System

Abstract

An object of a system according to an embodiment is to significantly improve the efficiency of video editing.SOLUTION: A system according to an embodiment includes a generation AI, a learning unit, and an editing unit. The generation AI learns the editing style of the video poster. The learning unit learns an editing style of a moving image poster. The editing unit automatically edits the live video photographed by the moving image poster on the basis of the editing style learned by the learning 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] With conventional technology, video editing requires a lot of time and effort, so there is a need for more efficient editing, but there is room for improvement.

[0005] The system according to the embodiment aims to significantly improve the efficiency of video editing. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a learning unit, and an editing unit. The generation AI learns the editing style of a video poster. The learning unit learns the editing style of the video poster. The editing unit automatically edits raw footage shot by the video poster based on the editing style learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can significantly improve the efficiency of video editing. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A video editing system according to an embodiment of the present invention uses a generative AI to quickly edit videos. This system uses a generative AI to learn editing styles, such as subtitles and sound effects, and allows video uploaders to automatically complete editing simply by shooting raw footage. This allows the video editing system to significantly reduce the amount of work required for video editing. It can also be used for video media such as television and breaking news, enabling the rapid provision of information.

[0029] A video editing system according to an embodiment includes a generation AI, a learning unit, and an editing unit. The generation AI includes a learning unit that learns the editing style of a video poster. For example, the learning unit learns the editing work the video poster has done in the past and understands that style. The learning unit learns the font and color of a specific caption, the timing of sound effects, and the like. The generation AI includes an editing unit that automatically edits raw video shot by the video poster based on the editing style learned by the learning unit. For example, the editing unit displays captions and inserts sound effects at specific times in the shot video. The editing unit can also add captions and sound effects to breaking news footage in real time. In this way, the video editing system learns the editing style of the video poster and automatically completes editing, significantly reducing the amount of work required for video editing.

[0030] The learning unit can learn the font or color of specific captions and the timing of sound effects. For example, the generation AI analyzes the emotions of the video uploader in real time and adds bright colors and light sound effects to scenes with positive emotions. For example, it inserts bright captions in scenes where people are smiling. This allows for more personalized editing by learning the font, color, and timing of specific captions.

[0031] The editing department can display captions and insert sound effects at specific times in the filmed footage. For example, the generative AI can learn the video editing styles of different genres, such as comedy, drama, and action, and automatically select captions and sound effects according to the genre. For example, humorous sound effects can be added to comedy. This allows for the display of captions and insertion of sound effects at specific times to enhance the visual and auditory effects.

[0032] The editorial department can add captions and sound effects to breaking news footage in real time. For example, the generative AI analyzes the video uploader's past editing history and learns changes in editing style. For example, it analyzes the patterns of captions and sound effects used in past videos. This allows for the rapid provision of information by adding captions and sound effects to breaking news footage in real time.

[0033] The learning unit can instruct the generation AI on an editing style that matches a specific theme or event, and edit based on that style. For example, the learning unit allows the generation AI to learn the editing styles of multiple video contributors and combine them to generate a new editing style. For example, it can combine the caption and sound effect patterns of different contributors. This allows for more diverse editing by instructing the AI ​​on an editing style that matches a specific theme or event.

[0034] The learning unit can learn video editing styles for different genres and automatically select the optimal editing style for each genre. For example, the generative AI can learn video editing styles for different genres, such as comedy, drama, and action, and automatically select subtitles and sound effects according to the genre. For example, humorous sound effects are added to comedy. This allows the system to learn video editing styles for different genres and provide the optimal editing style for each genre, enabling more diverse editing.

[0035] The learning unit analyzes the video uploader's past editing history and learns the evolution and transition of editing style, thereby enabling the provision of more advanced editing styles. For example, the learning unit uses a generative AI to analyze the video uploader's past editing history and learn the transition of editing style. For example, it analyzes the patterns of subtitles and sound effects used in past videos. This allows the system to analyze past editing history and learn the evolution and transition of editing style, thereby enabling the provision of more advanced editing styles.

[0036] The learning unit can learn the editing styles of other video contributors and combine different styles to generate new editing styles. For example, the learning unit's generation AI learns the editing styles of multiple video contributors and combines them to generate new editing styles. For example, it combines the caption and sound effect patterns of different contributors. This allows it to learn the editing styles of other video contributors and combine different styles to provide new editing styles.

[0037] The learning unit can learn from audio data and subtitle data and provide a comprehensive editing style that includes voice recognition and subtitle generation. For example, the learning unit uses a generation AI to analyze audio data and automatically generate subtitles using voice recognition technology. For example, conversations in a video are converted into text and displayed as subtitles. This allows the system to learn from audio data and subtitle data and provide a comprehensive editing style that includes voice recognition and subtitle generation.

[0038] The editing department can analyze the content of the video and perform optimal editing for each scene. For example, generative AI can analyze the content of the video and perform optimal editing for each scene. For example, dynamic sound effects can be added to action scenes. This allows for more effective editing by analyzing the content of the video and performing optimal editing for each scene.

[0039] The editing department can analyze multiple camera footage simultaneously and automatically select the optimal camera angle for editing. For example, the generative AI can analyze multiple camera footage simultaneously and automatically select the optimal camera angle for editing. For example, close-ups can be used for important scenes. This allows for more effective visual expression by analyzing multiple camera footage simultaneously and selecting the optimal camera angle for editing.

[0040] The editorial department can edit live streaming video in real time and provide the edited video to viewers instantly. For example, the editorial department will build a system in which generative AI analyzes live streaming video in real time and performs editing in real time. For example, adding subtitles and sound effects during the live broadcast. This allows live streaming video to be edited in real time and the edited video to be provided to viewers in real time, enabling a more interactive viewing experience.

[0041] The editorial department can automatically generate subtitles in different languages ​​and perform multilingual editing to accommodate international audiences. For example, the editorial department uses generation AI to automatically generate subtitles in different languages ​​using speech recognition technology. For example, subtitles in English, French, Chinese, etc. can be displayed simultaneously. This allows the automatic generation of subtitles in different languages ​​and multilingual editing to accommodate international audiences, making it possible to cater to a wider audience.

[0042] The learning unit can learn the font or color of specific captions and the timing of sound effects. For example, the generation AI analyzes the emotions of the video uploader in real time and adds bright colors and light sound effects to scenes with positive emotions. For example, it inserts bright captions in scenes where people are smiling. This allows for more personalized editing by learning the font, color, and timing of specific captions.

[0043] The editing department can display captions and insert sound effects at specific times in the filmed footage. For example, the generative AI can learn the video editing styles of different genres, such as comedy, drama, and action, and automatically select captions and sound effects according to the genre. For example, humorous sound effects can be added to comedy. This allows for the display of captions and insertion of sound effects at specific times to enhance the visual and auditory effects.

[0044] The editorial department can add captions and sound effects to breaking news footage in real time. For example, the generative AI analyzes the video uploader's past editing history and learns changes in editing style. For example, it analyzes the patterns of captions and sound effects used in past videos. This allows for the rapid provision of information by adding captions and sound effects to breaking news footage in real time.

[0045] The learning unit can instruct the generation AI on an editing style that matches a specific theme or event, and edit based on that style. For example, the learning unit allows the generation AI to learn the editing styles of multiple video contributors and combine them to generate a new editing style. For example, it can combine the caption and sound effect patterns of different contributors. This allows for more diverse editing by instructing the AI ​​on an editing style that matches a specific theme or event.

[0046] The learning unit can learn video editing styles for different genres and automatically select the optimal editing style for each genre. For example, the generative AI can learn video editing styles for different genres, such as comedy, drama, and action, and automatically select subtitles and sound effects according to the genre. For example, humorous sound effects are added to comedy. This allows the system to learn video editing styles for different genres and provide the optimal editing style for each genre, enabling more diverse editing.

[0047] The learning unit analyzes the video uploader's past editing history and learns the evolution and transition of editing style, thereby enabling the provision of more advanced editing styles. For example, the learning unit uses a generative AI to analyze the video uploader's past editing history and learn the transition of editing style. For example, it analyzes the patterns of subtitles and sound effects used in past videos. This allows the system to analyze past editing history and learn the evolution and transition of editing style, thereby enabling the provision of more advanced editing styles.

[0048] The learning unit can learn the editing styles of other video contributors and combine different styles to generate new editing styles. For example, the learning unit's generation AI learns the editing styles of multiple video contributors and combines them to generate new editing styles. For example, it combines the caption and sound effect patterns of different contributors. This allows it to learn the editing styles of other video contributors and combine different styles to provide new editing styles.

[0049] The learning unit can learn from audio data and subtitle data and provide a comprehensive editing style that includes voice recognition and subtitle generation. For example, the learning unit uses a generation AI to analyze audio data and automatically generate subtitles using voice recognition technology. For example, conversations in a video are converted into text and displayed as subtitles. This allows the system to learn from audio data and subtitle data and provide a comprehensive editing style that includes voice recognition and subtitle generation.

[0050] The editing department can analyze the content of the video and perform optimal editing for each scene. For example, generative AI can analyze the content of the video and perform optimal editing for each scene. For example, dynamic sound effects can be added to action scenes. This allows for more effective editing by analyzing the content of the video and performing optimal editing for each scene.

[0051] The editing department can analyze multiple camera footage simultaneously and automatically select the optimal camera angle for editing. For example, the generative AI can analyze multiple camera footage simultaneously and automatically select the optimal camera angle for editing. For example, close-ups can be used for important scenes. This allows for more effective visual expression by analyzing multiple camera footage simultaneously and selecting the optimal camera angle for editing.

[0052] The editorial department can edit live streaming video in real time and provide the edited video to viewers instantly. For example, the editorial department will build a system in which generative AI analyzes live streaming video in real time and performs editing in real time. For example, adding subtitles and sound effects during the live broadcast. This allows live streaming video to be edited in real time and the edited video to be provided to viewers in real time, enabling a more interactive viewing experience.

[0053] The editorial department can automatically generate subtitles in different languages ​​and perform multilingual editing to accommodate international audiences. For example, the editorial department uses generation AI to automatically generate subtitles in different languages ​​using speech recognition technology. For example, subtitles in English, French, Chinese, etc. can be displayed simultaneously. This allows the automatic generation of subtitles in different languages ​​and multilingual editing to accommodate international audiences, making it possible to cater to a wider audience.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The video editing system also includes a speech recognition unit. This unit analyzes the audio in the video and can automatically generate subtitles based on specific keywords and phrases. For example, in an interview video, answers to the interviewer's questions can be displayed as subtitles in real time. The speech recognition unit can also analyze audio in different languages ​​and generate subtitles in multiple languages. This makes it possible to provide information effectively to international audiences.

[0056] The video editing system further includes a scene analysis unit. The scene analysis unit analyzes scenes in the video and can select the optimal editing style for each scene. For example, dynamic sound effects can be added to action scenes, while calm music can be inserted in romantic scenes. The scene analysis unit can also recognize specific objects in the video and display information related to the object as subtitles. This enhances the visual and auditory effects.

[0057] The video editing system further includes a user feedback unit. The user feedback unit collects feedback from viewers and can optimize the editing style based on that data. For example, if a viewer gives a high rating to a particular scene, the editing style for that scene can be applied to other videos. The user feedback unit can also analyze viewer comments and ratings in real time and dynamically change the editing content. This allows for flexible editing that meets the needs of viewers.

[0058] The video editing system further includes an automatic transition unit. The automatic transition unit can automatically generate transition effects between scenes. For example, it can add transition effects such as fade-in, fade-out, and slide when switching scenes. The automatic transition unit can also select the optimal transition effect depending on the content of the scene. This makes the video flow smoother and enhances its visual appeal.

[0059] The video editing system also includes an automatic subtitling unit, which can analyze the audio in a video and automatically generate subtitles. For example, in an interview video, the conversation between the interviewer and interviewee can be converted into text in real time and displayed as subtitles. The automatic subtitling unit can also analyze audio in different languages ​​and generate subtitles in multiple languages, making it possible to provide information effectively to international audiences.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The learning module learns the editing style of the video uploader. For example, the learning module studies the video uploader's past editing work and understands their style. The learning module learns the font and color of specific subtitles, the timing of sound effects, etc. Step 2: The editing department automatically edits the raw footage shot by the video uploader based on the editing style learned by the learning department. For example, the editing department displays captions and inserts sound effects at specific times in the shot footage. The editing department can also add captions and sound effects in real time to breaking news footage.

[0062] (Example 2) A video editing system according to an embodiment of the present invention uses a generative AI to quickly edit videos. This system uses a generative AI to learn editing styles, such as subtitles and sound effects, and allows video uploaders to automatically complete editing simply by shooting raw footage. This allows the video editing system to significantly reduce the amount of work required for video editing. It can also be used for video media such as television and breaking news, enabling the rapid provision of information.

[0063] A video editing system according to an embodiment includes a generation AI, a learning unit, and an editing unit. The generation AI includes a learning unit that learns the editing style of a video poster. For example, the learning unit learns the editing work the video poster has done in the past and understands that style. The learning unit learns the font and color of a specific caption, the timing of sound effects, and the like. The generation AI includes an editing unit that automatically edits raw video shot by the video poster based on the editing style learned by the learning unit. For example, the editing unit displays captions and inserts sound effects at specific times in the shot video. The editing unit can also add captions and sound effects to breaking news footage in real time. In this way, the video editing system learns the editing style of the video poster and automatically completes editing, significantly reducing the amount of work required for video editing.

[0064] The learning unit can learn the font or color of specific captions and the timing of sound effects. For example, the generation AI analyzes the emotions of the video uploader in real time and adds bright colors and light sound effects to scenes with positive emotions. For example, it inserts bright captions in scenes where people are smiling. This allows for more personalized editing by learning the font, color, and timing of specific captions.

[0065] The editing department can display captions and insert sound effects at specific times in the filmed footage. For example, the generative AI can learn the video editing styles of different genres, such as comedy, drama, and action, and automatically select captions and sound effects according to the genre. For example, humorous sound effects can be added to comedy. This allows for the display of captions and insertion of sound effects at specific times to enhance the visual and auditory effects.

[0066] The editorial department can add captions and sound effects to breaking news footage in real time. For example, the generative AI analyzes the video uploader's past editing history and learns changes in editing style. For example, it analyzes the patterns of captions and sound effects used in past videos. This allows for the rapid provision of information by adding captions and sound effects to breaking news footage in real time.

[0067] The learning unit can instruct the generation AI on an editing style that matches a specific theme or event, and edit based on that style. For example, the learning unit allows the generation AI to learn the editing styles of multiple video contributors and combine them to generate a new editing style. For example, it can combine the caption and sound effect patterns of different contributors. This allows for more diverse editing by instructing the AI ​​on an editing style that matches a specific theme or event.

[0068] The learning unit can estimate the emotions of the video uploader and optimize the editing style based on those emotions. For example, the learning unit's generative AI analyzes the emotions of the video uploader in real time and adds bright colors and cheerful sound effects to scenes with positive emotions. For example, it inserts cheerful captions in scenes where the person is smiling. This allows the editing style to be optimized based on the emotions of the video uploader, making it possible to create more emotionally appealing edits.

[0069] The learning unit can learn video editing styles for different genres and automatically select the optimal editing style for each genre. For example, the generative AI can learn video editing styles for different genres, such as comedy, drama, and action, and automatically select subtitles and sound effects according to the genre. For example, humorous sound effects are added to comedy. This allows the system to learn video editing styles for different genres and provide the optimal editing style for each genre, enabling more diverse editing.

[0070] The learning unit analyzes the video uploader's past editing history and learns the evolution and transition of editing style, thereby enabling the provision of more advanced editing styles. For example, the learning unit uses a generative AI to analyze the video uploader's past editing history and learn the transition of editing style. For example, it analyzes the patterns of subtitles and sound effects used in past videos. This allows the system to analyze past editing history and learn the evolution and transition of editing style, thereby enabling the provision of more advanced editing styles.

[0071] The learning unit can learn the editing styles of other video contributors and combine different styles to generate new editing styles. For example, the learning unit's generation AI learns the editing styles of multiple video contributors and combines them to generate new editing styles. For example, it combines the caption and sound effect patterns of different contributors. This allows it to learn the editing styles of other video contributors and combine different styles to provide new editing styles.

[0072] The learning unit can learn from audio data and subtitle data and provide a comprehensive editing style that includes voice recognition and subtitle generation. For example, the learning unit uses a generation AI to analyze audio data and automatically generate subtitles using voice recognition technology. For example, conversations in a video are converted into text and displayed as subtitles. This allows the system to learn from audio data and subtitle data and provide a comprehensive editing style that includes voice recognition and subtitle generation.

[0073] The learning unit can use the emotion estimation function to analyze the emotional reactions of video viewers and adjust the editing style in real time based on that feedback. For example, the learning unit can use the emotion estimation function to analyze the emotional reactions of video viewers in real time and adjust the editing style based on that data. For example, humorous sound effects can be added to scenes in which the viewer is laughing. This allows the emotion estimation function to analyze the emotional reactions of viewers and adjust the editing style in real time based on that feedback.

[0074] The editing department can estimate the emotions of the video uploader in real time and automatically insert captions and sound effects that correspond to the emotions. For example, the editing department uses a generative AI to analyze the emotions of the video uploader in real time and automatically insert captions and sound effects that correspond to the emotions. For example, cheerful captions can be added to scenes where the video uploader is smiling. This allows for editing that appeals to more emotions by automatically inserting captions and sound effects that correspond to the emotions of the video uploader.

[0075] The editing department can analyze the content of the video and perform optimal editing for each scene. For example, generative AI can analyze the content of the video and perform optimal editing for each scene. For example, dynamic sound effects can be added to action scenes. This allows for more effective editing by analyzing the content of the video and performing optimal editing for each scene.

[0076] The editing department can analyze multiple camera footage simultaneously and automatically select the optimal camera angle for editing. For example, the generative AI can analyze multiple camera footage simultaneously and automatically select the optimal camera angle for editing. For example, close-ups can be used for important scenes. This allows for more effective visual expression by analyzing multiple camera footage simultaneously and selecting the optimal camera angle for editing.

[0077] The editorial department can edit live streaming video in real time and provide the edited video to viewers instantly. For example, the editorial department will build a system in which generative AI analyzes live streaming video in real time and performs editing in real time. For example, adding subtitles and sound effects during the live broadcast. This allows live streaming video to be edited in real time and the edited video to be provided to viewers in real time, enabling a more interactive viewing experience.

[0078] The editorial department can automatically generate subtitles in different languages ​​and perform multilingual editing to accommodate international audiences. For example, the editorial department uses generation AI to automatically generate subtitles in different languages ​​using speech recognition technology. For example, subtitles in English, French, Chinese, etc. can be displayed simultaneously. This allows the automatic generation of subtitles in different languages ​​and multilingual editing to accommodate international audiences, making it possible to cater to a wider audience.

[0079] The editorial department can use the emotion estimation function to monitor viewers' emotional reactions in real time and dynamically change the editing content based on those reactions. For example, the editorial department can use the emotion estimation function to monitor viewers' emotional reactions in real time and dynamically change the editing content based on that data. For example, humorous sound effects can be added to scenes where viewers are laughing. This allows for a more interactive viewing experience by monitoring viewers' emotional reactions in real time and dynamically changing the editing content based on those reactions.

[0080] The learning unit can learn the font or color of specific captions and the timing of sound effects. For example, the generation AI analyzes the emotions of the video uploader in real time and adds bright colors and light sound effects to scenes with positive emotions. For example, it inserts bright captions in scenes where people are smiling. This allows for more personalized editing by learning the font, color, and timing of specific captions.

[0081] The editing department can display captions and insert sound effects at specific times in the filmed footage. For example, the generative AI can learn the video editing styles of different genres, such as comedy, drama, and action, and automatically select captions and sound effects according to the genre. For example, humorous sound effects can be added to comedy. This allows for the display of captions and insertion of sound effects at specific times to enhance the visual and auditory effects.

[0082] The editorial department can add captions and sound effects to breaking news footage in real time. For example, the generative AI analyzes the video uploader's past editing history and learns changes in editing style. For example, it analyzes the patterns of captions and sound effects used in past videos. This allows for the rapid provision of information by adding captions and sound effects to breaking news footage in real time.

[0083] The learning unit can instruct the generation AI on an editing style that matches a specific theme or event, and edit based on that style. For example, the learning unit allows the generation AI to learn the editing styles of multiple video contributors and combine them to generate a new editing style. For example, it can combine the caption and sound effect patterns of different contributors. This allows for more diverse editing by instructing the AI ​​on an editing style that matches a specific theme or event.

[0084] The learning unit can estimate the emotions of the video uploader and optimize the editing style based on those emotions. For example, the learning unit's generative AI analyzes the emotions of the video uploader in real time and adds bright colors and cheerful sound effects to scenes with positive emotions. For example, it inserts cheerful captions in scenes where the person is smiling. This allows the editing style to be optimized based on the emotions of the video uploader, making it possible to create more emotionally appealing edits.

[0085] The learning unit can learn video editing styles for different genres and automatically select the optimal editing style for each genre. For example, the generative AI can learn video editing styles for different genres, such as comedy, drama, and action, and automatically select subtitles and sound effects according to the genre. For example, humorous sound effects are added to comedy. This allows the system to learn video editing styles for different genres and provide the optimal editing style for each genre, enabling more diverse editing.

[0086] The learning unit analyzes the video uploader's past editing history and learns the evolution and transition of editing style, thereby enabling the provision of more advanced editing styles. For example, the learning unit uses a generative AI to analyze the video uploader's past editing history and learn the transition of editing style. For example, it analyzes the patterns of subtitles and sound effects used in past videos. This allows the system to analyze past editing history and learn the evolution and transition of editing style, thereby enabling the provision of more advanced editing styles.

[0087] The learning unit can learn the editing styles of other video contributors and combine different styles to generate new editing styles. For example, the learning unit's generation AI learns the editing styles of multiple video contributors and combines them to generate new editing styles. For example, it combines the caption and sound effect patterns of different contributors. This allows it to learn the editing styles of other video contributors and combine different styles to provide new editing styles.

[0088] The learning unit can learn from audio data and subtitle data and provide a comprehensive editing style that includes voice recognition and subtitle generation. For example, the learning unit uses a generation AI to analyze audio data and automatically generate subtitles using voice recognition technology. For example, conversations in a video are converted into text and displayed as subtitles. This allows the system to learn from audio data and subtitle data and provide a comprehensive editing style that includes voice recognition and subtitle generation.

[0089] The learning unit can use the emotion estimation function to analyze the emotional reactions of video viewers and adjust the editing style in real time based on that feedback. For example, the learning unit can use the emotion estimation function to analyze the emotional reactions of video viewers in real time and adjust the editing style based on that data. For example, humorous sound effects can be added to scenes in which the viewer is laughing. This allows the emotion estimation function to analyze the emotional reactions of viewers and adjust the editing style in real time based on that feedback.

[0090] The editing department can estimate the emotions of the video uploader in real time and automatically insert captions and sound effects that correspond to the emotions. For example, the editing department uses a generative AI to analyze the emotions of the video uploader in real time and automatically insert captions and sound effects that correspond to the emotions. For example, cheerful captions can be added to scenes where the video uploader is smiling. This allows for editing that appeals to more emotions by automatically inserting captions and sound effects that correspond to the emotions of the video uploader.

[0091] The editing department can analyze the content of the video and perform optimal editing for each scene. For example, generative AI can analyze the content of the video and perform optimal editing for each scene. For example, dynamic sound effects can be added to action scenes. This allows for more effective editing by analyzing the content of the video and performing optimal editing for each scene.

[0092] The editing department can analyze multiple camera footage simultaneously and automatically select the optimal camera angle for editing. For example, the generative AI can analyze multiple camera footage simultaneously and automatically select the optimal camera angle for editing. For example, close-ups can be used for important scenes. This allows for more effective visual expression by analyzing multiple camera footage simultaneously and selecting the optimal camera angle for editing.

[0093] The editorial department can edit live streaming video in real time and provide the edited video to viewers instantly. For example, the editorial department will build a system in which generative AI analyzes live streaming video in real time and performs editing in real time. For example, adding subtitles and sound effects during the live broadcast. This allows live streaming video to be edited in real time and the edited video to be provided to viewers in real time, enabling a more interactive viewing experience.

[0094] The editorial department can automatically generate subtitles in different languages ​​and perform multilingual editing to accommodate international audiences. For example, the editorial department uses generation AI to automatically generate subtitles in different languages ​​using speech recognition technology. For example, subtitles in English, French, Chinese, etc. can be displayed simultaneously. This allows the automatic generation of subtitles in different languages ​​and multilingual editing to accommodate international audiences, making it possible to cater to a wider audience.

[0095] The editorial department can use the emotion estimation function to monitor viewers' emotional reactions in real time and dynamically change the editing content based on those reactions. For example, the editorial department can use the emotion estimation function to monitor viewers' emotional reactions in real time and dynamically change the editing content based on that data. For example, humorous sound effects can be added to scenes where viewers are laughing. This allows for a more interactive viewing experience by monitoring viewers' emotional reactions in real time and dynamically changing the editing content based on those reactions.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The video editing system also includes a speech recognition unit. This unit analyzes the audio in the video and can automatically generate subtitles based on specific keywords and phrases. For example, in an interview video, answers to the interviewer's questions can be displayed as subtitles in real time. The speech recognition unit can also analyze audio in different languages ​​and generate subtitles in multiple languages. This makes it possible to provide information effectively to international audiences.

[0098] The video editing system further includes a scene analysis unit. The scene analysis unit analyzes scenes in the video and can select the optimal editing style for each scene. For example, dynamic sound effects can be added to action scenes, while calm music can be inserted in romantic scenes. The scene analysis unit can also recognize specific objects in the video and display information related to the object as subtitles. This enhances the visual and auditory effects.

[0099] The video editing system further includes a user feedback unit. The user feedback unit collects feedback from viewers and can optimize the editing style based on that data. For example, if a viewer gives a high rating to a particular scene, the editing style for that scene can be applied to other videos. The user feedback unit can also analyze viewer comments and ratings in real time and dynamically change the editing content. This allows for flexible editing that meets the needs of viewers.

[0100] The video editing system further includes an automatic transition unit. The automatic transition unit can automatically generate transition effects between scenes. For example, it can add transition effects such as fade-in, fade-out, and slide when switching scenes. The automatic transition unit can also select the optimal transition effect depending on the content of the scene. This makes the video flow smoother and enhances its visual appeal.

[0101] The video editing system further includes a background music generation unit. The background music generation unit can automatically generate background music according to the content and atmosphere of the video. For example, it can add moving music to moving scenes and tense music to tense scenes. The background music generation unit can also analyze the viewer's emotional response and dynamically change the background music based on that data. This can enhance the emotional impact of the video.

[0102] The video editing system also includes a viewer emotion analysis unit. This unit analyzes the viewer's emotional reactions in real time and can optimize the editing content based on that data. For example, it can add emotional subtitles to scenes where the viewer is moved, and add humorous sound effects to scenes where the viewer is laughing. The viewer emotion analysis unit can also accumulate viewer emotion data and use it in future editing. This allows editing that appeals to the viewer's emotions.

[0103] The video editing system further includes an emotion feedback unit. The emotion feedback unit analyzes the emotions of the video uploader in real time and can optimize the editing style based on that data. For example, energetic sound effects can be added to scenes in which the video uploader is excited, and calm music can be inserted to scenes in which the video uploader is calm. The emotion feedback unit can also accumulate the video uploader's emotion data and use it for future editing. This makes it possible to edit in accordance with the video uploader's emotions.

[0104] The video editing system also includes a viewer interaction unit, which collects real-time reactions from viewers and dynamically changes the editing content based on that data. For example, if a viewer posts a comment on a particular scene, the comment can be displayed as a caption. The viewer interaction unit can also change the editing of a scene based on the results of viewer votes. This allows for interactive communication with viewers.

[0105] The video editing system further includes an emotion prediction unit. The emotion prediction unit can analyze the past emotion data of a video poster and predict future emotions. For example, it can predict the emotion the video poster will feel in a particular scene and set an editing style in advance based on the prediction. The emotion prediction unit can also analyze the past emotion data of viewers and predict their emotional reactions. This allows for more effective emotion-based editing.

[0106] The video editing system also includes an automatic subtitling unit, which can analyze the audio in a video and automatically generate subtitles. For example, in an interview video, the conversation between the interviewer and interviewee can be converted into text in real time and displayed as subtitles. The automatic subtitling unit can also analyze audio in different languages ​​and generate subtitles in multiple languages, making it possible to provide information effectively to international audiences.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The learning module learns the editing style of the video uploader. For example, the learning module studies the video uploader's past editing work and understands their style. The learning module learns the font and color of specific subtitles, the timing of sound effects, etc. Step 2: The editing department automatically edits the raw footage shot by the video uploader based on the editing style learned by the learning department. For example, the editing department displays captions and inserts sound effects at specific times in the shot footage. The editing department can also add captions and sound effects in real time to breaking news footage.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] 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.

[0121] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] 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.

[0135] 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.

[0136] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] 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.

[0151] 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.

[0152] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0153] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] 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.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0176] 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. Equipped with generative AI, The generated AI is A learning department that learns the editing styles of video uploaders, an editing unit that automatically edits raw video shot by a video poster based on the editing style learned by the learning unit. A system characterized by:

2. The learning unit Learn the font or color of a specific text or the timing of sound effects 2. The system of claim 1.

3. The editorial department Displaying captions and inserting sound effects at specific times in the filmed footage 2. The system of claim 1.

4. The learning unit The editing style suited to a specific theme or event is instructed to the generating AI, and editing is performed based on that style.

2. The system of claim 1.

5. The learning unit It learns audio data and subtitle data and provides a comprehensive editing style including speech recognition and subtitle generation.

2. The system of claim 1.

6. The editorial department The emotions of the video poster are estimated in real time, and subtitles and sound effects corresponding to the emotions are automatically inserted.

2. The system of claim 1.

7. The editorial department Analyzes the content of the video and performs the optimal editing for each scene 2. The system of claim 1.

8. The editorial department Edit live streaming footage in real time and instantly provide edited footage to viewers 2. The system of claim 1.

Images