitles," etc. This prompt information is sent to the server.

[1367] Emotion recognition by emotion engine

[1368] The device uses an emotion engine to recognize the user's emotions when inputting prompts. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, etc., and sends the results to the server.

[1369] AI-powered analysis

[1370] The server receives prompt information from the user and emotion recognition results from the emotion engine, and analyzes them using multimodal AI. The AI ​​generates editing parameters based on this information. These editing parameters include video effects, subtitles, audio adjustments, and effects.

[1371] Video editing

[1372] The server automatically edits the uploaded video file using the generated editing parameters. Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. Furthermore, the server fine-tunes the editing parameters based on the user's emotions to achieve the optimal editing results.

[1373] Final Output

[1374] The server generates the edited video content and provides the download link to the user. It also provides the user with analysis results on viewer attributes as feedback, allowing the user to check viewer reactions and use them to improve their next content creation.

[1375] Specific examples

[1376] For example, when a user creates "educational content for children," the following steps are taken.

[1377] 1. Users upload unedited "educational materials for children" to the system.

[1378] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[1379] 3. The device's emotion engine analyzes the user's emotions when entering the prompt and sends the emotion data to the server.

[1380] 4. The server analyzes the prompt information and emotional data and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[1381] 5. The server edits the video using the editing parameters to generate the completed educational content.

[1382] 6. The server provides the user with a download link for the edited video and provides analysis results on viewer attributes as feedback.

[1383] In this way, the system of the present invention helps users easily create high-quality, emotion-based, optimized video content.

[1384] The processing flow will be explained below.

[1385] Step 1:

[1386] The user logs into the system, accesses the upload page, selects the unedited video file, and clicks the "Upload" button.

[1387] Step 2:

[1388] The device sends the selected video file to the server, where it is temporarily stored.

[1389] Step 3:

[1390] Users access the prompt input page and enter specific instructions for video editing (e.g., "Suitable for a 30-year-old woman," "Style of a popular video creator," "Add English subtitles"), then click the "Submit" button.

[1391] Step 4:

[1392] The device's emotion engine analyzes data such as the user's facial expression and tone of voice when entering prompts, and the analyzed emotion data is sent to the server.

[1393] Step 5:

[1394] The server receives prompt information and emotional data sent by the user, which is then sent to a multimodal AI for analysis.

[1395] Step 6:

[1396] AI analyzes the prompt information and emotional data to generate appropriate editing parameters, including video cut positions, subtitles, audio adjustments, and effects.

[1397] Step 7:

[1398] The server then automatically edits the raw video file using the generated editing parameters, such as cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio.

[1399] Step 8:

[1400] The server generates a video file after the editing process is completed and stores it on the server.

[1401] Step 9:

[1402] The server generates a download link for the edited video file and notifies the user of this link.

[1403] Step 10:

[1404] The server provides the user with analysis results based on viewer attributes as feedback, allowing the user to use this information to improve their content and create their next video.

[1405] Example 2

[1406] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1407] In recent years, with the spread of video sharing platforms, there has been a growing need for many users to easily edit and publish their own videos. However, video editing requires specialized knowledge and skills, making it a difficult task for many users. Furthermore, in order to provide optimal content to viewers, editing must take into account the viewer's attributes and emotions, but this is extremely difficult to do manually. To solve these challenges, a system is needed that allows anyone to easily create high-quality video content.

[1408] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1409] In this invention, the server includes means for receiving unedited video, means for receiving prompts entered by a user, means for analyzing the prompts and generating editing parameters, means for editing the video by applying the generated editing parameters, means for outputting the edited video, means for recognizing the user's emotions, means for analyzing the recognized emotions and reflecting them in the editing parameters, and means for analyzing viewer attributes and feeding the results back to the user. This enables even users without specialized knowledge to generate high-quality video content that reflects viewer attributes and user emotions.

[1410] "Unedited video" refers to video data in its original state without any editing or processing.

[1411] A "prompt" refers to a sentence or keyword that a user enters regarding a specific instruction or request.

[1412] "Editing parameters" refers to data that includes specific editing instructions, such as where to cut a video, adding subtitles, adjusting the audio, etc., that are generated based on prompts and user emotion data.

[1413] "Means for editing videos" refers to a series of processes for automatically processing and correcting video files based on the generated editing parameters.

[1414] "Means for recognizing emotions" refers to technologies and devices that collect and analyze data such as a user's facial expressions and tone of voice to identify the user's emotional state.

[1415] "Viewer attributes" refers to characteristic information such as the age, gender, and interests of the video viewer.

[1416] "Means for analyzing viewer attributes" refers to technologies and algorithms that analyze viewer characteristics and use the results to understand viewer interests and demands.

[1417] "Means for analyzing the user's emotions and reflecting them in the editing parameters" refers to a process for adjusting the editing parameters based on the recognized emotion data to perform optimal video editing.

[1418] The present invention relates to a system for automatically generating high-quality video content based on a user's prompt input and emotion data after receiving unedited video. Specific embodiments of the present invention will be described below.

[1419] A user logs in to the system and uploads an unedited video file, which is then stored on the server and used for subsequent editing.

[1420] Next, the user inputs prompts, including specific requests for editing the video and instructions about the target audience. For example, they might input prompts such as "suitable for women in their 30s," "style of a popular video creator," or "add English subtitles." The prompt information is sent to the server, where it is analyzed and stored in a database.

[1421] The device uses a camera and microphone to capture the user's facial expressions and tone of voice as they input prompts. The captured data is analyzed by an emotion engine to generate user emotion data, which is then sent to the server.

[1422] The server integrates the received prompt information and emotional data and performs analysis using a multimodal generative AI model (e.g., a natural language processing model or an image analysis model). The AI ​​model generates editing parameters based on the input data. These editing parameters include video cut positions, adding subtitles, adjusting audio, etc. The generated editing parameters are stored in a database.

[1423] The server then begins editing the uploaded video file based on the generated editing parameters. This editing process is performed using video editing software (e.g., Adobe Premiere Pro API or FFmpeg). Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. Additionally, the editing parameters are fine-tuned as needed to reflect the user's emotional data.

[1424] Finally, the server generates the edited video and provides the user with a download link. The server also analyzes viewer attributes and provides the results as feedback to the user, allowing the user to check viewer reactions and use them to improve their next content creation.

[1425] As a specific example, if a user wants to create "educational content for children," he or she uploads unedited "educational materials for children" and enters "kids-oriented, fun learning style, add English subtitles" into the prompt. The device's emotion engine analyzes the user's emotion when entering the prompt and sends the emotion data to the server. The server analyzes the prompt information and emotion data, generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles, and edits the video. The server provides the user with a download link for the final edited educational content and provides feedback on the analysis results of viewer attributes.

[1426] In this way, the system of the present invention allows users to easily create high-quality, emotion-optimized video content without requiring specialized knowledge.

[1427] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1428] Step 1:

[1429] A user logs in to the system and uploads an unedited video file. This inputs the unedited video file and sends it to the server. The server saves the received video file in the file system and registers the path of the saved location in the database. This process ensures that the input video file is managed appropriately.

[1430] Input: Unedited video file

[1431] Data processing: Receive and save files, register save paths in the database

[1432] Output: Path to the saved video file

[1433] Specific behavior:

[1434] The user uploads a video file using a browser.

[1435] The server receives the file and stores it in the file system.

[1436] The server registers the location where the video file is saved in a database.

[1437] Step 2:

[1438] The user inputs prompts about their specific video editing preferences and target audience. The prompts are sent to the server, which analyzes them and stores them in a database. This process ensures that the editing instructions based on the user's preferences are properly recorded.

[1439] Input: User prompt text

[1440] Data processing: receiving and analyzing prompts, saving them to a database

[1441] Output: prompt data as analysis results

[1442] Specific behavior:

[1443] The user fills in the prompts in the input form and clicks "Submit."

[1444] The server receives the prompt information and parses its contents.

[1445] The server stores the prompt information in a database.

[1446] Step 3:

[1447] The device uses a camera and microphone to capture the user's facial expressions and tone of voice when the user enters prompts. This data is analyzed by the emotion engine, which generates the user's emotion data and sends it to the server. This process reflects the user's emotion in the edits.

[1448] Input: User's facial expression data and voice data

[1449] Data processing: Analysis using emotion engine, generation of emotion data

[1450] Output: Emotion data as the analysis result

[1451] Specific behavior:

[1452] The device uses a camera and microphone to capture the user's facial expressions and voice.

[1453] The device uses an emotion engine to analyze the data and generate emotion data.

[1454] The terminal transmits the generated emotion data to the server.

[1455] Step 4:

[1456] The server integrates the received prompt information and emotion data and analyzes them using a generative AI model. Editing parameters are generated as a result of the analysis and stored in a database. This process results in optimized editing instructions based on the prompt and emotion.

[1457] Input: prompt information, emotion data

[1458] Data processing: Analysis using generative AI models and generation of editing parameters

[1459] Output: Generated edit parameters

[1460] Specific behavior:

[1461] The server integrates the prompt information and the emotion data.

[1462] The server performs the analysis using the generated AI model.

[1463] The server stores the generated editing parameters in a database.

[1464] Step 5:

[1465] The server then uses video editing software to edit the video based on the generated editing parameters. This editing process includes cutting video, adding subtitles, and adjusting the audio. The editing parameters are also fine-tuned based on the user's emotional data. Finally, the completed edited video is generated.

[1466] Input: Editing parameters, raw video file

[1467] Data processing: Editing process using video editing software

[1468] Output: Edited video file

[1469] Specific behavior:

[1470] The server starts the video editing software based on the editing parameters.

[1471] The server determines where to cut the video, adds subtitles, and adjusts the audio.

[1472] The server fine-tunes the editing parameters by reflecting the user's emotional data.

[1473] Step 6:

[1474] The server generates the edited video and provides the download link to the user. It also analyzes viewer attributes and provides the results as feedback to the user, allowing the user to use this information to improve their next content creation.

[1475] Input: Edited video file

[1476] Data processing: generating download links, analyzing viewer attributes

[1477] Output: Download link, viewer demographic feedback

[1478] Specific behavior:

[1479] The server generates the final edited video.

[1480] The server creates a download link for the generated video.

[1481] The server analyzes viewer attributes and displays the results on the user's dashboard.

[1482] (Application example 2)

[1483] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1484] Conventional video editing systems require manual editing by users and have difficulty generating optimal content based on viewer emotions. Effective video editing is particularly challenging due to a lack of centralized methods for optimizing prompt information and editing parameters. Furthermore, there is a need to provide even higher-quality content by integrating viewer attribute analysis and emotional data into the editing process.

[1485] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving unedited video, means for receiving prompts entered by the user, means for collecting user emotion data using an emotion recognition engine, means for optimizing editing parameters using the emotion data, and means for editing video by applying the generated editing parameters. This enables the automatic generation of high-quality, optimized video content based on user emotion and prompt information. In addition, by feeding back the analysis results of viewer attributes, the results can be used in subsequent content creation.

[1486] "Unedited video" refers to a video file in its original state, shot by a user without any editing.

[1487] "Prompt" refers to text information that a user enters about their video editing preferences and target audience.

[1488] "Editing parameters" refer to specific settings that specify effects, cuts, subtitles, audio adjustments, etc. for specific parts of a video.

[1489] An "emotion recognition engine" refers to a software module that analyzes a user's emotional state from facial expressions, tone of voice, etc.

[1490] "Emotion data" refers to information about a user's emotional state analyzed by an emotion recognition engine.

[1491] "Viewer attributes" refers to characteristic information such as age, gender, region, and interests of the user demographic who watches video content.

[1492] "Feedback" refers to the results of editing video content and analysis of viewer attributes that the system provides to users.

[1493] "Optimization" refers to the process of adjusting the editing parameters of video content based on prompt information and emotional data to create a form that best suits the user's intentions.

[1494] "Server" refers to a computer system that receives video, analyzes prompts and emotional data, generates editing parameters, edits the video, and outputs it.

[1495] The system of the present invention allows users to upload unedited videos they have taken, and automatically generates high-quality video content by combining prompt input and emotion recognition using an emotion engine. This system is implemented in the following steps:

[1496] Video upload

[1497] After logging in to the system, users upload unedited video files, which are then stored on the server as is. This process is the first step in providing videos to the system.

[1498] Prompt Input

[1499] The user inputs prompts, such as specific requests for editing the video or specific target audiences, such as "for teenagers," "strengthen the entertainment element," or "add French subtitles." This prompt information is then sent to the server.

[1500] Emotion recognition by emotion engine

[1501] The device uses an emotion engine to recognize the user's emotions when entering prompts. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, etc., and sends the results to the server. Specifically, the device's camera captures the user's facial expressions, and the emotion recognition module analyzes the data.

[1502] AI-powered analysis

[1503] The server receives prompt information from the user and emotion recognition results from the emotion engine, and analyzes this information using a generative AI model. As a result of the analysis, editing parameters are generated, including video effects, cuts, subtitles, and audio adjustments.

[1504] Video editing

[1505] The server automatically edits the uploaded video file using the generated editing parameters. Specific editing tasks include cutting the video at specified points, adding captions based on prompts, and adjusting the volume and tone of the audio. The server also fine-tunes the editing parameters based on the user's emotional data to achieve optimal editing results.

[1506] Final Output

[1507] The server generates the edited video content and provides the download link to the user. It also provides the user with analysis results on viewer attributes as feedback, allowing the user to check viewer reactions and use them to improve their next content creation.

[1508] Specific examples

[1509] For example, when a user creates "educational content for children," the following steps are taken.

[1510] 1. Users upload unedited "educational materials for children" to the system.

[1511] 2. The user types "Kids-oriented, fun and educational style, English subtitles added" into the prompt.

[1512] 3. The device's emotion engine analyzes the user's emotions when entering the prompt and sends the emotion data to the server.

[1513] 4. The server analyzes the prompt information and emotional data and generates editing parameters such as optimal colors and character effects for children, fun background music, and adding English subtitles.

[1514] 5. The server edits the video using the editing parameters to generate the completed educational content.

[1515] 6. The server provides the user with a download link for the edited video and provides analysis results on viewer attributes as feedback.

[1516] In this way, the system of the present invention helps users easily create high-quality, emotion-based, optimized video content.

[1517] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1518] Step 1: Upload your video

[1519] After logging in to the system, the user uploads an unedited video file. As input, the unedited video file and the user's identification information are required. As output, the video file is saved on the server and an appropriate video ID is generated. Specifically, by selecting the video file and pressing the upload button, the file is sent to the server.

[1520] Step 2: Prompt Input

[1521] The user inputs specific instructions regarding their video editing preferences and target audience as prompts. Text information regarding the editing preferences and target audience is required as input. The prompt information is sent to the server as output. Specifically, the user enters the prompt text in the input field and presses the send button to send the data to the server.

[1522] Step 3: Emotion Recognition

[1523] The device uses an emotion engine to recognize the user's emotions when entering prompts. The input requires the user's facial expression data and voice data. The output is analyzed emotion data and sent to the server. Specifically, the user's facial expressions and voice are captured through a camera and microphone, and analyzed by the emotion recognition engine.

[1524] Step 4: AI analysis

[1525] The server receives prompt information from the user and the emotion recognition results from the emotion engine, and analyzes them using a generative AI model. Prompt information and emotion data are required as input. Editing parameters are generated as output. Specifically, the prompt text and emotion data are input into the AI ​​model, and editing parameters such as color, effects, subtitles, and audio adjustments are generated.

[1526] Step 5: Edit your video

[1527] The server automatically edits the uploaded video file using the generated editing parameters. The input requires an unedited video file and the editing parameters. The output is an edited video file. Specifically, based on the parameters generated by the AI, it automatically performs operations such as cutting the video, adding effects, inserting subtitles, and adjusting the audio.

[1528] Step 6: Final output

[1529] The server generates the edited video content and provides the download link to the user. The edited video file and user identification information are required as input. The download link is generated as output. Analysis results on viewer attributes are also provided as feedback. Specifically, the download link and analysis report are sent to the user via email or notification.

[1530] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1531] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1532] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1533] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1534] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1535] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1536] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1537] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1538] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1539] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1540] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1541] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1542] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1543] 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.

[1544] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1545] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1546] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1547] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1548] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1549] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1550] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1551] The following is further disclosed regarding the above embodiment.

[1552] (Claim 1)

[1553] a means for receiving the unedited video;

[1554] means for receiving a user-entered prompt;

[1555] means for parsing the prompt to generate edit parameters;

[1556] a means for editing the video by applying the generated editing parameters;

[1557] A means for outputting the edited video;

[1558] A system including:

[1559] (Claim 2)

[1560] 10. The system of claim 1, further comprising means for analyzing viewer attributes of the content based on the prompt and providing feedback on the results.

[1561] (Claim 3)

[1562] 10. The system of claim 1, further comprising means for integrating a plurality of editing parameters to generate optimal video content for the prompt.

[1563] "Example 1"

[1564] (Claim 1)

[1565] a means for receiving the unedited video;

[1566] means for receiving a user-entered prompt;

[1567] means for transmitting the prompts to a multimodal AI model for parsing and generating editing parameters;

[1568] a means for editing the video by applying the generated editing parameters;

[1569] A means for outputting the edited video;

[1570] A system including:

[1571] (Claim 2)

[1572] 10. The system of claim 1, further comprising means for analyzing viewer attributes of the content based on the prompt and providing feedback on the results.

[1573] (Claim 3)

[1574] 10. The system of claim 1, further comprising means for integrating a plurality of editing parameters to generate optimal video content for the prompt.

[1575] "Application Example 1"

[1576] (Claim 1)

[1577] a means for receiving the unedited video;

[1578] means for receiving a user-entered prompt;

[1579] means for parsing the prompt to generate edit parameters;

[1580] a means for editing the video by applying the generated editing parameters;

[1581] A means for outputting the edited video;

[1582] means for inputting prompts using a voice recognition interface;

[1583] A means for checking the edit results in real time on a visual display device;

[1584] A system including:

[1585] (Claim 2)

[1586] 10. The system of claim 1, further comprising means for analyzing viewer attributes of the content based on the prompt and providing feedback on the results.

[1587] (Claim 3)

[1588] 10. The system of claim 1, further comprising means for integrating a plurality of editing parameters to generate optimal video content for the prompt.

[1589] "Example 2: Combining Emotion Engines"

[1590] (Claim 1)

[1591] a means for receiving the unedited video;

[1592] means for receiving a user-entered prompt;

[1593] means for parsing the prompt to generate edit parameters;

[1594] a means for editing the video by applying the generated editing parameters;

[1595] A means for outputting the edited video;

[1596] means for recognizing a user's emotion;

[1597] A means for analyzing the recognized emotion and reflecting it in editing parameters;

[1598] A means for analyzing viewer attributes and providing feedback to users based on the results;

[1599] A system including:

[1600] (Claim 2)

[1601] 10. The system of claim 1, further comprising means for analyzing viewer attributes of the content based on the prompt and providing feedback on the results.

[1602] (Claim 3)

[1603] 10. The system of claim 1, further comprising means for integrating a plurality of editing parameters to generate optimal video content for the prompt.

[1604] "Application example 2 when combining emotion engines"

[1605] (Claim 1)

[1606] a means for receiving the unedited video;

[1607] means for receiving a user-entered prompt;

[1608] means for parsing the prompt to generate edit parameters;

[1609] means for collecting user emotion data using an emotion recognition engine;

[1610] means for optimizing editing parameters using emotion data;

[1611] a means for editing the video by applying the generated editing parameters;

[1612] A means for outputting the edited video;

[1613] A system including:

[1614] (Claim 2)

[1615] 10. The system of claim 1, further comprising means for analyzing viewer attributes of the content based on the prompts and feeding back the results after taking into consideration emotional data.

[1616] (Claim 3)

[1617] 10. The system of claim 1, further comprising means for integrating a plurality of editing parameters to generate video content that best suits the prompt and emotional data. [Explanation of symbols]

[1618] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for receiving the unedited video; means for receiving a user-entered prompt; means for parsing the prompt to generate edit parameters; a means for editing the video by applying the generated editing parameters; A means for outputting the edited video; A system including:

2. The system according to claim 1 , further comprising means for analyzing viewer attributes of the content based on the prompt and providing feedback on the results.

3. The system of claim 1 , further comprising means for integrating a plurality of editing parameters to generate optimal video content for the prompt.

Images