System

Abstract

To provide a system that automates video editing work.SOLUTION: A system according to an embodiment includes an analysis unit, a selection unit, a music unit, a color tone unit, and a subtitle unit. The analysis unit analyzes the content of a video. The selection unit selects appropriate cuts and effects on the basis of the content analyzed by the analysis unit. The music unit adds music and sound effects on the basis of the cuts and effects selected by the selection unit. The color tone unit performs color correction on the basis of the cuts and effects selected by the selection unit. The subtitle unit inserts subtitles on the basis of the cuts and effects selected by the selection unit.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques require manual video editing, which is time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to automate video editing work. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a selection unit, a music unit, a color tone unit, and a subtitle unit. The analysis unit analyzes the content of the video. The selection unit selects appropriate cuts and effects based on the content analyzed by the analysis unit. The music unit adds music and sound effects based on the cuts and effects selected by the selection unit. The color tone unit performs color correction based on the cuts and effects selected by the selection unit. The subtitle unit inserts subtitles based on the cuts and effects selected by the selection unit. Effect of the Invention

[0007] The system according to the embodiment can automate video editing operations. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. 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 is a system that analyzes the content of a video, selects optimal cuts and effects, and automatically edits the video. This system analyzes the content and composition of the video, and selects optimal cuts and effects. For example, a specific cut is selected to emphasize an important scene, and a transition effect is added to make the scene transition smoother. In addition, music and sound effects are added, color correction is performed, and subtitles are inserted automatically. This allows the user to create high-quality video without any effort. For example, the user inputs the video that he or she wants to edit into the AI. At this time, information such as the file format and resolution of the video is also input. For example, if a user wants to edit a video taken with a smartphone, the user uploads the video file to the AI. Next, the AI ​​analyzes the content and composition of the video. The AI ​​detects changes in objects and scenes in the video and identifies important and unnecessary scenes. For example, the AI ​​can analyze the movements and facial expressions of people in the video and detect changes in emotions. This allows the selection of cuts to emphasize important scenes. Furthermore, the AI ​​selects optimal cuts and effects and automatically edits the video. For example, a transition effect is added to make the scene transition smoother, and a filter is applied to a specific scene to change the atmosphere. The AI ​​also adds music and sound effects. For example, it automatically selects music that matches the atmosphere of the video and adds sound effects to match the scene. In addition, AI also automatically performs color correction and insertion of subtitles. For example, it adjusts the color tone of the video to balance the overall image, or inserts subtitles into specific scenes to provide information to the viewer. This allows users to create high-quality video without any effort. This mechanism greatly improves the efficiency of video editing work, allowing users to create professional videos in a short time. For example, even people without video editing skills, such as content creators and corporate marketing personnel, can easily create high-quality video. This allows the video editing system to automatically analyze the content of the video and select the optimal cuts and effects for editing.

[0029] The video editing system according to the embodiment includes an analysis unit, a selection unit, a music unit, a color tone unit, and a subtitle unit. The analysis unit analyzes the content of the video. The content of the video includes, for example, scenes, objects, actions, and the like, but is not limited to such examples. The analysis unit, for example, detects changes in objects and scenes in the video and identifies important scenes and unnecessary scenes. For example, the analysis unit can analyze the movements and expressions of people in the video and detect changes in emotions. The selection unit selects optimal cuts and effects based on the content analyzed by the analysis unit. For example, the selection unit adds transition effects to smooth scene changes, or applies filters to specific scenes to change the atmosphere. The music unit adds music and sound effects based on the cuts and effects selected by the selection unit. For example, the music unit automatically selects music that matches the atmosphere of the video and adds sound effects to match the scene. The color tone unit performs color tone correction based on the cuts and effects selected by the selection unit. For example, the color tone unit adjusts the color tone of the video to balance the overall image. The subtitle unit inserts subtitles based on the cuts and effects selected by the selection unit. The subtitle unit provides information to the viewer by, for example, inserting subtitles into specific scenes, so that the video editing system according to the embodiment can automatically analyze the content of the video and select optimal cuts and effects for editing.

[0030] The analysis unit analyzes the contents of the video in detail. The contents of the video include, but are not limited to, scenes, objects, actions, etc. Specifically, the analysis unit detects changes in objects and scenes in the video and identifies important and unnecessary scenes. For example, the analysis unit can analyze the movements and facial expressions of people in the video to detect changes in emotions. For this, face recognition technology and motion analysis algorithms are used to capture subtle changes in people's facial expressions and movements. In addition, the analysis unit also detects changes in the background and environment in the video to grasp the atmosphere and situation of the scene. For example, it can detect changes in weather and changes in time of day and classify scenes accordingly. In addition, the analysis unit can also perform audio analysis and extract important keywords and emotions from audio data in the video. This allows the content of the video to be analyzed from multiple angles and scenes to be identified with greater accuracy. In addition, the analysis unit can utilize past video data and statistical information to detect specific patterns and trends and use them as a reference for future video editing. For example, popular scenes and effects in a specific genre or theme can be analyzed and reflected in future editing. This enables the analysis unit to comprehensively analyze the content of the video and provide basic data for optimal editing.

[0031] The selection unit selects the optimal cuts and effects based on the content analyzed by the analysis unit. Specifically, the selection unit adds transition effects to make scene changes smoother, or applies filters to specific scenes to change the atmosphere. For example, transition effects such as fade-in, fade-out, and cross dissolve are automatically selected when switching scenes to make the flow of the video natural. In addition, color filters and effects can be applied to specific scenes to enhance the visual impact. The selection unit uses AI to analyze the content of the video and the viewer's preferences and selects the optimal cuts and effects. For example, based on the viewer's past viewing history and evaluation data, it is possible to analyze the tendency to prefer specific scenes and effects, and optimize the editing content based on that. In addition, the selection unit adjusts the length of the scene and the timing of the cuts, taking into account the tempo and rhythm of the video. This makes it possible to provide attractive video to viewers while maintaining the balance of the entire video. The selection unit can also collect user feedback and continuously improve the accuracy and effectiveness of the editing content. For example, by analyzing the viewer's reactions and evaluations of the edited video and reflecting them in the next edit, higher quality video editing can be achieved. This allows the selection section to select optimal cuts and effects according to the content of the video, and provide attractive video to the viewer.

[0032] The music department adds music and sound effects based on the cuts and effects selected by the selection department. Specifically, the music department automatically selects music that matches the atmosphere of the video and adds sound effects to match the scene. For example, orchestral music is selected to enhance emotions for moving scenes, and music with a fast rhythm is selected to increase tension for action scenes. In addition, appropriate sound effects are added according to the content of the scene. For example, the sound of a door opening and closing, footsteps, and natural sounds are inserted according to the scene to enhance the reality of the video. The music department uses AI to analyze the content of the video and the viewer's preferences and selects the optimal music and sound effects. For example, it can analyze the tendency of viewers to prefer certain music and sound effects based on their past viewing history and evaluation data, and select music and sound effects based on that. In addition, the music department adjusts the timing of the music to match the tempo and rhythm of the video. This makes the video and music integrated, providing a more attractive video experience for the viewer. The music department can also collect user feedback and continuously improve the accuracy and effectiveness of the selection of music and sound effects. For example, by analyzing the viewer's reactions and evaluations of the edited video and reflecting them in the next edit, the music department can select music and sound effects of higher quality. This allows the music department to select the most suitable music and sound effects according to the content of the video and provide viewers with attractive video.

[0033] The color tone section performs color correction based on the cuts and effects selected by the selection section. Specifically, the color tone section adjusts the color tone of the image to balance the overall image. For example, the brightness, contrast, and saturation of the image are adjusted to create a visually appealing image. In addition, a color tone filter can be applied to a specific scene to emphasize the atmosphere of the scene. For example, a warm color filter can be applied to a sunset scene to create an emotional atmosphere. The color tone section uses AI to analyze the content of the image and the viewer's preferences and perform optimal color correction. For example, based on the viewer's past viewing history and evaluation data, it is possible to analyze the tendency to prefer specific color tones and perform color correction based on that. In addition, the color tone section adjusts the change in color tone to match the tempo and rhythm of the image. This makes it possible to provide an image that is attractive to the viewer while maintaining the balance of the entire image. The color tone section can also collect user feedback and continuously improve the accuracy and effectiveness of color correction. For example, by analyzing the viewer's reaction and evaluation of the edited image and reflecting it in the next edit, higher quality color correction can be achieved. This allows the color correction unit to perform optimal color correction according to the content of the video, and provide an attractive video to the viewer.

[0034] The subtitle unit inserts subtitles based on the cuts and effects selected by the selection unit. Specifically, the subtitle unit inserts subtitles into specific scenes to provide information to the viewer. For example, the subtitle unit displays the lines of the characters in dialogue scenes to convey important information to the viewer. In addition, explanatory text and annotations can be added according to the content of the video. The subtitle unit uses AI to analyze the content of the video and the viewer's preferences and selects the optimal subtitles. For example, it can analyze the tendency of viewers to prefer specific subtitle styles and fonts based on their past viewing history and evaluation data, and select subtitles based on that. In addition, the subtitle unit adjusts the display timing of the subtitles to match the tempo and rhythm of the video. This makes it possible to provide a video experience that is easier for viewers to understand by integrating the video and the subtitles. The subtitle unit can also collect user feedback and continuously improve the accuracy and effectiveness of the subtitle selection. For example, it can analyze the viewer's reactions and evaluations of the edited video and reflect them in the next edit, thereby achieving the selection of higher quality subtitles. As a result, the subtitle unit can select the optimal subtitles according to the content of the video and provide attractive videos for the viewers.

[0035] The analysis unit can detect changes in objects or scenes in the video and identify important or unnecessary scenes. The analysis unit detects changes in objects or scenes in the video, for example. Examples include motion detection, color changes, and the like. The analysis unit also identifies important and unnecessary scenes. For example, the viewer's interest level, the length of the scene, and the like are used as criteria. This enables efficient editing by automatically identifying important and unnecessary scenes in the video. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model to detect changes in objects or scenes in the video.

[0036] The selection unit can select a cut for emphasizing an important scene identified by the analysis unit. The selection unit selects a cut for emphasizing an important scene identified by the analysis unit, for example. For example, the importance of the scene, visual effect, etc. are used as criteria. This makes it possible to automatically select a cut for emphasizing an important scene, thereby enhancing the appeal of the video. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can perform analysis using an AI model to select a cut for emphasizing an important scene.

[0037] The selection unit can add a transition effect to smooth the scene transition. The selection unit adds a transition effect to smooth the scene transition. For example, fade-in, fade-out, cross dissolve, etc. are included. This makes the scene transition smoother, making the video flow more natural. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select a transition effect using an AI model to smooth the scene transition.

[0038] The music section can automatically select music suitable for the atmosphere of the video and add sound effects suitable for the scene. The music section can automatically select music suitable for the atmosphere of the video and add sound effects suitable for the scene. For example, the genre, tempo, atmosphere, etc. are used as criteria. This makes it possible to enhance the appeal of the video by automatically adding music and sound effects that match the atmosphere of the video. Some or all of the above-mentioned processing in the music section can be performed, for example, using AI or can be performed without using AI. For example, the music section can perform analysis using an AI model to select music suitable for the atmosphere of the video.

[0039] The color tone unit can adjust the color tone of the image to adjust the overall balance. The color tone unit, for example, adjusts the color tone of the image to adjust the overall balance. For example, brightness, contrast, saturation, etc. are used as criteria. By adjusting the color tone of the image, the overall balance can be adjusted and the visual appeal can be enhanced. Some or all of the above-mentioned processing in the color tone unit may be performed, for example, using AI or may be performed without using AI. For example, the color tone unit can perform analysis using an AI model to adjust the color tone of the image.

[0040] The subtitle unit can insert subtitles into specific scenes to convey information to the viewer. The subtitle unit, for example, inserts subtitles into specific scenes to provide information to the viewer. For example, the font, size, display position, etc. are used as criteria. In this way, by inserting subtitles into specific scenes, information can be provided to the viewer and understanding can be aided. Some or all of the above-mentioned processing in the subtitle unit may be performed, for example, using AI, or may be performed without using AI. For example, the subtitle unit can perform analysis using an AI model to insert subtitles into specific scenes.

[0041] The analysis unit can analyze the audio data in the video and evaluate the importance of the scene based on the content of the audio. The analysis unit can, for example, analyze the audio data in the video and evaluate the importance of the scene based on the content of the audio. For example, AI detects emotional remarks from the audio data and evaluates the scene as an important scene. AI can also analyze the frequency of conversation from the audio data and evaluate a scene with a lot of conversation as an important scene. Furthermore, AI can detect a specific keyword from the audio data and evaluate a scene containing the keyword as an important scene. Thereby, the importance of the scene can be evaluated more accurately by analyzing the audio data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model to analyze the audio data.

[0042] The analysis unit can analyze the shooting date and time of the video and the location information, and determine the importance of the scene based on a specific event or situation. The analysis unit can, for example, analyze the shooting date and time of the video and the location information, and determine the importance of the scene based on a specific event or situation. For example, AI can analyze the shooting date and time of the video and determine a scene of a specific event (e.g., a fireworks display) as an important scene. AI can also analyze the location information of the video and determine a scene of a tourist spot as an important scene. Furthermore, AI can analyze the shooting date and time of the video in combination with the location information, and determine a scene related to a specific season or event as an important scene. In this way, the importance of a scene can be determined based on a specific event or situation by analyzing the shooting date and time and the location information. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model to analyze the shooting date and time and the location information.

[0043] The analysis unit can analyze text data in the video and evaluate the importance of a scene based on the content of the text. The analysis unit can, for example, analyze text data in the video and evaluate the importance of a scene based on the content of the text. For example, AI can detect important information from text data in the video and evaluate the scene as an important scene. AI can also detect emotional expressions from text data in the video and evaluate the scene as an important scene. Furthermore, AI can detect specific keywords from text data in the video and evaluate scenes containing the keywords as important scenes. This allows the importance of a scene to be evaluated more accurately by analyzing the text data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model to analyze the text data.

[0044] The analysis unit can analyze the viewing history of the video and determine the importance of a scene based on the past viewing tendency. The analysis unit can, for example, analyze the viewing history of the video and determine the importance of a scene based on the past viewing tendency. For example, an AI can analyze the user's past viewing history and determine a scene that has been frequently viewed as an important scene. The AI ​​can also analyze a specific genre from the user's past viewing history and determine a scene related to the genre as an important scene. Furthermore, the AI ​​can determine a scene containing a specific character or location as an important scene based on the user's past viewing history. Thereby, by analyzing the viewing history, the importance of a scene can be determined based on the past viewing tendency. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can perform analysis using an AI model to analyze the viewing history.

[0045] The selection unit can apply different cut selection algorithms depending on the genre of the video. The selection unit applies different cut selection algorithms depending on the genre of the video, for example. For example, in the case of a documentary video, the AI ​​preferentially selects interview scenes as cuts. In addition, in the case of an action movie, the AI ​​can also preferentially select action scenes as cuts. Furthermore, in the case of a comedy video, the AI ​​can also preferentially select humorous scenes as cuts. In this way, by applying a cut selection algorithm depending on the genre of the video, a more appropriate cut can be selected. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can perform analysis using an AI model to apply a cut selection algorithm depending on the genre of the video.

[0046] The selection unit can adjust the frequency of cut selection based on the length of the video. The selection unit adjusts the frequency of cut selection based on, for example, the length of the video. For example, in the case of a long video, the AI ​​sets the frequency of cuts low and emphasizes the continuity of the scene. In addition, in the case of a short video, the AI ​​can set the frequency of cuts high and perform editing with a good tempo. Furthermore, in the case of a medium-length video, the AI ​​can set the frequency of cuts to a medium level and perform balanced editing. By adjusting the frequency of cut selection based on the length of the video, it is possible to edit while maintaining the continuity of the scene. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can perform analysis using an AI model to adjust the frequency of cut selection based on the length of the video.

[0047] The selection unit can apply different transition effects depending on the theme of the video. The selection unit applies different transition effects depending on the theme of the video, for example. For example, in the case of documentary video, the AI ​​can apply a simple transition effect. In addition, in the case of action movies, the AI ​​can also apply a dynamic transition effect. Furthermore, in the case of comedy video, the AI ​​can also apply a humorous transition effect. This allows for more appropriate editing by applying transition effects depending on the theme of the video. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can perform analysis using an AI model to apply a transition effect depending on the theme of the video.

[0048] The selection unit can adjust the cut selection criteria based on the audience of the video. The selection unit adjusts the cut selection criteria based on the audience of the video, for example. For example, in the case of a video for young people, the AI ​​selects cuts with a good tempo. In addition, in the case of a video for elderly people, the AI ​​can also select cuts with a relaxed pace. Furthermore, in the case of a video for families, the AI ​​can also select cuts that are well-balanced. This allows for more appropriate editing by adjusting the cut selection criteria based on the audience. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can perform analysis using an AI model to adjust the cut selection criteria based on the audience.

[0049] The music section can apply different music selection algorithms depending on the genre of the video. The music section, for example, applies different music selection algorithms depending on the genre of the video. For example, in the case of a documentary video, the AI ​​can select music that enhances the narration. In addition, in the case of an action movie, the AI ​​can select music that increases the tension. Furthermore, in the case of a comedy video, the AI ​​can select humorous music. In this way, by applying the music selection algorithm depending on the genre of the video, more appropriate music can be selected. Some or all of the above-mentioned processing in the music section may be performed, for example, using AI, or may be performed without using AI. For example, the music section can perform analysis using an AI model to apply the music selection algorithm depending on the genre of the video.

[0050] The music section can adjust the frequency of music selection based on the length of the video. The music section adjusts the frequency of music selection based on, for example, the length of the video. For example, in the case of a long video, the AI ​​sets the frequency of music selection low and emphasizes the continuity of the scenes. In addition, in the case of a short video, the AI ​​can set the frequency of music selection high and perform editing with a good tempo. Furthermore, in the case of a medium-length video, the AI ​​can set the frequency of music selection to a medium level and perform balanced editing. As a result, by adjusting the frequency of music selection based on the length of the video, editing can be performed while maintaining the continuity of the scenes. Part or all of the above-mentioned processing in the music section may be performed, for example, using AI, or may be performed without using AI. For example, the music section can perform analysis using an AI model to adjust the frequency of music selection based on the length of the video.

[0051] The music section can apply different sound effects depending on the theme of the video. The music section, for example, applies different sound effects depending on the theme of the video. For example, in the case of a documentary video, the AI ​​applies sound effects that enhance the narration. Also, in the case of an action movie, the AI ​​can apply sound effects that increase tension. Furthermore, in the case of a comedy video, the AI ​​can apply humorous sound effects. This allows for more appropriate editing by applying sound effects depending on the theme of the video. Some or all of the above-mentioned processing in the music section may be performed, for example, using AI, or may be performed without using AI. For example, the music section can perform analysis using an AI model to apply sound effects depending on the theme of the video.

[0052] The music section can adjust the music selection criteria based on the audience of the video. The music section, for example, adjusts the music selection criteria based on the audience of the video. For example, for a video aimed at young people, the AI ​​can select up-tempo music. Also, for a video aimed at elderly people, the AI ​​can select calm music. Furthermore, for a video aimed at families, the AI ​​can select balanced music. This allows for more appropriate editing by adjusting the music selection criteria based on the audience. Some or all of the above-mentioned processing in the music section may be performed, for example, using AI or may be performed without using AI. For example, the music section can perform analysis using an AI model to adjust the music selection criteria based on the audience.

[0053] The color tone unit can apply different color correction algorithms depending on the genre of the video. The color tone unit, for example, applies different color correction algorithms depending on the genre of the video. For example, in the case of a documentary video, the AI ​​applies natural color correction. In addition, in the case of an action movie, the AI ​​can also apply color correction with high contrast. Furthermore, in the case of a comedy video, the AI ​​can also apply bright color correction. This allows for more appropriate color correction by applying a color correction algorithm depending on the genre of the video. Some or all of the above-mentioned processing in the color tone unit may be performed, for example, using AI, or may be performed without using AI. For example, the color tone unit can perform analysis using an AI model to apply a color correction algorithm depending on the genre of the video.

[0054] The color tone unit can adjust the standard of color tone correction based on the shooting environment of the video. The color tone unit adjusts the standard of color tone correction based on the shooting environment of the video, for example. For example, in the case of a video shot outdoors, the AI ​​applies color tone correction according to natural light. In addition, in the case of a video shot indoors, the AI ​​can also apply color tone correction according to artificial light. Furthermore, in the case of a video shot at night, the AI ​​can also apply color tone correction according to low illuminance. This allows more appropriate color tone correction by adjusting the standard of color tone correction based on the shooting environment. Part or all of the above-mentioned processing in the color tone unit may be performed, for example, using AI, or may be performed without using AI. For example, the color tone unit can perform analysis using an AI model to adjust the standard of color tone correction based on the shooting environment.

[0055] The color tone unit can apply different color corrections depending on the theme of the video. The color tone unit, for example, applies different color corrections depending on the theme of the video. For example, in the case of documentary video, the AI ​​applies natural color correction. In addition, in the case of action movies, the AI ​​can also apply high-contrast color correction. Furthermore, in the case of comedy video, the AI ​​can also apply bright color correction. This allows for more appropriate editing by applying color corrections depending on the theme of the video. Some or all of the above-mentioned processing in the color tone unit may be performed, for example, using AI or may be performed without using AI. For example, the color tone unit can perform analysis using an AI model to apply color corrections depending on the theme of the video.

[0056] The color tone unit can adjust the standard of color tone correction based on the audience of the video. The color tone unit adjusts the standard of color tone correction based on the audience of the video, for example. For example, in the case of a video for young people, the AI ​​applies vivid color tone correction. In addition, in the case of a video for elderly people, the AI ​​can also apply subdued color tone correction. Furthermore, in the case of a video for families, the AI ​​can also apply balanced color tone correction. This allows for more appropriate editing by adjusting the standard of color tone correction based on the audience. Some or all of the above-mentioned processing in the color tone unit may be performed, for example, using AI, or may be performed without using AI. For example, the color tone unit can perform analysis using an AI model to adjust the standard of color tone correction based on the audience.

[0057] The subtitle unit can apply different subtitle display algorithms depending on the genre of the video. The subtitle unit applies different subtitle display algorithms depending on the genre of the video, for example. For example, in the case of a documentary video, the AI ​​displays simple and easy-to-read subtitles. In addition, in the case of an action movie, the AI ​​can also display dynamic subtitles. Furthermore, in the case of a comedy video, the AI ​​can also display humorous subtitles. This allows for more appropriate subtitle display by applying a subtitle display algorithm depending on the genre of the video. Some or all of the above-mentioned processing in the subtitle unit may be performed using, for example, AI, or may be performed without using AI. For example, the subtitle unit can perform analysis using an AI model to apply a subtitle display algorithm depending on the genre of the video.

[0058] The subtitle unit can adjust the display method of the subtitles based on the language of the video. The subtitle unit adjusts the display method of the subtitles based on, for example, the language of the video. For example, in the case of an English video, the AI ​​displays English subtitles. Also, in the case of a Japanese video, the AI ​​can display Japanese subtitles. Furthermore, in the case of a multilingual video, the AI ​​can display subtitles in multiple languages. This allows for more appropriate subtitle display by adjusting the display method of the subtitles based on the language of the video. Part or all of the above-mentioned processing in the subtitle unit may be performed, for example, using AI, or may be performed without using AI. For example, the subtitle unit can perform analysis using an AI model to adjust the display method of the subtitles based on the language of the video.

[0059] The subtitle unit can apply different subtitle display depending on the theme of the video. The subtitle unit applies different subtitle display depending on the theme of the video, for example. For example, in the case of a documentary video, the AI ​​can display simple and easy-to-read subtitles. In addition, in the case of an action movie, the AI ​​can also display dynamic subtitles. Furthermore, in the case of a comedy video, the AI ​​can display humorous subtitles. This allows for more appropriate editing by applying subtitle display depending on the theme of the video. Some or all of the above-mentioned processing in the subtitle unit may be performed, for example, using AI, or may be performed without using AI. For example, the subtitle unit can perform analysis using an AI model to apply subtitle display depending on the theme of the video.

[0060] The subtitle unit can adjust the display method of the subtitles based on the viewer demographic of the video. The subtitle unit, for example, adjusts the display method of the subtitles based on the viewer demographic of the video. For example, in the case of a video for young people, the AI ​​can display colorful and eye-catching subtitles. In addition, in the case of a video for elderly people, the AI ​​can also display large and easy-to-read subtitles. Furthermore, in the case of a video for families, the AI ​​can also display balanced subtitles. This allows for more appropriate editing by adjusting the display method of the subtitles based on the viewer demographic. Some or all of the above-mentioned processing in the subtitle unit may be performed, for example, using AI, or may be performed without using AI. For example, the subtitle unit can perform analysis using an AI model to adjust the display method of the subtitles based on the viewer demographic.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The video editing system may further include a history analysis unit that analyzes the user's viewing history. The history analysis unit may analyze the user's preferences for genres and scenes of videos that the user has viewed in the past, and may select cuts and effects for video editing based on the results. For example, if the user likes action movies, cuts that emphasize action scenes may be selected. Also, if the user likes documentaries, cuts that emphasize interview scenes may be selected. Furthermore, if the user likes comedies, cuts that emphasize humorous scenes may be selected. In this way, video editing is performed based on the user's viewing history, and more personalized videos are provided.

[0063] The video editing system may further include an audio analysis unit that analyzes the audio data of the video. The audio analysis unit may analyze the audio data in the video and evaluate the importance of a scene based on the content of the audio. For example, emotional remarks may be detected from the audio data and the scene may be evaluated as an important scene. The frequency of conversation may also be analyzed from the audio data and a scene with a lot of conversation may be evaluated as an important scene. Furthermore, a specific keyword may be detected from the audio data and a scene containing the keyword may be evaluated as an important scene. In this way, the importance of a scene may be evaluated more accurately by analyzing the audio data.

[0064] The video editing system may further include a location information analysis unit that analyzes the shooting date and time and location information of the video. The location information analysis unit can analyze the shooting date and time and location information of the video and determine the importance of a scene based on a specific event or situation. For example, the shooting date and time may be analyzed to determine a scene of a specific event (e.g., a fireworks display) as an important scene. The location information may also be analyzed to determine a scene of a tourist spot as an important scene. Furthermore, the shooting date and time and location information may be analyzed in combination to determine a scene related to a specific season or event as an important scene. In this way, the importance of a scene can be determined based on a specific event or situation by analyzing the shooting date and time and location information.

[0065] The video editing system may further include a text analysis unit that analyzes text data in the video. The text analysis unit may analyze the text data in the video and evaluate the importance of a scene based on the content of the text. For example, important information may be detected from the text data and the scene may be evaluated as an important scene. Emotional expressions may also be detected from the text data and the scene may be evaluated as an important scene. Furthermore, a specific keyword may be detected from the text data and a scene containing the keyword may be evaluated as an important scene. This allows the importance of a scene to be evaluated more accurately by analyzing the text data.

[0066] The video editing system can further include an audience analysis unit that adjusts cut selection criteria based on the audience of the video. The audience analysis unit can adjust the cut selection criteria based on the audience of the video. For example, for video aimed at young people, cuts with a good tempo can be selected. Also, for video aimed at elderly people, relaxed cuts can be selected. Furthermore, for video aimed at families, well-balanced cuts can be selected. In this way, more appropriate editing can be achieved by adjusting the cut selection criteria based on the audience.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The analysis unit analyzes the content of the video. The content of the video includes scenes, objects, actions, etc. The analysis unit detects changes in objects and scenes in the video and identifies important and unnecessary scenes. For example, it can analyze people's movements and facial expressions to detect changes in emotions. Step 2: The Selector selects the best cuts and effects based on the content analyzed by the Analyser. The Selector adds transition effects to smooth scene changes, or applies filters to specific scenes to change the atmosphere. Step 3: The music department adds music and sound effects based on the cuts and effects selected by the selection department. The music department automatically selects music that matches the atmosphere of the video and adds sound effects to match the scenes. Step 4: The color tone section performs color correction based on the cuts and effects selected by the selection section. The color tone section adjusts the color tone of the image to achieve an overall balance. Step 5: The subtitling unit inserts subtitles based on the scenes and effects selected by the selection unit. The subtitling unit inserts subtitles into specific scenes to provide information to the viewers.

[0069] (Example 2) A video editing system according to an embodiment of the present invention is a system that analyzes the content of a video, selects optimal cuts and effects, and automatically edits the video. This system analyzes the content and composition of the video, and selects optimal cuts and effects. For example, a specific cut is selected to emphasize an important scene, and a transition effect is added to make the scene transition smoother. In addition, music and sound effects are added, color correction is performed, and subtitles are inserted automatically. This allows the user to create high-quality video without any effort. For example, the user inputs the video that he or she wants to edit into the AI. At this time, information such as the file format and resolution of the video is also input. For example, if a user wants to edit a video taken with a smartphone, the user uploads the video file to the AI. Next, the AI ​​analyzes the content and composition of the video. The AI ​​detects changes in objects and scenes in the video and identifies important and unnecessary scenes. For example, the AI ​​can analyze the movements and facial expressions of people in the video and detect changes in emotions. This allows the selection of cuts to emphasize important scenes. Furthermore, the AI ​​selects optimal cuts and effects and automatically edits the video. For example, a transition effect is added to make the scene transition smoother, and a filter is applied to a specific scene to change the atmosphere. The AI ​​also adds music and sound effects. For example, it automatically selects music that matches the atmosphere of the video and adds sound effects to match the scene. In addition, AI also automatically performs color correction and insertion of subtitles. For example, it adjusts the color tone of the video to balance the overall image, or inserts subtitles into specific scenes to provide information to the viewer. This allows users to create high-quality video without any effort. This mechanism greatly improves the efficiency of video editing work, allowing users to create professional videos in a short time. For example, even people without video editing skills, such as content creators and corporate marketing personnel, can easily create high-quality video. This allows the video editing system to automatically analyze the content of the video and select the optimal cuts and effects for editing.

[0070] The video editing system according to the embodiment includes an analysis unit, a selection unit, a music unit, a color tone unit, and a subtitle unit. The analysis unit analyzes the content of the video. The content of the video includes, for example, scenes, objects, actions, and the like, but is not limited to such examples. The analysis unit, for example, detects changes in objects and scenes in the video and identifies important scenes and unnecessary scenes. For example, the analysis unit can analyze the movements and expressions of people in the video and detect changes in emotions. The selection unit selects optimal cuts and effects based on the content analyzed by the analysis unit. For example, the selection unit adds transition effects to smooth scene changes, or applies filters to specific scenes to change the atmosphere. The music unit adds music and sound effects based on the cuts and effects selected by the selection unit. For example, the music unit automatically selects music that matches the atmosphere of the video and adds sound effects to match the scene. The color tone unit performs color tone correction based on the cuts and effects selected by the selection unit. For example, the color tone unit adjusts the color tone of the video to balance the overall image. The subtitle unit inserts subtitles based on the cuts and effects selected by the selection unit. The subtitle unit provides information to the viewer by, for example, inserting subtitles into specific scenes, so that the video editing system according to the embodiment can automatically analyze the content of the video and select optimal cuts and effects for editing.

[0071] The analysis unit analyzes the contents of the video in detail. The contents of the video include, but are not limited to, scenes, objects, actions, etc. Specifically, the analysis unit detects changes in objects and scenes in the video and identifies important and unnecessary scenes. For example, the analysis unit can analyze the movements and facial expressions of people in the video to detect changes in emotions. For this, face recognition technology and motion analysis algorithms are used to capture subtle changes in people's facial expressions and movements. In addition, the analysis unit also detects changes in the background and environment in the video to grasp the atmosphere and situation of the scene. For example, it can detect changes in weather and changes in time of day and classify scenes accordingly. In addition, the analysis unit can also perform audio analysis and extract important keywords and emotions from audio data in the video. This allows the content of the video to be analyzed from multiple angles and scenes to be identified with greater accuracy. In addition, the analysis unit can utilize past video data and statistical information to detect specific patterns and trends and use them as a reference for future video editing. For example, popular scenes and effects in a specific genre or theme can be analyzed and reflected in future editing. This enables the analysis unit to comprehensively analyze the content of the video and provide basic data for optimal editing.

[0072] The selection unit selects the optimal cuts and effects based on the content analyzed by the analysis unit. Specifically, the selection unit adds transition effects to make scene changes smoother, or applies filters to specific scenes to change the atmosphere. For example, transition effects such as fade-in, fade-out, and cross dissolve are automatically selected when switching scenes to make the flow of the video natural. In addition, color filters and effects can be applied to specific scenes to enhance the visual impact. The selection unit uses AI to analyze the content of the video and the viewer's preferences and selects the optimal cuts and effects. For example, based on the viewer's past viewing history and evaluation data, it is possible to analyze the tendency to prefer specific scenes and effects, and optimize the editing content based on that. In addition, the selection unit adjusts the length of the scene and the timing of the cuts, taking into account the tempo and rhythm of the video. This makes it possible to provide attractive video to viewers while maintaining the balance of the entire video. The selection unit can also collect user feedback and continuously improve the accuracy and effectiveness of the editing content. For example, by analyzing the viewer's reactions and evaluations of the edited video and reflecting them in the next edit, higher quality video editing can be achieved. This allows the selection section to select optimal cuts and effects according to the content of the video, and provide attractive video to the viewer.

[0073] The music department adds music and sound effects based on the cuts and effects selected by the selection department. Specifically, the music department automatically selects music that matches the atmosphere of the video and adds sound effects to match the scene. For example, orchestral music is selected to enhance emotions for moving scenes, and music with a fast rhythm is selected to increase tension for action scenes. In addition, appropriate sound effects are added according to the content of the scene. For example, the sound of a door opening and closing, footsteps, and natural sounds are inserted according to the scene to enhance the reality of the video. The music department uses AI to analyze the content of the video and the viewer's preferences and selects the optimal music and sound effects. For example, it can analyze the tendency of viewers to prefer certain music and sound effects based on their past viewing history and evaluation data, and select music and sound effects based on that. In addition, the music department adjusts the timing of the music to match the tempo and rhythm of the video. This makes the video and music integrated, providing a more attractive video experience for the viewer. The music department can also collect user feedback and continuously improve the accuracy and effectiveness of the selection of music and sound effects. For example, by analyzing the viewer's reactions and evaluations of the edited video and reflecting them in the next edit, the music department can select music and sound effects of higher quality. This allows the music department to select the most suitable music and sound effects according to the content of the video and provide viewers with attractive video.

[0074] The color tone section performs color correction based on the cuts and effects selected by the selection section. Specifically, the color tone section adjusts the color tone of the image to balance the overall image. For example, the brightness, contrast, and saturation of the image are adjusted to create a visually appealing image. In addition, a color tone filter can be applied to a specific scene to emphasize the atmosphere of the scene. For example, a warm color filter can be applied to a sunset scene to create an emotional atmosphere. The color tone section uses AI to analyze the content of the image and the viewer's preferences and perform optimal color correction. For example, based on the viewer's past viewing history and evaluation data, it is possible to analyze the tendency to prefer specific color tones and perform color correction based on that. In addition, the color tone section adjusts the change in color tone to match the tempo and rhythm of the image. This makes it possible to provide an image that is attractive to the viewer while maintaining the balance of the entire image. The color tone section can also collect user feedback and continuously improve the accuracy and effectiveness of color correction. For example, by analyzing the viewer's reaction and evaluation of the edited image and reflecting it in the next edit, higher quality color correction can be achieved. This allows the color correction unit to perform optimal color correction according to the content of the video, and provide an attractive video to the viewer.

[0075] The subtitle unit inserts subtitles based on the cuts and effects selected by the selection unit. Specifically, the subtitle unit inserts subtitles into specific scenes to provide information to the viewer. For example, the subtitle unit displays the lines of the characters in dialogue scenes to convey important information to the viewer. In addition, explanatory text and annotations can be added according to the content of the video. The subtitle unit uses AI to analyze the content of the video and the viewer's preferences and selects the optimal subtitles. For example, it can analyze the tendency of viewers to prefer specific subtitle styles and fonts based on their past viewing history and evaluation data, and select subtitles based on that. In addition, the subtitle unit adjusts the display timing of the subtitles to match the tempo and rhythm of the video. This makes it possible to provide a video experience that is easier for viewers to understand by integrating the video and the subtitles. The subtitle unit can also collect user feedback and continuously improve the accuracy and effectiveness of the subtitle selection. For example, it can analyze the viewer's reactions and evaluations of the edited video and reflect them in the next edit, thereby achieving the selection of higher quality subtitles. As a result, the subtitle unit can select the optimal subtitles according to the content of the video and provide attractive videos for the viewers.

[0076] The analysis unit can detect changes in objects or scenes in the video and identify important or unnecessary scenes. The analysis unit detects changes in objects or scenes in the video, for example. Examples include motion detection, color changes, and the like. The analysis unit also identifies important and unnecessary scenes. For example, the viewer's interest level, the length of the scene, and the like are used as criteria. This enables efficient editing by automatically identifying important and unnecessary scenes in the video. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model to detect changes in objects or scenes in the video.

[0077] The selection unit can select a cut for emphasizing an important scene identified by the analysis unit. The selection unit selects a cut for emphasizing an important scene identified by the analysis unit, for example. For example, the importance of the scene, visual effect, etc. are used as criteria. This makes it possible to automatically select a cut for emphasizing an important scene, thereby enhancing the appeal of the video. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can perform analysis using an AI model to select a cut for emphasizing an important scene.

[0078] The selection unit can add a transition effect to smooth the scene transition. The selection unit adds a transition effect to smooth the scene transition. For example, fade-in, fade-out, cross dissolve, etc. are included. This makes the scene transition smoother, making the video flow more natural. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select a transition effect using an AI model to smooth the scene transition.

[0079] The music section can automatically select music suitable for the atmosphere of the video and add sound effects suitable for the scene. The music section can automatically select music suitable for the atmosphere of the video and add sound effects suitable for the scene. For example, the genre, tempo, atmosphere, etc. are used as criteria. This makes it possible to enhance the appeal of the video by automatically adding music and sound effects that match the atmosphere of the video. Some or all of the above-mentioned processing in the music section can be performed, for example, using AI or can be performed without using AI. For example, the music section can perform analysis using an AI model to select music suitable for the atmosphere of the video.

[0080] The color tone unit can adjust the color tone of the image to adjust the overall balance. The color tone unit, for example, adjusts the color tone of the image to adjust the overall balance. For example, brightness, contrast, saturation, etc. are used as criteria. By adjusting the color tone of the image, the overall balance can be adjusted and the visual appeal can be enhanced. Some or all of the above-mentioned processing in the color tone unit may be performed, for example, using AI or may be performed without using AI. For example, the color tone unit can perform analysis using an AI model to adjust the color tone of the image.

[0081] The subtitle unit can insert subtitles into specific scenes to convey information to the viewer. The subtitle unit, for example, inserts subtitles into specific scenes to provide information to the viewer. For example, the font, size, display position, etc. are used as criteria. In this way, by inserting subtitles into specific scenes, information can be provided to the viewer and understanding can be aided. Some or all of the above-mentioned processing in the subtitle unit may be performed, for example, using AI, or may be performed without using AI. For example, the subtitle unit can perform analysis using an AI model to insert subtitles into specific scenes.

[0082] The analysis unit can estimate the user's emotion and identify important scenes in the video based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and identifies important scenes in the video based on the estimated user's emotion. For example, if the user is excited, the AI ​​identifies an action scene in the video as an important scene. Also, if the user is sad, the AI ​​can identify a moving scene in the video as an important scene. Furthermore, if the user is relaxed, the AI ​​can identify a landscape scene in the video as an important scene. This enables more personalized video editing by identifying important scenes based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can perform analysis using an AI model to estimate the user's emotion.

[0083] The analysis unit can analyze the audio data in the video and evaluate the importance of the scene based on the content of the audio. The analysis unit can, for example, analyze the audio data in the video and evaluate the importance of the scene based on the content of the audio. For example, AI detects emotional remarks from the audio data and evaluates the scene as an important scene. AI can also analyze the frequency of conversation from the audio data and evaluate a scene with a lot of conversation as an important scene. Furthermore, AI can detect a specific keyword from the audio data and evaluate a scene containing the keyword as an important scene. Thereby, the importance of the scene can be evaluated more accurately by analyzing the audio data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model to analyze the audio data.

[0084] The analysis unit can analyze the shooting date and time of the video and the location information, and determine the importance of the scene based on a specific event or situation. The analysis unit can, for example, analyze the shooting date and time of the video and the location information, and determine the importance of the scene based on a specific event or situation. For example, AI can analyze the shooting date and time of the video and determine a scene of a specific event (e.g., a fireworks display) as an important scene. AI can also analyze the location information of the video and determine a scene of a tourist spot as an important scene. Furthermore, AI can analyze the shooting date and time of the video in combination with the location information, and determine a scene related to a specific season or event as an important scene. In this way, the importance of a scene can be determined based on a specific event or situation by analyzing the shooting date and time and the location information. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model to analyze the shooting date and time and the location information.

[0085] The analysis unit can estimate the user's emotion and automatically remove unnecessary scenes from the video based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and automatically removes unnecessary scenes from the video based on the estimated user's emotion. For example, if the user is bored, the AI ​​removes static scenes in the video as unnecessary scenes. In addition, if the user is impatient, the AI ​​can also remove long scenes in the video as unnecessary scenes. Furthermore, if the user is relaxed, the AI ​​can also remove tense scenes in the video as unnecessary scenes. This enables more personalized video editing by removing unnecessary scenes based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can perform analysis using an AI model to estimate the user's emotion.

[0086] The analysis unit can analyze text data in the video and evaluate the importance of a scene based on the content of the text. The analysis unit can, for example, analyze text data in the video and evaluate the importance of a scene based on the content of the text. For example, AI can detect important information from text data in the video and evaluate the scene as an important scene. AI can also detect emotional expressions from text data in the video and evaluate the scene as an important scene. Furthermore, AI can detect specific keywords from text data in the video and evaluate scenes containing the keywords as important scenes. This allows the importance of a scene to be evaluated more accurately by analyzing the text data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model to analyze the text data.

[0087] The analysis unit can analyze the viewing history of the video and determine the importance of a scene based on the past viewing tendency. The analysis unit can, for example, analyze the viewing history of the video and determine the importance of a scene based on the past viewing tendency. For example, an AI can analyze the user's past viewing history and determine a scene that has been frequently viewed as an important scene. The AI ​​can also analyze a specific genre from the user's past viewing history and determine a scene related to the genre as an important scene. Furthermore, the AI ​​can determine a scene containing a specific character or location as an important scene based on the user's past viewing history. Thereby, by analyzing the viewing history, the importance of a scene can be determined based on the past viewing tendency. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can perform analysis using an AI model to analyze the viewing history.

[0088] The selection unit can estimate the user's emotion and adjust the cut selection criteria based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and adjusts the cut selection criteria based on the estimated user's emotion. For example, if the user is excited, the AI ​​preferentially selects action scenes as cuts. Also, if the user is sad, the AI ​​can preferentially select moving scenes as cuts. Furthermore, if the user is relaxed, the AI ​​can preferentially select landscape scenes as cuts. This allows for more personalized video editing by adjusting the cut selection criteria based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can perform analysis using an AI model to estimate the user's emotion.

[0089] The selection unit can apply different cut selection algorithms depending on the genre of the video. The selection unit applies different cut selection algorithms depending on the genre of the video, for example. For example, in the case of a documentary video, the AI ​​preferentially selects interview scenes as cuts. In addition, in the case of an action movie, the AI ​​can also preferentially select action scenes as cuts. Furthermore, in the case of a comedy video, the AI ​​can also preferentially select humorous scenes as cuts. In this way, by applying a cut selection algorithm depending on the genre of the video, a more appropriate cut can be selected. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can perform analysis using an AI model to apply a cut selection algorithm depending on the genre of the video.

[0090] The selection unit can adjust the frequency of cut selection based on the length of the video. The selection unit adjusts the frequency of cut selection based on, for example, the length of the video. For example, in the case of a long video, the AI ​​sets the frequency of cuts low and emphasizes the continuity of the scene. In addition, in the case of a short video, the AI ​​can set the frequency of cuts high and perform editing with a good tempo. Furthermore, in the case of a medium-length video, the AI ​​can set the frequency of cuts to a medium level and perform balanced editing. By adjusting the frequency of cut selection based on the length of the video, it is possible to edit while maintaining the continuity of the scene. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can perform analysis using an AI model to adjust the frequency of cut selection based on the length of the video.

[0091] The selection unit can estimate the user's emotion and adjust the selection criteria for the transition effect based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and adjusts the selection criteria for the transition effect based on the estimated user's emotion. For example, if the user is excited, the AI ​​can select a dynamic transition effect. Also, if the user is sad, the AI ​​can select a fade-in / fade-out transition effect. Furthermore, if the user is relaxed, the AI ​​can select a smooth transition effect. This enables more personalized video editing by adjusting the selection criteria for the transition effect based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the selection unit can perform analysis using an AI model to estimate the user's emotion.

[0092] The selection unit can apply different transition effects depending on the theme of the video. The selection unit applies different transition effects depending on the theme of the video, for example. For example, in the case of documentary video, the AI ​​can apply a simple transition effect. In addition, in the case of action movies, the AI ​​can also apply a dynamic transition effect. Furthermore, in the case of comedy video, the AI ​​can also apply a humorous transition effect. This allows for more appropriate editing by applying transition effects depending on the theme of the video. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can perform analysis using an AI model to apply a transition effect depending on the theme of the video.

[0093] The selection unit can adjust the cut selection criteria based on the audience of the video. The selection unit adjusts the cut selection criteria based on the audience of the video, for example. For example, in the case of a video for young people, the AI ​​selects cuts with a good tempo. In addition, in the case of a video for elderly people, the AI ​​can also select cuts with a relaxed pace. Furthermore, in the case of a video for families, the AI ​​can also select cuts that are well-balanced. This allows for more appropriate editing by adjusting the cut selection criteria based on the audience. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can perform analysis using an AI model to adjust the cut selection criteria based on the audience.

[0094] The music section can estimate the user's emotion and adjust the music selection criteria based on the estimated user's emotion. The music section, for example, estimates the user's emotion and adjusts the music selection criteria based on the estimated user's emotion. For example, if the user is excited, the AI ​​can select up-tempo music. Also, if the user is sad, the AI ​​can select moving music. Furthermore, if the user is relaxed, the AI ​​can select calm music. This allows for more personalized video editing by adjusting the music selection criteria based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the music section may be performed, for example, using an AI, or may be performed without using an AI. For example, the music section can perform analysis using an AI model to estimate the user's emotion.

[0095] The music section can apply different music selection algorithms depending on the genre of the video. The music section, for example, applies different music selection algorithms depending on the genre of the video. For example, in the case of a documentary video, the AI ​​can select music that enhances the narration. In addition, in the case of an action movie, the AI ​​can select music that increases the tension. Furthermore, in the case of a comedy video, the AI ​​can select humorous music. In this way, by applying the music selection algorithm depending on the genre of the video, more appropriate music can be selected. Some or all of the above-mentioned processing in the music section may be performed, for example, using AI, or may be performed without using AI. For example, the music section can perform analysis using an AI model to apply the music selection algorithm depending on the genre of the video.

[0096] The music section can adjust the frequency of music selection based on the length of the video. The music section adjusts the frequency of music selection based on, for example, the length of the video. For example, in the case of a long video, the AI ​​sets the frequency of music selection low and emphasizes the continuity of the scenes. In addition, in the case of a short video, the AI ​​can set the frequency of music selection high and perform editing with a good tempo. Furthermore, in the case of a medium-length video, the AI ​​can set the frequency of music selection to a medium level and perform balanced editing. As a result, by adjusting the frequency of music selection based on the length of the video, editing can be performed while maintaining the continuity of the scenes. Part or all of the above-mentioned processing in the music section may be performed, for example, using AI, or may be performed without using AI. For example, the music section can perform analysis using an AI model to adjust the frequency of music selection based on the length of the video.

[0097] The music section can estimate the user's emotion and adjust the selection criteria for the sound effects based on the estimated user's emotion. The music section, for example, estimates the user's emotion and adjusts the selection criteria for the sound effects based on the estimated user's emotion. For example, if the user is excited, the AI ​​can select dynamic sound effects. Also, if the user is sad, the AI ​​can select moving sound effects. Furthermore, if the user is relaxed, the AI ​​can select gentle sound effects. This allows for more personalized video editing by adjusting the selection criteria for the sound effects based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the music section may be performed, for example, using an AI, or may be performed without using an AI. For example, the music section can perform analysis using an AI model to estimate the user's emotion.

[0098] The music section can apply different sound effects depending on the theme of the video. The music section, for example, applies different sound effects depending on the theme of the video. For example, in the case of a documentary video, the AI ​​applies sound effects that enhance the narration. Also, in the case of an action movie, the AI ​​can apply sound effects that increase tension. Furthermore, in the case of a comedy video, the AI ​​can apply humorous sound effects. This allows for more appropriate editing by applying sound effects depending on the theme of the video. Some or all of the above-mentioned processing in the music section may be performed, for example, using AI, or may be performed without using AI. For example, the music section can perform analysis using an AI model to apply sound effects depending on the theme of the video.

[0099] The music section can adjust the music selection criteria based on the audience of the video. The music section, for example, adjusts the music selection criteria based on the audience of the video. For example, for a video aimed at young people, the AI ​​can select up-tempo music. Also, for a video aimed at elderly people, the AI ​​can select calm music. Furthermore, for a video aimed at families, the AI ​​can select balanced music. This allows for more appropriate editing by adjusting the music selection criteria based on the audience. Some or all of the above-mentioned processing in the music section may be performed, for example, using AI or may be performed without using AI. For example, the music section can perform analysis using an AI model to adjust the music selection criteria based on the audience.

[0100] The color tone unit can estimate the user's emotion and adjust the color correction standard based on the estimated user's emotion. The color tone unit, for example, estimates the user's emotion and adjusts the color correction standard based on the estimated user's emotion. For example, if the user is excited, the AI ​​applies vivid color tones. Also, if the user is sad, the AI ​​can apply subdued color tones. Furthermore, if the user is relaxed, the AI ​​can apply soft color tones. This allows for more personalized video editing by adjusting the color correction standard based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the color tone unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the color tone unit can perform analysis using an AI model to estimate the user's emotion.

[0101] The color tone unit can apply different color correction algorithms depending on the genre of the video. The color tone unit, for example, applies different color correction algorithms depending on the genre of the video. For example, in the case of a documentary video, the AI ​​applies natural color correction. In addition, in the case of an action movie, the AI ​​can also apply color correction with high contrast. Furthermore, in the case of a comedy video, the AI ​​can also apply bright color correction. This allows for more appropriate color correction by applying a color correction algorithm depending on the genre of the video. Some or all of the above-mentioned processing in the color tone unit may be performed, for example, using AI, or may be performed without using AI. For example, the color tone unit can perform analysis using an AI model to apply a color correction algorithm depending on the genre of the video.

[0102] The color tone unit can adjust the standard of color tone correction based on the shooting environment of the video. The color tone unit adjusts the standard of color tone correction based on the shooting environment of the video, for example. For example, in the case of a video shot outdoors, the AI ​​applies color tone correction according to natural light. In addition, in the case of a video shot indoors, the AI ​​can also apply color tone correction according to artificial light. Furthermore, in the case of a video shot at night, the AI ​​can also apply color tone correction according to low illuminance. This allows more appropriate color tone correction by adjusting the standard of color tone correction based on the shooting environment. Part or all of the above-mentioned processing in the color tone unit may be performed, for example, using AI, or may be performed without using AI. For example, the color tone unit can perform analysis using an AI model to adjust the standard of color tone correction based on the shooting environment.

[0103] The color tone unit can estimate the user's emotion and determine the priority of color correction based on the estimated user's emotion. The color tone unit, for example, estimates the user's emotion and determines the priority of color correction based on the estimated user's emotion. For example, if the user is excited, the AI ​​prioritizes vivid color correction. Also, if the user is sad, the AI ​​can prioritize calm color correction. Furthermore, if the user is relaxed, the AI ​​can prioritize soft color correction. This enables more personalized video editing by determining the priority of color correction based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the color tone unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the color tone unit can perform analysis using an AI model to estimate the user's emotion.

[0104] The color tone unit can apply different color corrections depending on the theme of the video. The color tone unit, for example, applies different color corrections depending on the theme of the video. For example, in the case of documentary video, the AI ​​applies natural color correction. In addition, in the case of action movies, the AI ​​can also apply high-contrast color correction. Furthermore, in the case of comedy video, the AI ​​can also apply bright color correction. This allows for more appropriate editing by applying color corrections depending on the theme of the video. Some or all of the above-mentioned processing in the color tone unit may be performed, for example, using AI or may be performed without using AI. For example, the color tone unit can perform analysis using an AI model to apply color corrections depending on the theme of the video.

[0105] The color tone unit can adjust the standard of color tone correction based on the audience of the video. The color tone unit adjusts the standard of color tone correction based on the audience of the video, for example. For example, in the case of a video for young people, the AI ​​applies vivid color tone correction. In addition, in the case of a video for elderly people, the AI ​​can also apply subdued color tone correction. Furthermore, in the case of a video for families, the AI ​​can also apply balanced color tone correction. This allows for more appropriate editing by adjusting the standard of color tone correction based on the audience. Some or all of the above-mentioned processing in the color tone unit may be performed, for example, using AI, or may be performed without using AI. For example, the color tone unit can perform analysis using an AI model to adjust the standard of color tone correction based on the audience.

[0106] The subtitle unit can estimate the user's emotion and adjust the display method of the subtitles based on the estimated user's emotion. The subtitle unit, for example, estimates the user's emotion and adjusts the display method of the subtitles based on the estimated user's emotion. For example, when the user is excited, the AI ​​displays large and noticeable subtitles. Also, when the user is sad, the AI ​​can display subtitles in calm colors. Furthermore, when the user is relaxed, the AI ​​can display subtitles in soft fonts. This enables more personalized video editing by adjusting the display method of the subtitles based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the subtitle unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the subtitle unit can perform analysis using an AI model to estimate the user's emotion.

[0107] The subtitle unit can apply different subtitle display algorithms depending on the genre of the video. The subtitle unit applies different subtitle display algorithms depending on the genre of the video, for example. For example, in the case of a documentary video, the AI ​​displays simple and easy-to-read subtitles. In addition, in the case of an action movie, the AI ​​can also display dynamic subtitles. Furthermore, in the case of a comedy video, the AI ​​can also display humorous subtitles. This allows for more appropriate subtitle display by applying a subtitle display algorithm depending on the genre of the video. Some or all of the above-mentioned processing in the subtitle unit may be performed using, for example, AI, or may be performed without using AI. For example, the subtitle unit can perform analysis using an AI model to apply a subtitle display algorithm depending on the genre of the video.

[0108] The subtitle unit can adjust the display method of the subtitles based on the language of the video. The subtitle unit adjusts the display method of the subtitles based on, for example, the language of the video. For example, in the case of an English video, the AI ​​displays English subtitles. Also, in the case of a Japanese video, the AI ​​can display Japanese subtitles. Furthermore, in the case of a multilingual video, the AI ​​can display subtitles in multiple languages. This allows for more appropriate subtitle display by adjusting the display method of the subtitles based on the language of the video. Part or all of the above-mentioned processing in the subtitle unit may be performed, for example, using AI, or may be performed without using AI. For example, the subtitle unit can perform analysis using an AI model to adjust the display method of the subtitles based on the language of the video.

[0109] The subtitle unit can estimate the user's emotion and adjust the display timing of the subtitles based on the estimated user's emotion. The subtitle unit, for example, estimates the user's emotion and adjusts the display timing of the subtitles based on the estimated user's emotion. For example, if the user is excited, the AI ​​advances the display timing of the subtitles. Also, if the user is sad, the AI ​​can delay the display timing of the subtitles. Furthermore, if the user is relaxed, the AI ​​can naturally adjust the display timing of the subtitles. This enables more personalized video editing by adjusting the display timing of the subtitles based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the subtitle unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the subtitle unit can perform analysis using an AI model to estimate the user's emotion.

[0110] The subtitle unit can apply different subtitle display depending on the theme of the video. The subtitle unit applies different subtitle display depending on the theme of the video, for example. For example, in the case of a documentary video, the AI ​​can display simple and easy-to-read subtitles. In addition, in the case of an action movie, the AI ​​can also display dynamic subtitles. Furthermore, in the case of a comedy video, the AI ​​can display humorous subtitles. This allows for more appropriate editing by applying subtitle display depending on the theme of the video. Some or all of the above-mentioned processing in the subtitle unit may be performed, for example, using AI, or may be performed without using AI. For example, the subtitle unit can perform analysis using an AI model to apply subtitle display depending on the theme of the video.

[0111] The subtitle unit can adjust the display method of the subtitles based on the viewer demographic of the video. The subtitle unit, for example, adjusts the display method of the subtitles based on the viewer demographic of the video. For example, in the case of a video for young people, the AI ​​can display colorful and eye-catching subtitles. In addition, in the case of a video for elderly people, the AI ​​can also display large and easy-to-read subtitles. Furthermore, in the case of a video for families, the AI ​​can also display balanced subtitles. This allows for more appropriate editing by adjusting the display method of the subtitles based on the viewer demographic. Some or all of the above-mentioned processing in the subtitle unit may be performed, for example, using AI, or may be performed without using AI. For example, the subtitle unit can perform analysis using an AI model to adjust the display method of the subtitles based on the viewer demographic.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The video editing system may further include a history analysis unit that analyzes the user's viewing history. The history analysis unit may analyze the user's preferences for genres and scenes of videos that the user has viewed in the past, and may select cuts and effects for video editing based on the results. For example, if the user likes action movies, cuts that emphasize action scenes may be selected. Also, if the user likes documentaries, cuts that emphasize interview scenes may be selected. Furthermore, if the user likes comedies, cuts that emphasize humorous scenes may be selected. In this way, video editing is performed based on the user's viewing history, and more personalized videos are provided.

[0114] The video editing system may further include an audio analysis unit that analyzes the audio data of the video. The audio analysis unit may analyze the audio data in the video and evaluate the importance of a scene based on the content of the audio. For example, emotional remarks may be detected from the audio data and the scene may be evaluated as an important scene. The frequency of conversation may also be analyzed from the audio data and a scene with a lot of conversation may be evaluated as an important scene. Furthermore, a specific keyword may be detected from the audio data and a scene containing the keyword may be evaluated as an important scene. In this way, the importance of a scene may be evaluated more accurately by analyzing the audio data.

[0115] The video editing system may further include a location information analysis unit that analyzes the shooting date and time and location information of the video. The location information analysis unit can analyze the shooting date and time and location information of the video and determine the importance of a scene based on a specific event or situation. For example, the shooting date and time may be analyzed to determine a scene of a specific event (e.g., a fireworks display) as an important scene. The location information may also be analyzed to determine a scene of a tourist spot as an important scene. Furthermore, the shooting date and time and location information may be analyzed in combination to determine a scene related to a specific season or event as an important scene. In this way, the importance of a scene can be determined based on a specific event or situation by analyzing the shooting date and time and location information.

[0116] The video editing system may further include a text analysis unit that analyzes text data in the video. The text analysis unit may analyze the text data in the video and evaluate the importance of a scene based on the content of the text. For example, important information may be detected from the text data and the scene may be evaluated as an important scene. Emotional expressions may also be detected from the text data and the scene may be evaluated as an important scene. Furthermore, a specific keyword may be detected from the text data and a scene containing the keyword may be evaluated as an important scene. This allows the importance of a scene to be evaluated more accurately by analyzing the text data.

[0117] The video editing system can further include an audience analysis unit that adjusts cut selection criteria based on the audience of the video. The audience analysis unit can adjust the cut selection criteria based on the audience of the video. For example, for video aimed at young people, cuts with a good tempo can be selected. Also, for video aimed at elderly people, relaxed cuts can be selected. Furthermore, for video aimed at families, well-balanced cuts can be selected. In this way, more appropriate editing can be achieved by adjusting the cut selection criteria based on the audience.

[0118] The video editing system can further estimate the user's emotions and automatically remove unnecessary scenes from the video based on the estimated user's emotions. For example, if the user is bored, static scenes can be removed as unnecessary scenes. Also, if the user is impatient, long scenes can be removed as unnecessary scenes. Furthermore, if the user is relaxed, tense scenes can be removed as unnecessary scenes. This enables more personalized video editing by removing unnecessary scenes based on the user's emotions.

[0119] The video editing system can further estimate the user's emotion and adjust the cut selection criteria based on the estimated user's emotion. For example, if the user is excited, action scenes can be preferentially selected as cuts. Also, if the user is sad, moving scenes can be preferentially selected as cuts. Furthermore, if the user is relaxed, landscape scenes can be preferentially selected as cuts. This allows for more personalized video editing by adjusting the cut selection criteria based on the user's emotion.

[0120] The video editing system can further estimate the user's emotions and adjust the transition effect selection criteria based on the estimated user's emotions. For example, if the user is excited, a dynamic transition effect can be selected. If the user is sad, a fade-in / fade-out transition effect can be selected. Furthermore, if the user is relaxed, a smooth transition effect can be selected. This enables more personalized video editing by adjusting the transition effect selection criteria based on the user's emotions.

[0121] The video editing system can further estimate the user's emotion and adjust the music selection criteria based on the estimated user's emotion. For example, if the user is excited, upbeat music can be selected. If the user is sad, moving music can be selected. If the user is relaxed, calm music can be selected. This allows for more personalized video editing by adjusting the music selection criteria based on the user's emotion.

[0122] The video editing system can further estimate the user's emotion and adjust the display method of the subtitles based on the estimated user's emotion. For example, if the user is excited, large and noticeable subtitles can be displayed. If the user is sad, subtitles in subdued colors can be displayed. Furthermore, if the user is relaxed, subtitles in soft fonts can be displayed. This allows for more personalized video editing by adjusting the display method of the subtitles based on the user's emotion.

[0123] The process flow of the second embodiment will be briefly described below.

[0124] Step 1: The analysis unit analyzes the content of the video. The content of the video includes scenes, objects, actions, etc. The analysis unit detects changes in objects and scenes in the video and identifies important and unnecessary scenes. For example, it can analyze people's movements and facial expressions to detect changes in emotions. Step 2: The Selector selects the best cuts and effects based on the content analyzed by the Analyser. The Selector adds transition effects to make scene changes smoother, or applies filters to certain scenes to change the atmosphere. Step 3: The music department adds music and sound effects based on the cuts and effects selected by the selection department. The music department automatically selects music that matches the atmosphere of the video and adds sound effects to match the scenes. Step 4: The color tone section performs color correction based on the cuts and effects selected by the selection section. The color tone section adjusts the color tone of the image to achieve an overall balance. Step 5: The subtitling unit inserts subtitles based on the scenes and effects selected by the selection unit. The subtitling unit inserts subtitles into specific scenes to provide information to the viewers.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0126] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0127] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned analysis unit, selection unit, music unit, color tone unit, and subtitle unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit analyzes the content of the video using the camera 42 and microphone 38B of the smart device 14, and detects changes in objects and scenes in the video by the control unit 46A. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects optimal cuts and effects based on the analyzed content. The music unit is realized, for example, by the control unit 46A of the smart device 14, and adds music and sound effects that match the atmosphere of the video. The color tone unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the color tone of the video. The subtitle unit is realized, for example, by the control unit 46A of the smart device 14, and inserts subtitles into specific scenes. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] Fig. 4 shows an example of 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.

[0137] 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.

[0138] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned analysis unit, selection unit, music unit, color tone unit, and subtitle unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit analyzes the content of the video using the camera 42 and microphone 238 of the smart glasses 214, and detects changes in objects and scenes in the video by the control unit 46A. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects optimal cuts and effects based on the analyzed content. The music unit is realized, for example, by the control unit 46A of the smart glasses 214, and adds music and sound effects that match the atmosphere of the video. The color tone unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the color tone of the video. The subtitle unit is realized, for example, by the control unit 46A of the smart glasses 214, and inserts subtitles into specific scenes. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0147] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0151] 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 performed in a secure state.

[0152] 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.

[0153] 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.

[0154] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0155] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0156] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0157] 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 voice indicating a user input for 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.

[0158] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0159] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements including the above-mentioned analysis unit, selection unit, music unit, color tone unit, and subtitle unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit analyzes the content of the video using the camera 42 and microphone 238 of the headset type terminal 314, and detects changes in objects and scenes in the video by the control unit 46A. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects optimal cuts and effects based on the analyzed content. The music unit is realized, for example, by the control unit 46A of the headset type terminal 314, and adds music and sound effects that match the atmosphere of the video. The color tone unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the color tone of the video. The subtitle unit is realized, for example, by the control unit 46A of the headset type terminal 314, and inserts subtitles into specific scenes. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0165] 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 the voice according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0167] 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 performed in a secure state.

[0168] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0169] 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.

[0170] 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.

[0171] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0172] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0173] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0174] 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 a voice indicating a user input for 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.

[0175] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0176] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0177] Each of the multiple elements including the above-mentioned analysis unit, selection unit, music unit, color tone unit, and subtitle unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit analyzes the content of the video using the camera 42 and microphone 238 of the robot 414, and detects changes in objects and scenes in the video by the control unit 46A. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects optimal cuts and effects based on the analyzed content. The music unit is realized, for example, by the control unit 46A of the robot 414, and adds music and sound effects that match the atmosphere of the video. The color tone unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the color tone of the video. The subtitle unit is realized, for example, by the control unit 46A of the robot 414, and inserts subtitles into specific scenes. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0178] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0179] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0181] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0183] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0185] 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.

[0186] 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 Universal Serial Bus (USB) 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.

[0187] In addition, 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 upon request from the data processing device 12.

[0188] 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.

[0189] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0190] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0191] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0192] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0194] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0196] (Appendix 1) An analysis section that analyzes the content of the video; a selection unit that selects appropriate cuts and effects based on the content analyzed by the analysis unit; a music section that adds music and sound effects based on the cuts and effects selected by the selection section; a color tone section that performs color tone correction based on the cut or effect selected by the selection section; a subtitle unit that inserts subtitles based on the cuts and effects selected by the selection unit. A system characterized by: (Appendix 2) The analysis unit includes: Detect object or scene changes in the video and identify important or unwanted scenes 2. The system of claim 1, (Appendix 3) The selection unit is Selecting cuts to highlight important scenes identified by the analysis unit 2. The system of claim 1, (Appendix 4) The selection unit is Add transition effects to smooth scene changes 2. The system of claim 1, (Appendix 5) The music department: Automatically select music that matches the atmosphere of the video and add sound effects that suit the scene 2. The system of claim 1, (Appendix 6) The color tone portion is Adjust the color tone of the image to achieve the overall balance 2. The system of claim 1, (Appendix 7) The subtitle section includes: Add subtitles to specific scenes to give information to your audience 2. The system of claim 1, (Appendix 8) The analysis unit includes: Estimating user emotions and identifying important scenes in a video based on the estimated user emotions 2. The system of claim 1, (Appendix 9) The analysis unit includes: Analyzes audio data in video and evaluates the importance of scenes based on audio content 2. The system of claim 1, (Appendix 10) The analysis unit includes: Analyzes footage and location information to determine the importance of scenes based on specific events or situations 2. The system of claim 1, (Appendix 11) The analysis unit includes: Estimating user emotions and automatically removing unnecessary scenes from video based on the estimated user emotions 2. The system of claim 1, (Appendix 12) The analysis unit includes: Analyzes text data in a video and evaluates the importance of scenes based on the content of the text 2. The system of claim 1, (Appendix 13) The analysis unit includes: Analyze video viewing history and determine the importance of scenes based on past viewing habits 2. The system of claim 1, (Appendix 14) The selection unit is Estimating a user's emotion and adjusting cut selection criteria based on the estimated user's emotion 2. The system of claim 1, (Appendix 15) The selection unit is Apply different cut selection algorithms depending on the genre of the footage 2. The system of claim 1, (Appendix 16) The selection unit is Adjust cut selection frequency based on footage length 2. The system of claim 1, (Appendix 17) The selection unit is Estimating a user's emotion and adjusting a selection criterion for transition effects based on the estimated user's emotion 2. The system of claim 1, (Appendix 18) The selection unit is Apply different transition effects according to the theme of the video 2. The system of claim 1, (Appendix 19) The selection unit is Tailor your cut selection criteria based on your film's audience 2. The system of claim 1, (Appendix 20) The music department: To estimate a user's emotion and adjust a music selection criterion based on the estimated user's emotion. 2. The system of claim 1, (Appendix 21) The music department: Apply different music selection algorithms depending on the genre of the video 2. The system of claim 1, (Appendix 22) The music department: Adjust the frequency of music selection based on the length of footage 2. The system of claim 1, (Appendix 23) The music department: To estimate a user's emotion and adjust a selection criterion for a sound effect based on the estimated user's emotion. 2. The system of claim 1, (Appendix 24) The music department: Apply different sound effects according to the theme of the video 2. The system of claim 1, (Appendix 25) The music department: Tailor your music selections based on your video's audience 2. The system of claim 1, (Appendix 26) The color tone portion is To estimate a user's emotion and adjust a standard of color correction based on the estimated user's emotion. 2. The system of claim 1, (Appendix 27) The color tone portion is Apply different color correction algorithms depending on the genre of the footage 2. The system of claim 1, (Appendix 28) The color tone portion is Adjust the color correction standard based on the shooting environment of the image 2. The system of claim 1, (Appendix 29) The color tone portion is A user's emotion is estimated, and a priority order for color correction is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 30) The color tone portion is Apply different color corrections depending on the theme of the footage 2. The system of claim 1, (Appendix 31) The color tone portion is Adjust color correction standards based on the video's audience 2. The system of claim 1, (Appendix 32) The subtitle section includes: Estimate user emotions and adjust the way subtitles are displayed based on the estimated user emotions 2. The system of claim 1, (Appendix 33) The subtitle section includes: Apply different subtitle display algorithms depending on the genre of the video 2. The system of claim 1, (Appendix 34) The subtitle section includes: Adjust how subtitles are displayed based on the language of the video 2. The system of claim 1, (Appendix 35) The subtitle section includes: Estimate the user's emotions and adjust the timing of subtitle display based on the estimated user emotions. 2. The system of claim 1, (Appendix 36) The subtitle section includes: Apply different subtitle display depending on the theme of the video 2. The system of claim 1, (Appendix 37) The subtitle section includes: Tailor how subtitles are displayed based on the video's audience 2. The system of claim 1, [Explanation of symbols]

[0197] 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. An analysis section that analyzes the content of the video; a selection unit that selects appropriate cuts and effects based on the content analyzed by the analysis unit; a music section that adds music and sound effects based on the cuts and effects selected by the selection section; a color tone section that performs color tone correction based on the cut or effect selected by the selection section; a subtitle unit that inserts subtitles based on the cuts and effects selected by the selection unit. A system characterized by:

2. The analysis unit includes: Detect object or scene changes in the video and identify important or unwanted scenes 2. The system of claim 1 .

3. The selection unit is Selecting cuts to highlight important scenes identified by the analysis unit 2. The system of claim 1 .

4. The selection unit is Add transition effects to smooth scene changes 2. The system of claim 1 .

5. The music department: Automatically select music that matches the atmosphere of the video and add sound effects that suit the scene 2. The system of claim 1 .

6. The color tone portion is Adjust the color tone of the image to achieve the overall balance 2. The system of claim 1 .

7. The subtitle section includes: Add subtitles to specific scenes to give information to your audience 2. The system of claim 1 .

8. The analysis unit includes: Estimating user emotions and identifying important scenes in a video based on the estimated user emotions 2. The system of claim 1 .

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