System

Abstract

An object of a system according to an embodiment is to automatically edit and utilize a moving image based on a condition designated by a user.SOLUTION: A system according to an embodiment includes a moving image upload unit, a condition designation unit, an analysis unit, an editing unit, and a provision unit. The moving image upload unit uploads a moving image from a user. The condition designation unit designates an editing condition for the moving image uploaded by the moving image upload unit. The analysis unit analyzes the moving image on the basis of the condition designated by the condition designation unit. The editing unit edits the moving image on the basis of a result analyzed by the analysis unit. The provision section provides the user with the video edited by the editing section.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 footage often goes unused and is buried, and video editing is a time-consuming process.

[0005] The system according to the embodiment aims to automatically edit and utilize videos based on conditions specified by a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a video uploading unit, a condition specification unit, an analysis unit, an editing unit, and a providing unit. The video uploading unit uploads videos from a user. The condition specification unit specifies editing conditions for the videos uploaded by the video uploading unit. The analysis unit analyzes the videos based on the conditions specified by the condition specification unit. The editing unit edits the videos based on the results of the analysis by the analysis unit. The providing unit provides the videos edited by the editing unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can automatically edit and utilize videos based on conditions specified by a user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A video editing system according to an embodiment of the present invention is a system that automatically edits videos based on conditions specified by a user to create attractive videos. As a result, the video editing system can automatically edit shot videos and provide them to users.

[0029] A video editing system according to an embodiment includes a video uploading unit, a condition specifying unit, an analysis unit, an editing unit, and a providing unit. The video uploading unit uploads videos from a user. For example, the user can upload videos he or she has shot to the system. The video uploading unit also supports multiple video file formats (e.g., MP4, AVI, MOV, etc.). The condition specifying unit specifies editing conditions for videos uploaded by the video uploading unit. For example, a user can specify conditions such as "I want the editing to focus on my child's smiling faces" or "I want a collection of cute moments of my pet." The analysis unit analyzes the video based on the conditions specified by the condition specifying unit. For example, the generation AI detects smiling scenes and pet movements in the video. The editing unit edits the video based on the results of the analysis by the analysis unit. For example, the generation AI stitches together the detected scenes to create a single video. The providing unit provides the video edited by the editing unit to a user. For example, the user can download the edited video or share it via social media or a messaging app. As a result, the video editing system according to an embodiment can automatically edit videos based on the conditions specified by the user to create an appealing video.

[0030] The condition specification unit can detect specific scenes based on user-specified conditions and connect and edit them. For example, when a user uploads a video, the generation AI automatically analyzes the video's content and suggests optimal editing conditions. For example, the generation AI automatically analyzes the video's content and detects scenes with smiling faces and high levels of movement. Based on these analysis results, the generation AI then suggests editing conditions to the user, such as "editing to focus on smiling faces" or "emphasizing scenes with high levels of movement." The generation AI also analyzes the audio and background sounds in the video and automatically suggests editing conditions, such as "adding music" or "removing noise." This allows users to easily create higher-quality videos. Furthermore, the generation AI analyzes the emotions in each scene in the video and suggests editing conditions, such as "emphasizing moving scenes" or "collecting fun scenes." This allows the atmosphere of the video to be edited to match the user's intentions. This allows specific scenes to be edited based on user-specified conditions.

[0031] The analysis unit can analyze the audio in the video and perform editing based on specific keywords and phrases. For example, the generation AI analyzes the audio in the video and performs editing based on specific keywords and phrases. For example, the generation AI analyzes the audio in the video and detects specific keywords and phrases such as "laughter" or "applause." Then, based on these keywords and phrases, it performs editing to emphasize scenes of laughter. The generation AI also analyzes the audio data and detects specific phrases such as "happy birthday." Then, based on this phrase, it performs editing to emphasize birthday scenes. Furthermore, the generation AI analyzes the audio in the video and detects words of gratitude such as "thank you." Then, based on this keyword, it performs editing to emphasize scenes of gratitude. This allows editing to be performed based on the audio in the video.

[0032] The providing unit can output the edited video in multiple formats to support playback on different devices and platforms. For example, the generating AI outputs the edited video in multiple formats to support playback on different devices and platforms. For example, the generating AI saves the edited video in different formats, such as "MP4 format" or "AVI format." This allows users to play the video on various devices. The generating AI also outputs the edited video in different resolutions. For example, by saving it in different resolutions, such as "HD resolution" or "4K resolution," users can enjoy the video in the optimal image quality for their device. Furthermore, the generating AI outputs the edited video in formats compatible with different platforms. For example, it saves it in a format optimized for a specific platform, such as "YouTube format" or "Instagram format." This allows the edited video to be played on various devices and platforms.

[0033] The providing unit can automatically generate thumbnail images for edited videos and suggest optimal thumbnails. For example, the providing unit uses a generation AI to automatically generate thumbnail images from edited videos and suggest optimal thumbnails. For example, the generation AI automatically generates thumbnail images from edited videos and suggests "smiling scenes" or "moving scenes" as thumbnails. This allows users to easily select attractive thumbnails. The generation AI also automatically generates multiple thumbnail images from edited videos and suggests the "most visually appealing thumbnail" to users. For example, it selects "scenes with vivid colors" or "scenes with movement" as thumbnails. Furthermore, the generation AI automatically generates thumbnail images from edited videos and suggests "highlight scenes of the video" as thumbnails. This allows users to select thumbnails that maximize the appeal of the video. This allows optimal thumbnails to be suggested for edited videos.

[0034] The providing unit can automatically save the edited video in cloud storage, making it accessible from multiple devices. For example, the providing unit allows the generation AI to automatically save the edited video in cloud storage, making it accessible from multiple devices. For example, the generation AI automatically saves the edited video in cloud storage, allowing the user to access it from multiple devices, such as a smartphone, tablet, or PC. This simplifies video management. Furthermore, by saving the edited video in cloud storage, the user can access the video from anywhere with an internet connection. For example, the user can watch the video while traveling or on the go. Furthermore, the generation AI automatically saves the edited video in cloud storage, allowing the user to easily share the video with family and friends by simply sending a cloud link. This allows the edited video to be saved in cloud storage and accessed from multiple devices.

[0035] The editing department can add appropriate music and effects based on the theme and style selected by the user. For example, the generation AI automatically adds appropriate music based on the theme selected by the user. For example, if the user selects an "emotional movie" theme, the generation AI automatically adds emotional music. Furthermore, during editing, the generation AI automatically adds appropriate effects based on the style selected by the user. For example, if the user selects a "retro" style, the generation AI automatically adds a sepia-toned filter or an old-movie effect. Furthermore, the generation AI edits the video based on the theme and style selected by the user. For example, if the user selects a "birthday party" theme, the generation AI automatically adds bright music and colorful effects. This allows appropriate music and effects to be added based on the theme and style selected by the user.

[0036] The providing unit can add a function that allows edited videos to be easily shared not only on social media and messaging apps, but also on email and blogs. For example, the providing unit adds a function that allows the generation AI to easily share edited videos not only on social media and messaging apps, but also on email and blogs. For example, the providing unit provides a function that allows the generation AI to easily share edited videos not only on social media and messaging apps, but also on email and blogs. For example, the providing unit provides a function that allows the generation AI to automatically generate embedding codes for blogs for edited videos, allowing users to easily embed videos in blogs. Furthermore, the generating AI enables the generation AI to easily share edited videos not only on social media and messaging apps, but also on email and blogs. For example, the providing unit provides a function that automatically generates email templates that include video thumbnails and links. This allows edited videos to be easily shared on various platforms.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The video editing system may further include an eye-tracking unit that tracks the user's gaze. The eye-tracking unit can analyze the movement of the user's gaze while watching a video and identify scenes that the user is particularly paying attention to. For example, if the user focuses their gaze on a particular character or object, editing can be performed to emphasize that scene. The eye-tracking unit can also detect scenes in which the user fixates their gaze for a long period of time and edit those scenes as highlights. Furthermore, the eye-tracking unit can detect scenes in which the user frequently moves their gaze and cut those scenes, thereby maintaining the viewer's concentration.

[0039] The video editing system may further include a voice recognition unit. The voice recognition unit can analyze the audio in the video and perform editing based on specific keywords or phrases. For example, the voice recognition unit can detect specific keywords such as "laughter" or "applause" from the audio in the video and perform editing to emphasize those scenes. The voice recognition unit can also detect specific phrases such as "happy birthday" from the audio in the video and perform editing to emphasize those scenes. Furthermore, the voice recognition unit can detect words of gratitude such as "thank you" from the audio in the video and perform editing to emphasize those scenes.

[0040] The video editing system may further include a viewing environment analysis unit that analyzes the user's viewing environment and performs optimal editing. The viewing environment analysis unit analyzes the environment in which the user is viewing a video (e.g., brightness and volume) and can perform editing that suits that environment. For example, if the user is viewing in a bright environment, the viewing environment analysis unit can perform editing that emphasizes bright scenes. Also, if the user is viewing in a quiet environment, the viewing environment analysis unit can perform editing that emphasizes quiet scenes. Furthermore, if the user is viewing in a noisy environment, the viewing environment analysis unit can perform editing that adjusts the volume.

[0041] The video editing system may further include a viewing environment analysis unit that analyzes the user's viewing environment and suggests optimal music and effects. The viewing environment analysis unit may analyze the environment (e.g., brightness and volume) in which the user is viewing a video and suggest music and effects that match that environment. For example, the viewing environment analysis unit may suggest bright music if the user is viewing in a bright environment. The viewing environment analysis unit may also suggest quiet music if the user is viewing in a quiet environment. Furthermore, the viewing environment analysis unit may suggest effects to adjust the volume if the user is viewing in a noisy environment.

[0042] The video editing system may further include a viewing environment analysis unit that analyzes the user's viewing environment and suggests optimal text and captions. The viewing environment analysis unit may analyze the environment (e.g., brightness and volume) in which the user is viewing a video and suggest text and captions that match the environment. For example, the viewing environment analysis unit may suggest bright text and captions if the user is viewing in a bright environment. The viewing environment analysis unit may also suggest quiet text and captions if the user is viewing in a quiet environment. Furthermore, the viewing environment analysis unit may suggest text and captions that adjust the volume if the user is viewing in a noisy environment.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The video uploader allows users to upload videos. For example, users can upload videos they have taken to the system. The video uploader also supports multiple video file formats (e.g., MP4, AVI, MOV, etc.). Step 2: The condition specification unit specifies editing conditions for the video uploaded by the video upload unit. For example, the user can specify conditions such as "I want the video to be edited with a focus on smiling children" or "I want a collection of cute moments of pets." Step 3: The analysis unit analyzes the video based on the conditions specified by the condition specification unit. For example, the generation AI detects smiling scenes and pet movements in the video. Step 4: The editing department edits the video based on the results of the analysis by the analysis department. For example, the generation AI stitches together the detected scenes to create a single video. Step 5: The providing unit provides the video edited by the editing unit to the user. For example, the user can download the edited video or share it via social media or a messaging app.

[0045] (Example 2) A video editing system according to an embodiment of the present invention is a system that automatically edits videos based on conditions specified by a user to create attractive videos. As a result, the video editing system can automatically edit shot videos and provide them to users.

[0046] A video editing system according to an embodiment includes a video uploading unit, a condition specifying unit, an analysis unit, an editing unit, and a providing unit. The video uploading unit uploads videos from a user. For example, the user can upload videos he or she has shot to the system. The video uploading unit also supports multiple video file formats (e.g., MP4, AVI, MOV, etc.). The condition specifying unit specifies editing conditions for videos uploaded by the video uploading unit. For example, a user can specify conditions such as "I want the editing to focus on my child's smiling faces" or "I want a collection of cute moments of my pet." The analysis unit analyzes the video based on the conditions specified by the condition specifying unit. For example, the generation AI detects smiling scenes and pet movements in the video. The editing unit edits the video based on the results of the analysis by the analysis unit. For example, the generation AI stitches together the detected scenes to create a single video. The providing unit provides the video edited by the editing unit to a user. For example, the user can download the edited video or share it via social media or a messaging app. As a result, the video editing system according to an embodiment can automatically edit videos based on the conditions specified by the user to create an appealing video.

[0047] The condition specification unit can detect specific scenes based on user-specified conditions and connect and edit them. For example, when a user uploads a video, the generation AI automatically analyzes the video's content and suggests optimal editing conditions. For example, the generation AI automatically analyzes the video's content and detects scenes with smiling faces and high levels of movement. Based on these analysis results, the generation AI then suggests editing conditions to the user, such as "editing to focus on smiling faces" or "emphasizing scenes with high levels of movement." The generation AI also analyzes the audio and background sounds in the video and automatically suggests editing conditions, such as "adding music" or "removing noise." This allows users to easily create higher-quality videos. Furthermore, the generation AI analyzes the emotions in each scene in the video and suggests editing conditions, such as "emphasizing moving scenes" or "collecting fun scenes." This allows the atmosphere of the video to be edited to match the user's intentions. This allows specific scenes to be edited based on user-specified conditions.

[0048] The analysis unit can analyze the audio in the video and perform editing based on specific keywords and phrases. For example, the generation AI analyzes the audio in the video and performs editing based on specific keywords and phrases. For example, the generation AI analyzes the audio in the video and detects specific keywords and phrases such as "laughter" or "applause." Then, based on these keywords and phrases, it performs editing to emphasize scenes of laughter. The generation AI also analyzes the audio data and detects specific phrases such as "happy birthday." Then, based on this phrase, it performs editing to emphasize birthday scenes. Furthermore, the generation AI analyzes the audio in the video and detects words of gratitude such as "thank you." Then, based on this keyword, it performs editing to emphasize scenes of gratitude. This allows editing to be performed based on the audio in the video.

[0049] The analysis unit can analyze the emotions of people in the video and perform editing based on changes in emotions. For example, the analysis unit uses a generation AI to analyze the emotions of people in the video and perform editing based on changes in emotions. For example, the generation AI analyzes the emotions of people in the video and detects changes in emotions such as "smiles" and "tears." Then, based on these changes in emotions, editing is performed to emphasize smiling scenes. The generation AI also analyzes people's emotions in real time and detects changes in emotions such as "surprise" and "joy." Then, based on these changes in emotions, editing is performed to emphasize surprising scenes. The generation AI also analyzes people's emotions in the video and detects changes in emotions such as "sadness" and "emotion." Then, based on these changes in emotions, editing is performed to emphasize moving scenes. This allows editing based on the emotions of people in the video.

[0050] The providing unit can output the edited video in multiple formats to support playback on different devices and platforms. For example, the generating AI outputs the edited video in multiple formats to support playback on different devices and platforms. For example, the generating AI saves the edited video in different formats, such as "MP4 format" or "AVI format." This allows users to play the video on various devices. The generating AI also outputs the edited video in different resolutions. For example, by saving it in different resolutions, such as "HD resolution" or "4K resolution," users can enjoy the video in the optimal image quality for their device. Furthermore, the generating AI outputs the edited video in formats compatible with different platforms. For example, it saves it in a format optimized for a specific platform, such as "YouTube format" or "Instagram format." This allows the edited video to be played on various devices and platforms.

[0051] The providing unit can automatically generate thumbnail images for edited videos and suggest optimal thumbnails. For example, the providing unit uses a generation AI to automatically generate thumbnail images from edited videos and suggest optimal thumbnails. For example, the generation AI automatically generates thumbnail images from edited videos and suggests "smiling scenes" or "moving scenes" as thumbnails. This allows users to easily select attractive thumbnails. The generation AI also automatically generates multiple thumbnail images from edited videos and suggests the "most visually appealing thumbnail" to users. For example, it selects "scenes with vivid colors" or "scenes with movement" as thumbnails. Furthermore, the generation AI automatically generates thumbnail images from edited videos and suggests "highlight scenes of the video" as thumbnails. This allows users to select thumbnails that maximize the appeal of the video. This allows optimal thumbnails to be suggested for edited videos.

[0052] The providing unit can automatically save the edited video in cloud storage, making it accessible from multiple devices. For example, the providing unit allows the generation AI to automatically save the edited video in cloud storage, making it accessible from multiple devices. For example, the generation AI automatically saves the edited video in cloud storage, allowing the user to access it from multiple devices, such as a smartphone, tablet, or PC. This simplifies video management. Furthermore, by saving the edited video in cloud storage, the user can access the video from anywhere with an internet connection. For example, the user can watch the video while traveling or on the go. Furthermore, the generation AI automatically saves the edited video in cloud storage, allowing the user to easily share the video with family and friends by simply sending a cloud link. This allows the edited video to be saved in cloud storage and accessed from multiple devices.

[0053] The editing department can add appropriate music and effects based on the theme and style selected by the user. For example, the generation AI automatically adds appropriate music based on the theme selected by the user. For example, if the user selects an "emotional movie" theme, the generation AI automatically adds emotional music. Furthermore, during editing, the generation AI automatically adds appropriate effects based on the style selected by the user. For example, if the user selects a "retro" style, the generation AI automatically adds a sepia-toned filter or an old-movie effect. Furthermore, the generation AI edits the video based on the theme and style selected by the user. For example, if the user selects a "birthday party" theme, the generation AI automatically adds bright music and colorful effects. This allows appropriate music and effects to be added based on the theme and style selected by the user.

[0054] The editorial department can use the emotion estimation function to suggest music and effects based on the user's emotions. For example, the editorial department has the generation AI analyze the user's emotions and suggest music and effects based on the emotions. For example, the generation AI analyzes the user's emotions and suggests music and effects based on the emotions, such as "if the user is moved, we suggest moving music." This enhances the emotional atmosphere of the video. Furthermore, during editing, the generation AI analyzes the user's emotions in real time and suggests music and effects based on the emotions, such as "if the user is watching with a smile, we suggest happy music." Furthermore, the generation AI analyzes the user's emotions and suggests music and effects based on the emotions, such as "if the user is excited, we suggest energetic music." This increases the emotional impact of the video. This allows music and effects to be suggested based on the user's emotions.

[0055] The editorial department can use the emotion estimation function to add text and captions based on the user's emotions. For example, the editorial department uses the generation AI to analyze the user's emotions and add text and captions based on the emotions. For example, the generation AI analyzes the user's emotions and automatically adds text and captions that match the emotion, such as "If the user is moved, add an inspiring message." This enhances the emotional atmosphere of the video. Furthermore, during editing, the generation AI analyzes the user's emotions in real time and automatically adds text and captions that match the emotion, such as "If the user is smiling, add a cheerful message." Furthermore, the generation AI analyzes the user's emotions and automatically adds text and captions that match the emotion, such as "If the user is excited, add an energetic message." This enhances the emotional impact of the video. This allows text and captions to be added based on the user's emotions.

[0056] The providing unit can add a function that allows edited videos to be easily shared not only on social media and messaging apps, but also on email and blogs. For example, the providing unit adds a function that allows the generation AI to easily share edited videos not only on social media and messaging apps, but also on email and blogs. For example, the providing unit provides a function that allows the generation AI to easily share edited videos not only on social media and messaging apps, but also on email and blogs. For example, the providing unit provides a function that allows the generation AI to automatically generate embedding codes for blogs for edited videos, allowing users to easily embed videos in blogs. Furthermore, the generating AI enables the generation AI to easily share edited videos not only on social media and messaging apps, but also on email and blogs. For example, the providing unit provides a function that automatically generates email templates that include video thumbnails and links. This allows edited videos to be easily shared on various platforms.

[0057] The providing unit can use the emotion estimation function to display the emotions of the user when watching the edited video in real time, thereby providing an interface that elicits positive emotions. For example, the providing unit uses a generation AI to analyze the user's emotions in real time and provide an interface that elicits positive emotions. For example, the generation AI analyzes the user's emotions in real time and provides an interface such as "displaying a positive message if the user is smiling while watching." This improves the user's viewing experience. Furthermore, the generation AI analyzes the user's emotions in real time when watching the edited video and displays the results on the screen. For example, a function such as "displaying an inspiring message if the user is moved" is added. Furthermore, the generation AI analyzes the user's emotions in real time and provides an interface such as "displaying a supportive message if the user is excited." This makes the user's viewing experience more positive. This makes it possible to provide an interface that displays the user's emotions in real time and elicits positive emotions.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The video editing system may further include an eye-tracking unit that tracks the user's gaze. The eye-tracking unit can analyze the movement of the user's gaze while watching a video and identify scenes that the user is particularly paying attention to. For example, if the user focuses their gaze on a particular character or object, editing can be performed to emphasize that scene. The eye-tracking unit can also detect scenes in which the user fixates their gaze for a long period of time and edit those scenes as highlights. Furthermore, the eye-tracking unit can detect scenes in which the user frequently moves their gaze and cut those scenes, thereby maintaining the viewer's concentration.

[0060] The video editing system may further include a voice recognition unit. The voice recognition unit can analyze the audio in the video and perform editing based on specific keywords or phrases. For example, the voice recognition unit can detect specific keywords such as "laughter" or "applause" from the audio in the video and perform editing to emphasize those scenes. The voice recognition unit can also detect specific phrases such as "happy birthday" from the audio in the video and perform editing to emphasize those scenes. Furthermore, the voice recognition unit can detect words of gratitude such as "thank you" from the audio in the video and perform editing to emphasize those scenes.

[0061] The video editing system may further include an emotion estimation unit that estimates the user's emotion and performs editing based on the estimated emotion. The emotion estimation unit can estimate the emotion by analyzing the user's facial expression and voice while watching a video. For example, if the user is smiling while watching, the emotion estimation unit can perform editing to emphasize that scene. Furthermore, if the user is moved, the emotion estimation unit can perform editing to emphasize that scene. Furthermore, if the user is excited, the emotion estimation unit can perform editing to emphasize that scene.

[0062] The video editing system may further include a viewing history analysis unit that analyzes a user's viewing history and performs editing based on the tendencies of videos viewed in the past. The viewing history analysis unit analyzes the genres and contents of videos viewed in the past by the user and can perform editing that matches the user's preferences. For example, the viewing history analysis unit can detect scenes that the user particularly enjoyed from videos viewed in the past and perform editing that emphasizes those scenes. The viewing history analysis unit can also detect scenes that the user found particularly moving from videos viewed in the past and perform editing that emphasizes those scenes. Furthermore, the viewing history analysis unit can detect scenes that the user found particularly exciting from videos viewed in the past and perform editing that emphasizes those scenes.

[0063] The video editing system may further include an emotion estimation unit that estimates the user's emotion and suggests music and effects based on the estimated emotion. The emotion estimation unit can estimate the emotion by analyzing the user's facial expression and voice while watching a video. For example, the emotion estimation unit can suggest emotional music if the user is moved. Furthermore, the emotion estimation unit can suggest fun music if the user is smiling while watching. Furthermore, the emotion estimation unit can suggest energetic music if the user is excited.

[0064] The video editing system may further include a viewing environment analysis unit that analyzes the user's viewing environment and performs optimal editing. The viewing environment analysis unit analyzes the environment in which the user is viewing a video (e.g., brightness and volume) and can perform editing that suits that environment. For example, if the user is viewing in a bright environment, the viewing environment analysis unit can perform editing that emphasizes bright scenes. Also, if the user is viewing in a quiet environment, the viewing environment analysis unit can perform editing that emphasizes quiet scenes. Furthermore, if the user is viewing in a noisy environment, the viewing environment analysis unit can perform editing that adjusts the volume.

[0065] The video editing system may further include a viewing history analysis unit that analyzes a user's viewing history and suggests music and effects based on the trends of videos viewed in the past. The viewing history analysis unit may analyze the genres and contents of videos viewed in the past by the user and suggest music and effects that match the user's preferences. For example, the viewing history analysis unit may detect music that the user particularly liked among videos viewed in the past and suggest that music. The viewing history analysis unit may also detect effects that the user particularly liked among videos viewed in the past and suggest that effect. Furthermore, the viewing history analysis unit may detect scenes that the user particularly enjoyed among videos viewed in the past and suggest music and effects that match that scene.

[0066] The video editing system may further include a viewing environment analysis unit that analyzes the user's viewing environment and suggests optimal music and effects. The viewing environment analysis unit may analyze the environment (e.g., brightness and volume) in which the user is viewing a video and suggest music and effects that match that environment. For example, the viewing environment analysis unit may suggest bright music if the user is viewing in a bright environment. The viewing environment analysis unit may also suggest quiet music if the user is viewing in a quiet environment. Furthermore, the viewing environment analysis unit may suggest effects to adjust the volume if the user is viewing in a noisy environment.

[0067] The video editing system may further include a viewing history analysis unit that analyzes a user's viewing history and suggests text or captions based on the trends of videos viewed in the past. The viewing history analysis unit can analyze the genres and contents of videos viewed in the past by the user and suggest text or captions that match the user's preferences. For example, the viewing history analysis unit can detect text or captions that the user particularly liked in videos viewed in the past and suggest those text or captions. The viewing history analysis unit can also detect particularly moving scenes in videos viewed in the past by the user and suggest text or captions that match those scenes. Furthermore, the viewing history analysis unit can detect particularly exciting scenes in videos viewed in the past by the user and suggest text or captions that match those scenes.

[0068] The video editing system may further include a viewing environment analysis unit that analyzes the user's viewing environment and suggests optimal text and captions. The viewing environment analysis unit may analyze the environment (e.g., brightness and volume) in which the user is viewing a video and suggest text and captions that match the environment. For example, the viewing environment analysis unit may suggest bright text and captions if the user is viewing in a bright environment. The viewing environment analysis unit may also suggest quiet text and captions if the user is viewing in a quiet environment. Furthermore, the viewing environment analysis unit may suggest text and captions that adjust the volume if the user is viewing in a noisy environment.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The video uploader allows users to upload videos. For example, users can upload videos they have taken to the system. The video uploader also supports multiple video file formats (e.g., MP4, AVI, MOV, etc.). Step 2: The condition specification unit specifies editing conditions for the video uploaded by the video upload unit. For example, the user can specify conditions such as "I want the video to be edited with a focus on smiling children" or "I want a collection of cute moments of pets." Step 3: The analysis unit analyzes the video based on the conditions specified by the condition specification unit. For example, the generation AI detects smiling scenes and pet movements in the video. Step 4: The editing department edits the video based on the results of the analysis by the analysis department. For example, the generation AI stitches together the detected scenes to create a single video. Step 5: The providing unit provides the video edited by the editing unit to the user. For example, the user can download the edited video or share it via social media or a messaging app.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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]

[0138] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a video upload unit for uploading videos from users; a condition specification unit that specifies editing conditions for the video uploaded by the video upload unit; an analysis unit that analyzes the video based on the conditions specified by the condition specification unit; an editing unit that edits the video based on the results of the analysis by the analysis unit; a providing unit that provides the video edited by the editing unit to a user. A system characterized by:

2. The condition specification unit Detect specific scenes based on user-specified criteria and stitch them together for editing 2. The system of claim 1.

3. The analysis unit Analyzes audio in your videos and makes edits based on specific keywords and phrases 2. The system of claim 1.

4. The analysis unit Analyze the emotions of people in the video and edit based on changes in emotions 2. The system of claim 1.

5. The providing unit Output your edited video in multiple formats for playback on different devices and platforms 2. The system of claim 1.

Images