system

Abstract

An object of the system according to the embodiment is to enable even a user with little knowledge of moving image editing to create full-scale moving image content.SOLUTION: A system includes a reception unit, a generation unit, an editing unit, and an adjustment unit. The reception unit uploads the unedited moving image data. The generation unit analyzes the moving image data uploaded by the reception unit and automatically generates subtitles, narration, BGM, and sound effects. The editing unit edits the element generated by the generation unit in accordance with the moving image. The adjustment unit sets a target reproduction time and performs editing by emphasizing a highlight.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] Conventional techniques have had the problem that it is difficult for users with little knowledge of video editing to create full-fledged video content.

[0005] The system according to the embodiment aims to enable even users with little knowledge of video editing to create professional video content. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, an editing unit, and an adjustment unit. The reception unit uploads unedited video data. The generation unit analyzes the video data uploaded by the reception unit and automatically generates subtitles, narration, background music, and sound effects. The editing unit edits the elements generated by the generation unit to fit the video. The adjustment unit sets a target playback time and edits by emphasizing highlights. [Effects of the Invention]

[0007] The system according to the embodiment allows even users with little knowledge of video editing to create professional video content. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A video editing system according to an embodiment of the present invention allows even inexperienced users to smoothly create professional-grade video content. This system allows users to simply upload unedited video data. The AI ​​automatically generates subtitles, narration, background music, sound effects, and other elements, and edits the video to match the video. Furthermore, by setting a target playback time, highlight-like editing is also possible. For example, a user uploads unedited video data. The user must upload the video data in a specific format. The AI ​​then analyzes the input video data and automatically generates subtitles, narration, background music, sound effects, and other elements. The AI ​​understands the content of the video and generates appropriate subtitles and narration. It can also automatically add background music and sound effects that match the atmosphere of the video. Furthermore, the AI ​​edits the video to match the video. For example, sound effects can be added to specific scenes in the video, or narration can be inserted. Furthermore, by setting a target playback time, it is possible to edit the video to match the highlights. This allows even inexperienced users to smoothly create professional-grade video content. This video editing system allows users to easily create compelling video content, enabling self-expression and information dissemination.

[0029] A video editing system according to an embodiment includes a receiving unit, a generating unit, an editing unit, and an adjusting unit. The receiving unit uploads unedited video data. Examples of unedited video data include, but are not limited to, MP4, AVI, and MOV formats. The receiving unit allows a user to upload video data in a specific format. The generating unit uses a generation AI to analyze the video data uploaded by the receiving unit and automatically generate subtitles, narration, background music, and sound effects. The generation AI uses, for example, deep learning or natural language processing technology to understand the content of the video and generate appropriate subtitles and narration. The generating unit can also use the generation AI to automatically add background music and sound effects that match the atmosphere of the video. For example, the generation AI can analyze what is spoken in the video and generate subtitles based on that content. The generation AI can also automatically add background music and sound effects that match the atmosphere of the video. The editing unit edits the elements generated by the generating unit to fit the video. For example, the editing unit can add sound effects or insert narration to specific scenes in the video. For example, the editing unit adds sound effects to specific scenes in a video. The editing unit can also insert narration into specific scenes in a video. The adjustment unit sets a target playback time and edits the video to emphasize highlights. For example, if a user wants to create a five-minute video, the adjustment unit uses the generation AI to extract important scenes from the video and summarize them into a five-minute video. This allows the video editing system according to the embodiment to smoothly create professional-grade video content, even for users with limited knowledge. For example, a user uploads unedited video data, and the generation AI automatically generates subtitles, narration, background music, and sound effects, and edits them to match the video. Furthermore, by setting a target playback time, it is possible to edit the video in a highlight-like manner. This allows users to easily create compelling video content, enabling self-expression and information dissemination.

[0030] The generation unit can use a generation AI to understand the content of the video and generate appropriate subtitles and narration. The generation unit, for example, uses a generation AI to understand the content of the video and generate appropriate subtitles and narration. The generation AI analyzes the content of the video using, for example, deep learning or natural language processing technology. The generation unit can also use the generation AI to analyze what is being said in the video and generate subtitles based on that content. For example, the generation AI analyzes audio data in the video, converts it into text data using speech recognition technology, and generates subtitles based on that text data. The generation unit can also use the generation AI to generate narration based on the content of the video. For example, the generation AI analyzes the content of the video and generates appropriate narration based on that content. This allows the generation AI to understand the content of the video and generate appropriate subtitles and narration, thereby improving the quality of the video. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0031] The generation unit can automatically add background music and sound effects that match the atmosphere of the video using a generation AI. The generation unit, for example, uses a generation AI to automatically add background music and sound effects that match the atmosphere of the video. The generation AI, for example, analyzes the color tone and audio data of the video and selects background music that matches the atmosphere. The generation unit can also automatically add sound effects to specific scenes of the video using the generation AI. For example, the generation AI analyzes scenes in the video and selects and adds appropriate sound effects based on the content of the scene. This increases the appeal of the video by automatically adding background music and sound effects that match the atmosphere of the video using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0032] The editing department can add sound effects and insert narration to specific scenes of the video. The editing department, for example, adds sound effects to specific scenes of the video. For example, the editing department adds sound effects to the climax scene of the video. The editing department can also insert narration to specific scenes of the video. For example, the editing department inserts narration to explanatory scenes of the video. In this way, adding sound effects and inserting narration to specific scenes of the video improves the expressiveness of the video. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI.

[0033] The adjustment unit can set a target playback time and edit the video to emphasize its highlights. For example, if a user wants to create a five-minute video, the generation AI extracts important scenes from the video and summarizes them into five minutes. For example, the adjustment unit extracts the climax scene from the video and edits it to fit the target playback time. The adjustment unit can also extract explanatory scenes from the video and edit them to fit the target playback time. In this way, by setting a target playback time and editing the video to highlight it, a video that meets the user's needs can be created. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI.

[0034] The reception unit can upload video data according to a specific format. The reception unit, for example, enables a user to upload video data according to a specific format. Specific formats include, but are not limited to, MP4, AVI, and MOV formats. Uploading video data according to a specific format improves system compatibility. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0035] The video editing system includes a reception unit that analyzes a user's past upload history and selects the optimal upload method. The reception unit, for example, prioritizes and suggests upload methods (audio, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used during a specific time period based on the user's past upload history. Furthermore, the reception unit can automatically set the format of videos previously uploaded by the user, thereby simplifying the upload procedure. In this way, the optimal upload method can be provided by analyzing the user's past upload history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0036] The video editing system includes a reception unit that filters video data based on a user's current project and areas of interest when uploading the video data. The reception unit, for example, filters the video data so that the user uploads only video data related to the project currently in progress. The reception unit can also prioritize uploading highly relevant video data based on the user's areas of interest. Furthermore, the reception unit can also suggest new related video data by referring to video data previously uploaded by the user. This allows highly relevant video data to be uploaded by filtering based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The video editing system includes a reception unit that selects the optimal uploading means according to a user's input method when uploading video data. For example, when a user issues instructions by voice, the reception unit prioritizes voice input and uploads the video data. In addition, when a user issues instructions by text, the reception unit can also prioritize text input and upload the video data. Furthermore, when a user issues instructions using images, the reception unit can also prioritize image input and upload the video data. This improves upload efficiency by selecting the optimal uploading means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0038] The video editing system includes a reception unit that, when uploading video data, prioritizes uploading highly relevant data in consideration of the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes uploading video data related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize uploading video data related to the travel destination. Furthermore, if the user is participating in an event, the reception unit can also prioritize uploading video data related to the event. In this way, highly relevant video data can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0039] The video editing system includes a reception unit that analyzes a user's social media activity and uploads related data when uploading video data. The reception unit, for example, prioritizes uploading video data shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and upload related video data. Furthermore, the reception unit can also upload related video data by referring to the activity of the user's friends on social media. In this way, related video data can be uploaded by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI.

[0040] The video editing system includes a reception unit that customizes an upload method by reflecting a user's past feedback when uploading video data. The reception unit, for example, suggests an optimal upload method based on feedback provided by the user in the past. The reception unit can also simplify specific upload procedures based on the user's past feedback. Furthermore, the reception unit can also improve error messages displayed during uploading by referring to the user's past feedback. This allows the optimal upload method to be provided by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0041] The generation unit can adjust the level of detail of the subtitles and narration based on the importance of the video during generation. For example, the generation unit generates detailed subtitles and narration for important scenes. The generation unit can also generate concise subtitles and narration for general scenes. Furthermore, the generation unit can generate particularly detailed subtitles and narration for important scenes specified by the user. This allows important scenes to be emphasized by adjusting the level of detail of the subtitles and narration based on the importance of the video. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0042] The generation unit can apply different generation algorithms depending on the category of the video during generation. For example, in the case of an educational video, the generation unit generates detailed subtitles and narration including technical terms. In addition, the generation unit can generate visually appealing subtitles and narration for an entertainment video. Furthermore, the generation unit can generate accurate and concise subtitles and narration for a news video. Thus, by applying different generation algorithms depending on the category of the video, more appropriate videos can be generated. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0043] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit adjusts the generation algorithm, for example, based on feedback from videos the user has generated in the past. The generation unit can also learn specific patterns from the user's past generation results and improve the accuracy of generation. Furthermore, the generation unit can generate a new video by referring to the style of videos the user has generated in the past. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0044] The generation unit can determine the generation priority based on the submission time of the video at the time of generation. For example, the generation unit generates videos with an upcoming deadline with priority. The generation unit can also determine the generation priority based on the submission time specified by the user. Furthermore, the generation unit can postpone the generation of videos with a more distant submission time. In this way, by determining the generation priority based on the submission time of the video, it is possible to generate videos in time for the deadline. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0045] The generation unit can adjust the order of generation based on the relevance of the videos during generation. For example, the generation unit prioritizes generating videos with high relevance specified by the user. The generation unit can also postpone generating videos with low relevance. Furthermore, the generation unit can also prioritize generating videos with high relevance by referring to the user's past generation history. In this way, by adjusting the order of generation based on the relevance of the videos, it is possible to prioritize generating videos with high relevance. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0046] During generation, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates subtitles and narration that use a lot of technical terminology. In addition, if the user is a beginner, the generation unit can generate subtitles and narration that explain things in simple terms. Furthermore, the generation unit can adjust the use of appropriate technical terminology according to the user's level of expertise. In this way, by adjusting the use of technical terminology according to the user's level of expertise, a video that is easy for the user to understand can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0047] During editing, the editing department can add sound effects to specific scenes in the video or insert narration. For example, the editing department can add sound effects to the climax scene of the video. The editing department can also insert narration to explanatory scenes in the video. Furthermore, the editing department can add sound effects that enhance emotions to moving scenes in the video. In this way, adding sound effects to specific scenes in the video or inserting narration improves the expressiveness of the video. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or can be performed without using AI.

[0048] During editing, the editing department can select the optimal editing method based on the content of the video. For example, in the case of an educational video, the editing department selects a visually easy-to-understand editing method. In addition, in the case of an entertainment video, the editing department can also select a visually appealing editing method. Furthermore, in the case of a news video, the editing department can also select an accurate and concise editing method. In this way, by selecting the optimal editing method based on the content of the video, the quality of the video is improved. Some or all of the above-mentioned processing in the editing department may be performed, for example, using AI, or may be performed without using AI.

[0049] When editing, the editing department can improve the accuracy of editing by referring to the user's past editing history. For example, the editing department adjusts the editing algorithm based on feedback from the user's past editing. The editing department can also learn specific patterns from the user's past editing history and improve the accuracy of editing. Furthermore, the editing department can edit new videos by referring to the user's past editing style. In this way, the accuracy of editing is improved by referring to the user's past editing history. Some or all of the above-mentioned processing in the editing department may be performed, for example, using AI or without using AI.

[0050] The editing department can perform editing while taking into account the geographical distribution of the video. For example, the editing department prioritizes editing geographically related scenes based on the shooting location of the video. Furthermore, when a user edits a video related to a specific region, the editing department can add information related to that region. Furthermore, the editing department can perform visually appealing editing by taking into account the geographical distribution of the video. In this way, visually appealing editing can be performed by taking into account the geographical distribution of the video. Some or all of the above-mentioned processing in the editing department may be performed, for example, using AI or without using AI.

[0051] During editing, the editing department can improve the accuracy of editing by referring to literature related to the video. For example, the editing department can refer to literature related to the content of the video and add accurate information. The editing department can also refer to research papers related to the theme of the video and add specialized information. Furthermore, the editing department can also refer to books related to the content of the video and add detailed information. In this way, accurate information can be added by referring to literature related to the video. Some or all of the above-mentioned processing in the editing department can be performed, for example, using AI, or can be performed without using AI.

[0052] The editing department can take into consideration the market value of the video when editing. For example, the editing department evaluates the market value of the video and performs visually appealing editing. The editing department can also prioritize editing of specific scenes by taking into consideration the market value of the video. Furthermore, the editing department can add sound effects and background music to improve the market value of the video. In this way, visually appealing editing can be performed by taking into consideration the market value of the video. Some or all of the above-mentioned processing in the editing department can be performed, for example, using AI, or can be performed without using AI.

[0053] During adjustment, the adjustment unit can extract important scenes from the video and edit them to fit the target playback time. For example, the adjustment unit can extract a climax scene from the video and edit it to fit the target playback time. The adjustment unit can also extract explanatory scenes from the video and edit them to fit the target playback time. Furthermore, the adjustment unit can extract moving scenes from the video and edit them to fit the target playback time. In this way, by extracting important scenes from the video and editing them to fit the target playback time, it is possible to create a video that meets the user's needs. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI.

[0054] During adjustment, the adjustment unit can apply different adjustment algorithms depending on the category of the video. For example, in the case of an educational video, the adjustment unit can apply a visually easy-to-understand adjustment algorithm. In addition, in the case of an entertainment video, the adjustment unit can also apply a visually appealing adjustment algorithm. Furthermore, in the case of a news video, the adjustment unit can also apply an accurate and concise adjustment algorithm. In this way, by applying different adjustment algorithms depending on the category of the video, more appropriate videos can be created. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or without using AI.

[0055] During adjustment, the adjustment unit can improve the accuracy of the adjustment by referring to the user's past adjustment results. The adjustment unit adjusts the adjustment algorithm, for example, based on feedback from adjustments made by the user in the past. The adjustment unit can also learn specific patterns from the user's past adjustment results and improve the accuracy of the adjustment. Furthermore, the adjustment unit can adjust new videos by referring to the adjustment styles made by the user in the past. In this way, the accuracy of the adjustment is improved by referring to the user's past adjustment results. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or without using AI.

[0056] During adjustment, the adjustment unit can determine the priority of adjustment based on the submission date of the video. For example, the adjustment unit prioritizes adjustment of videos with an upcoming deadline. The adjustment unit can also determine the priority of adjustment based on the submission date specified by the user. Furthermore, the adjustment unit can postpone adjustment of videos with a more distant submission date. In this way, by determining the priority of adjustment based on the submission date of the video, it is possible to adjust videos so as to meet the deadline. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or without using AI.

[0057] During adjustment, the adjustment unit can adjust the order of adjustment based on the relevance of the videos. For example, the adjustment unit prioritizes adjustment of videos with high relevance specified by the user. The adjustment unit can also postpone adjustment of videos with low relevance. Furthermore, the adjustment unit can also prioritize adjustment of videos with high relevance by referring to the user's past adjustment history. In this way, by adjusting the order of adjustment based on the relevance of the videos, it is possible to prioritize adjustment of videos with high relevance. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI.

[0058] During adjustment, the adjustment unit can customize the adjustment method according to the user's level of expertise. For example, if the user has expertise, the adjustment unit can provide a professional adjustment method. Furthermore, if the user is a beginner, the adjustment unit can also provide a simple adjustment method. Furthermore, the adjustment unit can suggest an appropriate adjustment method according to the user's level of expertise. In this way, by customizing the adjustment method according to the user's level of expertise, it is possible to create an optimal video for the user. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The video editing system includes an editing unit that analyzes a user's past viewing history and proposes an optimal editing style. The editing unit, for example, analyzes the style of videos the user has previously viewed and proposes a similar editing style. The editing unit can also propose an editing style suitable for a specific genre based on the user's viewing history. Furthermore, the editing unit can also propose an optimal editing style based on the user's evaluation of videos they have previously viewed. In this way, the optimal editing style can be provided by analyzing the user's past viewing history. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or may be performed without using AI.

[0061] The video editing system includes an editing unit that proposes an optimal editing method in consideration of the user's current activity status. For example, if the user is at work, the editing unit proposes an efficient editing method. Furthermore, if the user is on vacation, the editing unit can also propose a relaxed editing method. Furthermore, if the user is participating in an event, the editing unit can also propose an editing method suitable for that event. In this way, the optimal editing method can be provided by considering the user's current activity status. Some or all of the above-mentioned processing in the editing unit may be performed, for example, using AI, or may be performed without using AI.

[0062] The video editing system includes an editing unit that proposes an optimal editing method taking into account the performance of a user's device. For example, if the user is using a high-performance device, the editing unit proposes an advanced editing method. In addition, if the user is using a low-performance device, the editing unit can also propose a lightweight editing method. Furthermore, the editing unit can also propose an optimal editing method taking into account the storage capacity of the user's device. In this way, the optimal editing method can be provided by taking into account the performance of the user's device. Some or all of the above-mentioned processing in the editing unit may be performed, for example, using AI, or may be performed without using AI.

[0063] The video editing system includes an editing unit that proposes an optimal editing method taking into account the user's internet connection status. For example, if the user has a high-speed internet connection, the editing unit proposes high-resolution video editing. Alternatively, if the user has a slow internet connection, the editing unit can also propose low-resolution video editing. Furthermore, the editing unit can propose an optimal editing method taking into account the stability of the user's internet connection. This makes it possible to provide an optimal editing method by taking into account the user's internet connection status. Some or all of the above-described processing in the editing unit may be performed, for example, using AI or without using AI.

[0064] The video editing system includes an editing unit that proposes an optimal editing method taking into account the language used by the user. For example, if the user speaks English, the editing unit proposes English subtitles and narration. Furthermore, if the user speaks Japanese, the editing unit can also propose Japanese subtitles and narration. Furthermore, if the user speaks multiple languages, the editing unit can also propose subtitles and narration in multiple languages. This makes it possible to provide an optimal editing method by taking into account the language used by the user. Some or all of the above-mentioned processing in the editing unit may be performed, for example, using AI, or may be performed without using AI.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit uploads unedited video data. Unedited video data may be in formats such as, but not limited to, MP4, AVI, and MOV. The reception unit allows the user to upload video data in a specific format. Step 2: The generation unit uses the generation AI to analyze the video data uploaded by the reception unit and automatically generate subtitles, narration, background music, and sound effects. The generation AI uses deep learning and natural language processing technology to understand the content of the video and generate appropriate subtitles and narration. The generation AI can also automatically add background music and sound effects that match the atmosphere of the video. Step 3: The editing department edits the elements generated by the generation department to fit the video. The editing department can add sound effects or insert narration to specific scenes in the video. Step 4: The adjustment unit sets a target playback time and edits the video to emphasize the highlights. For example, if a user wants to create a 5-minute video, the generation AI will extract the important scenes from the video and summarize them into a 5-minute video.

[0067] (Example 2) A video editing system according to an embodiment of the present invention allows even inexperienced users to smoothly create professional-grade video content. This system allows users to simply upload unedited video data. The AI ​​automatically generates subtitles, narration, background music, sound effects, and other elements, and edits the video to match the video. Furthermore, by setting a target playback time, highlight-like editing is also possible. For example, a user uploads unedited video data. The user must upload the video data in a specific format. The AI ​​then analyzes the input video data and automatically generates subtitles, narration, background music, sound effects, and other elements. The AI ​​understands the content of the video and generates appropriate subtitles and narration. It can also automatically add background music and sound effects that match the atmosphere of the video. Furthermore, the AI ​​edits the video to match the video. For example, sound effects can be added to specific scenes in the video, or narration can be inserted. Furthermore, by setting a target playback time, it is possible to edit the video to match the highlights. This allows even inexperienced users to smoothly create professional-grade video content. This video editing system allows users to easily create compelling video content, enabling self-expression and information dissemination.

[0068] A video editing system according to an embodiment includes a receiving unit, a generating unit, an editing unit, and an adjusting unit. The receiving unit uploads unedited video data. Examples of unedited video data include, but are not limited to, MP4, AVI, and MOV formats. The receiving unit allows a user to upload video data in a specific format. The generating unit uses a generation AI to analyze the video data uploaded by the receiving unit and automatically generate subtitles, narration, background music, and sound effects. The generation AI uses, for example, deep learning or natural language processing technology to understand the content of the video and generate appropriate subtitles and narration. The generating unit can also use the generation AI to automatically add background music and sound effects that match the atmosphere of the video. For example, the generation AI can analyze what is spoken in the video and generate subtitles based on that content. The generation AI can also automatically add background music and sound effects that match the atmosphere of the video. The editing unit edits the elements generated by the generating unit to fit the video. For example, the editing unit can add sound effects or insert narration to specific scenes in the video. For example, the editing unit adds sound effects to specific scenes in a video. The editing unit can also insert narration into specific scenes in a video. The adjustment unit sets a target playback time and edits the video to emphasize highlights. For example, if a user wants to create a five-minute video, the adjustment unit uses the generation AI to extract important scenes from the video and summarize them into a five-minute video. This allows the video editing system according to the embodiment to smoothly create professional-grade video content, even for users with limited knowledge. For example, a user uploads unedited video data, and the generation AI automatically generates subtitles, narration, background music, and sound effects, and edits them to match the video. Furthermore, by setting a target playback time, it is possible to edit the video in a highlight-like manner. This allows users to easily create compelling video content, enabling self-expression and information dissemination.

[0069] The generation unit can use a generation AI to understand the content of the video and generate appropriate subtitles and narration. The generation unit, for example, uses a generation AI to understand the content of the video and generate appropriate subtitles and narration. The generation AI analyzes the content of the video using, for example, deep learning or natural language processing technology. The generation unit can also use the generation AI to analyze what is being said in the video and generate subtitles based on that content. For example, the generation AI analyzes audio data in the video, converts it into text data using speech recognition technology, and generates subtitles based on that text data. The generation unit can also use the generation AI to generate narration based on the content of the video. For example, the generation AI analyzes the content of the video and generates appropriate narration based on that content. This allows the generation AI to understand the content of the video and generate appropriate subtitles and narration, thereby improving the quality of the video. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0070] The generation unit can automatically add background music and sound effects that match the atmosphere of the video using a generation AI. The generation unit, for example, uses a generation AI to automatically add background music and sound effects that match the atmosphere of the video. The generation AI, for example, analyzes the color tone and audio data of the video and selects background music that matches the atmosphere. The generation unit can also automatically add sound effects to specific scenes of the video using the generation AI. For example, the generation AI analyzes scenes in the video and selects and adds appropriate sound effects based on the content of the scene. This increases the appeal of the video by automatically adding background music and sound effects that match the atmosphere of the video using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0071] The editing department can add sound effects and insert narration to specific scenes of the video. The editing department, for example, adds sound effects to specific scenes of the video. For example, the editing department adds sound effects to the climax scene of the video. The editing department can also insert narration to specific scenes of the video. For example, the editing department inserts narration to explanatory scenes of the video. In this way, adding sound effects and inserting narration to specific scenes of the video improves the expressiveness of the video. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI.

[0072] The adjustment unit can set a target playback time and edit the video to emphasize its highlights. For example, if a user wants to create a five-minute video, the generation AI extracts important scenes from the video and summarizes them into five minutes. For example, the adjustment unit extracts the climax scene from the video and edits it to fit the target playback time. The adjustment unit can also extract explanatory scenes from the video and edit them to fit the target playback time. In this way, by setting a target playback time and editing the video to highlight it, a video that meets the user's needs can be created. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI.

[0073] The reception unit can upload video data according to a specific format. The reception unit, for example, enables a user to upload video data according to a specific format. Specific formats include, but are not limited to, MP4, AVI, and MOV formats. Uploading video data according to a specific format improves system compatibility. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The video editing system includes a reception unit that estimates a user's emotions and adjusts the timing of uploading video data based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit simplifies the upload procedure to enable quick uploads. Furthermore, if the user is relaxed, the reception unit can provide detailed upload options and suggest a customizable upload method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick uploads of video data. This improves user convenience by adjusting the upload timing according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI.

[0075] The video editing system includes a reception unit that analyzes a user's past upload history and selects the optimal upload method. The reception unit, for example, prioritizes and suggests upload methods (audio, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used during a specific time period based on the user's past upload history. Furthermore, the reception unit can automatically set the format of videos previously uploaded by the user, thereby simplifying the upload procedure. In this way, the optimal upload method can be provided by analyzing the user's past upload history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0076] The video editing system includes a reception unit that filters video data based on a user's current project and areas of interest when uploading the video data. The reception unit, for example, filters the video data so that the user uploads only video data related to the project currently in progress. The reception unit can also prioritize uploading highly relevant video data based on the user's areas of interest. Furthermore, the reception unit can also suggest new related video data by referring to video data previously uploaded by the user. This allows highly relevant video data to be uploaded by filtering based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0077] The video editing system includes a reception unit that selects the optimal uploading means according to a user's input method when uploading video data. For example, when a user issues instructions by voice, the reception unit prioritizes voice input and uploads the video data. In addition, when a user issues instructions by text, the reception unit can also prioritize text input and upload the video data. Furthermore, when a user issues instructions using images, the reception unit can also prioritize image input and upload the video data. This improves upload efficiency by selecting the optimal uploading means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The video editing system includes a reception unit that estimates a user's emotions and determines the priority of video data to be uploaded based on the estimated user emotions. For example, if the user is excited, the reception unit prioritizes uploading important video data. Furthermore, if the user is relaxed, the reception unit can also prioritize uploading detailed video data. Furthermore, if the user is in a hurry, the reception unit can also prioritize uploading video data that can be uploaded in a short time. Thus, by determining the priority of video data according to the user's emotions, important video data can be uploaded preferentially. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI.

[0079] The video editing system includes a reception unit that, when uploading video data, prioritizes uploading highly relevant data in consideration of the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes uploading video data related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize uploading video data related to the travel destination. Furthermore, if the user is participating in an event, the reception unit can also prioritize uploading video data related to the event. In this way, highly relevant video data can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0080] The video editing system includes a reception unit that analyzes a user's social media activity and uploads related data when uploading video data. The reception unit, for example, prioritizes uploading video data shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and upload related video data. Furthermore, the reception unit can also upload related video data by referring to the activity of the user's friends on social media. In this way, related video data can be uploaded by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI.

[0081] The video editing system includes a reception unit that customizes an upload method by reflecting a user's past feedback when uploading video data. The reception unit, for example, suggests an optimal upload method based on feedback provided by the user in the past. The reception unit can also simplify specific upload procedures based on the user's past feedback. Furthermore, the reception unit can also improve error messages displayed during uploading by referring to the user's past feedback. This allows the optimal upload method to be provided by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0082] The video editing system includes a generation unit that estimates a user's emotions and adjusts the expression method of subtitles and narration based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates narration with a relaxed tone. Furthermore, if the user is excited, the generation unit can also generate narration with an energetic tone. Furthermore, if the user is sad, the generation unit can also generate narration with a calm tone. This allows for the generation of more appropriate videos by adjusting the expression method of subtitles and narration according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI.

[0083] The generation unit can adjust the level of detail of the subtitles and narration based on the importance of the video during generation. For example, the generation unit generates detailed subtitles and narration for important scenes. The generation unit can also generate concise subtitles and narration for general scenes. Furthermore, the generation unit can generate particularly detailed subtitles and narration for important scenes specified by the user. This allows important scenes to be emphasized by adjusting the level of detail of the subtitles and narration based on the importance of the video. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0084] The generation unit can apply different generation algorithms depending on the category of the video during generation. For example, in the case of an educational video, the generation unit generates detailed subtitles and narration including technical terms. In addition, the generation unit can generate visually appealing subtitles and narration for an entertainment video. Furthermore, the generation unit can generate accurate and concise subtitles and narration for a news video. Thus, by applying different generation algorithms depending on the category of the video, more appropriate videos can be generated. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0085] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit adjusts the generation algorithm, for example, based on feedback from videos the user has generated in the past. The generation unit can also learn specific patterns from the user's past generation results and improve the accuracy of generation. Furthermore, the generation unit can generate a new video by referring to the style of videos the user has generated in the past. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0086] The video editing system includes a generation unit that estimates a user's emotions and adjusts the length of subtitles and narration based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates short, concise subtitles and narration. If the user is relaxed, the generation unit can also generate longer subtitles and narration with detailed explanations. Furthermore, if the user is excited, the generation unit can also generate subtitles and narration with visually stimulating effects. This allows for the generation of more appropriate videos by adjusting the length of the subtitles and narration according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI.

[0087] The generation unit can determine the generation priority based on the submission time of the video at the time of generation. For example, the generation unit generates videos with an upcoming deadline with priority. The generation unit can also determine the generation priority based on the submission time specified by the user. Furthermore, the generation unit can postpone the generation of videos with a more distant submission time. In this way, by determining the generation priority based on the submission time of the video, it is possible to generate videos in time for the deadline. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0088] The generation unit can adjust the order of generation based on the relevance of the videos during generation. For example, the generation unit prioritizes generating videos with high relevance specified by the user. The generation unit can also postpone generating videos with low relevance. Furthermore, the generation unit can also prioritize generating videos with high relevance by referring to the user's past generation history. In this way, by adjusting the order of generation based on the relevance of the videos, it is possible to prioritize generating videos with high relevance. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0089] During generation, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates subtitles and narration that use a lot of technical terminology. In addition, if the user is a beginner, the generation unit can generate subtitles and narration that explain things in simple terms. Furthermore, the generation unit can adjust the use of appropriate technical terminology according to the user's level of expertise. In this way, by adjusting the use of technical terminology according to the user's level of expertise, a video that is easy for the user to understand can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0090] The video editing system includes an editing unit that estimates a user's emotions and adjusts the editing method based on the estimated user emotions. For example, if the user is relaxed, the editing unit performs editing at a leisurely pace. Furthermore, if the user is in a hurry, the editing unit can select a method to quickly complete the editing. Furthermore, if the user is excited, the editing unit can perform editing that adds visually stimulating effects. This allows for more appropriate editing by adjusting the editing method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or without AI.

[0091] During editing, the editing department can add sound effects to specific scenes in the video or insert narration. For example, the editing department can add sound effects to the climax scene of the video. The editing department can also insert narration to explanatory scenes in the video. Furthermore, the editing department can add sound effects that enhance emotions to moving scenes in the video. In this way, adding sound effects to specific scenes in the video or inserting narration improves the expressiveness of the video. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or can be performed without using AI.

[0092] During editing, the editing department can select the optimal editing method based on the content of the video. For example, in the case of an educational video, the editing department selects a visually easy-to-understand editing method. In addition, in the case of an entertainment video, the editing department can also select a visually appealing editing method. Furthermore, in the case of a news video, the editing department can also select an accurate and concise editing method. In this way, by selecting the optimal editing method based on the content of the video, the quality of the video is improved. Some or all of the above-mentioned processing in the editing department may be performed, for example, using AI, or may be performed without using AI.

[0093] When editing, the editing department can improve the accuracy of editing by referring to the user's past editing history. For example, the editing department adjusts the editing algorithm based on feedback from the user's past editing. The editing department can also learn specific patterns from the user's past editing history and improve the accuracy of editing. Furthermore, the editing department can edit new videos by referring to the user's past editing style. In this way, the accuracy of editing is improved by referring to the user's past editing history. Some or all of the above-mentioned processing in the editing department may be performed, for example, using AI or without using AI.

[0094] The video editing system includes an editing unit that estimates a user's emotions and determines editing priorities based on the estimated user emotions. For example, if the user is excited, the editing unit prioritizes editing important scenes. Furthermore, if the user is relaxed, the editing unit can also prioritize editing detailed scenes. Furthermore, if the user is in a hurry, the editing unit can prioritize editing scenes that can be edited in a short amount of time. Thus, by determining editing priorities according to the user's emotions, important scenes can be prioritized for editing. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or without AI.

[0095] The editing department can perform editing while taking into account the geographical distribution of the video. For example, the editing department prioritizes editing geographically related scenes based on the shooting location of the video. Furthermore, when a user edits a video related to a specific region, the editing department can add information related to that region. Furthermore, the editing department can perform visually appealing editing by taking into account the geographical distribution of the video. In this way, visually appealing editing can be performed by taking into account the geographical distribution of the video. Some or all of the above-mentioned processing in the editing department may be performed, for example, using AI or without using AI.

[0096] During editing, the editing department can improve the accuracy of editing by referring to literature related to the video. For example, the editing department can refer to literature related to the content of the video and add accurate information. The editing department can also refer to research papers related to the theme of the video and add specialized information. Furthermore, the editing department can also refer to books related to the content of the video and add detailed information. In this way, accurate information can be added by referring to literature related to the video. Some or all of the above-mentioned processing in the editing department can be performed, for example, using AI, or can be performed without using AI.

[0097] The editing department can take into consideration the market value of the video when editing. For example, the editing department evaluates the market value of the video and performs visually appealing editing. The editing department can also prioritize editing of specific scenes by taking into consideration the market value of the video. Furthermore, the editing department can add sound effects and background music to improve the market value of the video. In this way, visually appealing editing can be performed by taking into consideration the market value of the video. Some or all of the above-mentioned processing in the editing department can be performed, for example, using AI, or can be performed without using AI.

[0098] The video editing system includes an adjustment unit that estimates a user's emotion and adjusts a target playback time setting based on the estimated user's emotion. For example, if the user is relaxed, the adjustment unit sets a longer target playback time. Furthermore, if the user is in a hurry, the adjustment unit can also set a shorter target playback time. Furthermore, if the user is excited, the adjustment unit can also set a target playback time that includes many visually stimulating scenes. This allows for the creation of a more appropriate video by adjusting the target playback time setting according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or without AI.

[0099] During adjustment, the adjustment unit can extract important scenes from the video and edit them to fit the target playback time. For example, the adjustment unit can extract a climax scene from the video and edit it to fit the target playback time. The adjustment unit can also extract explanatory scenes from the video and edit them to fit the target playback time. Furthermore, the adjustment unit can extract moving scenes from the video and edit them to fit the target playback time. In this way, by extracting important scenes from the video and editing them to fit the target playback time, it is possible to create a video that meets the user's needs. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI.

[0100] During adjustment, the adjustment unit can apply different adjustment algorithms depending on the category of the video. For example, in the case of an educational video, the adjustment unit can apply a visually easy-to-understand adjustment algorithm. In addition, in the case of an entertainment video, the adjustment unit can also apply a visually appealing adjustment algorithm. Furthermore, in the case of a news video, the adjustment unit can also apply an accurate and concise adjustment algorithm. In this way, by applying different adjustment algorithms depending on the category of the video, more appropriate videos can be created. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or without using AI.

[0101] During adjustment, the adjustment unit can improve the accuracy of the adjustment by referring to the user's past adjustment results. The adjustment unit adjusts the adjustment algorithm, for example, based on feedback from adjustments made by the user in the past. The adjustment unit can also learn specific patterns from the user's past adjustment results and improve the accuracy of the adjustment. Furthermore, the adjustment unit can adjust new videos by referring to the adjustment styles made by the user in the past. In this way, the accuracy of the adjustment is improved by referring to the user's past adjustment results. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or without using AI.

[0102] The video editing system includes an adjustment unit that estimates a user's emotions and determines adjustment priorities based on the estimated user emotions. For example, when the user is excited, the adjustment unit prioritizes adjusting important scenes. Furthermore, when the user is relaxed, the adjustment unit can also prioritize adjusting detailed scenes. Furthermore, when the user is in a hurry, the adjustment unit can prioritize adjusting scenes that can be adjusted in a short time. Thus, by determining the adjustment priorities according to the user's emotions, important scenes can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or without AI.

[0103] During adjustment, the adjustment unit can determine the priority of adjustment based on the submission date of the video. For example, the adjustment unit prioritizes adjustment of videos with an upcoming deadline. The adjustment unit can also determine the priority of adjustment based on the submission date specified by the user. Furthermore, the adjustment unit can postpone adjustment of videos with a more distant submission date. In this way, by determining the priority of adjustment based on the submission date of the video, it is possible to adjust videos so as to meet the deadline. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or without using AI.

[0104] During adjustment, the adjustment unit can adjust the order of adjustment based on the relevance of the videos. For example, the adjustment unit prioritizes adjustment of videos with high relevance specified by the user. The adjustment unit can also postpone adjustment of videos with low relevance. Furthermore, the adjustment unit can also prioritize adjustment of videos with high relevance by referring to the user's past adjustment history. In this way, by adjusting the order of adjustment based on the relevance of the videos, it is possible to prioritize adjustment of videos with high relevance. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI.

[0105] During adjustment, the adjustment unit can customize the adjustment method according to the user's level of expertise. For example, if the user has expertise, the adjustment unit can provide a professional adjustment method. Furthermore, if the user is a beginner, the adjustment unit can also provide a simple adjustment method. Furthermore, the adjustment unit can suggest an appropriate adjustment method according to the user's level of expertise. In this way, by customizing the adjustment method according to the user's level of expertise, it is possible to create an optimal video for the user. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, editing unit, and adjustment unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can upload unedited video data using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the video data using a generation AI to automatically generate subtitles, narration, background music, and sound effects. The editing unit is realized by the control unit 46A of the smart device 14 and edits the generated elements to match the video. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and sets a target playback time and edits by emphasizing highlights. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, editing unit, and adjustment unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can upload unedited video data using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the video data using a generation AI to automatically generate subtitles, narration, background music, and sound effects. The editing unit is realized by the control unit 46A of the smart glasses 214 and edits the generated elements to match the video. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and sets a target playback time and edits by emphasizing highlights. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, editing unit, and adjustment unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can upload unedited video data using the microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the video data using a generation AI to automatically generate subtitles, narration, background music, and sound effects. The editing unit is realized by the control unit 46A of the headset-type terminal 314, and edits the generated elements to match the video. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12, and sets a target playback time and edits by emphasizing highlights. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, editing unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can upload unedited video data using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the video data using a generation AI to automatically generate subtitles, narration, background music, and sound effects. The editing unit is realized by the control unit 46A of the robot 414, and edits the generated elements to match the video. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12, and sets a target playback time and edits by emphasizing highlights.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The video editing system includes a generation unit that estimates a user's emotion and adjusts the color tone of a video based on the estimated user's emotion. For example, if the user is relaxed, the generation unit emphasizes warm colors. If the user is excited, the generation unit can also emphasize vibrant colors. If the user is sad, the generation unit can also emphasize muted colors. This allows for the generation of a more visually appealing video by adjusting the color tone of the video according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI.

[0108] The video editing system includes an editing unit that estimates a user's emotions and adjusts the tempo of a video based on the estimated user's emotions. For example, if the user is relaxed, the editing unit edits the video at a slower tempo. Furthermore, if the user is excited, the editing unit can also edit the video at a faster tempo. Furthermore, if the user is sad, the editing unit can also edit the video at a slower tempo. By adjusting the video tempo according to the user's emotions, a more visually appealing video can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or without AI.

[0109] The video editing system includes an editing unit that estimates a user's emotions and adjusts video effects based on the estimated user emotions. For example, the editing unit adds a calming effect when the user is relaxed. The editing unit can also add a dynamic effect when the user is excited. Furthermore, the editing unit can add a calming effect when the user is sad. By adjusting the video effects according to the user's emotions, a more visually appealing video can be generated. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or without AI.

[0110] The video editing system includes an editing unit that estimates a user's emotions and adjusts video transitions based on the estimated user emotions. For example, the editing unit uses smooth transitions when the user is relaxed. The editing unit can also use fast transitions when the user is excited. Furthermore, the editing unit can also use gentle transitions when the user is sad. By adjusting video transitions according to the user's emotions, a more visually appealing video can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or without AI.

[0111] The video editing system includes an adjustment unit that estimates a user's emotion and adjusts the volume of a video based on the estimated user's emotion. For example, the adjustment unit sets the volume low when the user is relaxed. The adjustment unit can also set the volume high when the user is excited. The adjustment unit can also set the volume to medium when the user is sad. This allows for adjusting the volume of a video according to the user's emotion, thereby generating a more visually appealing video. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or without AI.

[0112] The video editing system includes an editing unit that analyzes a user's past viewing history and proposes an optimal editing style. The editing unit, for example, analyzes the style of videos the user has previously viewed and proposes a similar editing style. The editing unit can also propose an editing style suitable for a specific genre based on the user's viewing history. Furthermore, the editing unit can also propose an optimal editing style based on the user's evaluation of videos they have previously viewed. In this way, the optimal editing style can be provided by analyzing the user's past viewing history. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or may be performed without using AI.

[0113] The video editing system includes an editing unit that proposes an optimal editing method in consideration of the user's current activity status. For example, if the user is at work, the editing unit proposes an efficient editing method. Furthermore, if the user is on vacation, the editing unit can also propose a relaxed editing method. Furthermore, if the user is participating in an event, the editing unit can also propose an editing method suitable for that event. In this way, the optimal editing method can be provided by considering the user's current activity status. Some or all of the above-mentioned processing in the editing unit may be performed, for example, using AI, or may be performed without using AI.

[0114] The video editing system includes an editing unit that proposes an optimal editing method taking into account the performance of a user's device. For example, if the user is using a high-performance device, the editing unit proposes an advanced editing method. In addition, if the user is using a low-performance device, the editing unit can also propose a lightweight editing method. Furthermore, the editing unit can also propose an optimal editing method taking into account the storage capacity of the user's device. In this way, the optimal editing method can be provided by taking into account the performance of the user's device. Some or all of the above-mentioned processing in the editing unit may be performed, for example, using AI, or may be performed without using AI.

[0115] The video editing system includes an editing unit that proposes an optimal editing method taking into account the user's internet connection status. For example, if the user has a high-speed internet connection, the editing unit proposes high-resolution video editing. Alternatively, if the user has a slow internet connection, the editing unit can also propose low-resolution video editing. Furthermore, the editing unit can propose an optimal editing method taking into account the stability of the user's internet connection. This makes it possible to provide an optimal editing method by taking into account the user's internet connection status. Some or all of the above-described processing in the editing unit may be performed, for example, using AI or without using AI.

[0116] The video editing system includes an editing unit that proposes an optimal editing method taking into account the language used by the user. For example, if the user speaks English, the editing unit proposes English subtitles and narration. Furthermore, if the user speaks Japanese, the editing unit can also propose Japanese subtitles and narration. Furthermore, if the user speaks multiple languages, the editing unit can also propose subtitles and narration in multiple languages. This makes it possible to provide an optimal editing method by taking into account the language used by the user. Some or all of the above-mentioned processing in the editing unit may be performed, for example, using AI, or may be performed without using AI.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit uploads unedited video data. Unedited video data may be in formats such as, but not limited to, MP4, AVI, and MOV. The reception unit allows the user to upload video data in a specific format. Step 2: The generation unit uses the generation AI to analyze the video data uploaded by the reception unit and automatically generate subtitles, narration, background music, and sound effects. The generation AI uses deep learning and natural language processing technology to understand the content of the video and generate appropriate subtitles and narration. The generation AI can also automatically add background music and sound effects that match the atmosphere of the video. Step 3: The editing department edits the elements generated by the generation department to fit the video. The editing department can add sound effects or insert narration to specific scenes in the video. Step 4: The adjustment unit sets a target playback time and edits the video to emphasize the highlights. For example, if a user wants to create a 5-minute video, the generation AI will extract the important scenes from the video and summarize them into a 5-minute video.

[0119] 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.

[0120] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0126] 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0142] 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0158] 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.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] 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.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0163] 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.

[0164] 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.

[0165] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0174] 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.

[0175] 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).

[0176] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0184] The hardware resource that executes the specific process 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 process may be a single processor.

[0185] 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.

[0186] 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.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk for uploading unedited video data; a generation unit that analyzes the video data uploaded by the reception unit and automatically generates subtitles, narration, background music, and sound effects; an editing unit that edits the elements generated by the generating unit to match the elements with the video; An adjustment unit that sets a target playback time and edits by emphasizing highlights. A system characterized by:

2. The generation unit Generative AI understands the content of videos and generates appropriate subtitles and narration 2. The system of claim 1.

3. The generation unit Generative AI automatically adds background music and sound effects that match the atmosphere of the video.

2. The system of claim 1.

4. The editorial department Add sound effects and narration to specific scenes in your video 2. The system of claim 1.

5. The adjustment unit Set a target duration and edit your video to highlight the highlights 2. The system of claim 1.

6. The reception unit Upload video data in a specific format 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the timing of uploading video data based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's upload history and select the optimal upload method 2. The system of claim 1.

9. The reception unit Filtering video data as it is uploaded based on the user's current projects and interests 2. The system of claim 1.

10. The reception unit When uploading video data, select the optimal upload method according to the user's input method.

2. The system of claim 1.

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