System

Abstract

An object of a system according to an embodiment is to generate video content that can be viewed from any angle.SOLUTION: A system includes a video reading unit, an analysis unit, and a generation unit. The video reading unit reads a video. The analysis unit analyzes the video read by the video reading unit. The generation part generates a content viewable from an arbitrary angle on the basis of the data analyzed by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that the viewing angle of video content is limited, and viewers cannot enjoy it from any angle.

[0005] The system according to the embodiment aims to generate video content that can be viewed from any angle. [Means for solving the problem]

[0006] The system according to the embodiment includes a video reading unit, an analysis unit, and a generation unit. The video reading unit reads video. The analysis unit analyzes the video read by the video reading unit. The generation unit generates content that can be viewed from any angle based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate video content that can be viewed from any angle. [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 viewing system according to an embodiment of the present invention is a system that converts video into content that can be viewed from any angle, thereby enabling the video viewing system to infinitely expand the viewing experience.

[0029] A video viewing system according to an embodiment includes a video reading unit, an analysis unit, and a generation unit. The video reading unit reads video. For example, the video reading unit reads video in digital format. It can also read live video in real time. It can also read 3D video. The analysis unit analyzes the video read by the video reading unit. For example, the analysis unit can identify the positions of people and objects in the video using image analysis technology. It can also analyze audio data in the video using audio analysis technology. It can also analyze movement in the video using data analysis technology. The generation unit generates content that can be viewed from any angle based on the data analyzed by the analysis unit. For example, the generation unit generates a 3D model to allow a user to view the content from any angle. The generation unit can also generate video in real time according to a user's desired viewing angle or distance by specifying the angle or distance. The generation unit can also enable a specific scene or performance to be viewed repeatedly. This allows the video viewing system according to an embodiment to convert video into content that can be viewed from any angle. For example, a user can view a concert or play video from various angles. Users can also watch their favorite idols in action from any angle in 360 degrees. Users can also replay and watch past live performance footage from various angles.

[0030] The analysis unit analyzes the audio data and can reflect the position of the sound source and the reverberation of the sound in the 3D model. For example, the analysis unit analyzes audio data in a video and identifies the position of the sound source. For example, the analysis unit identifies the positions of speakers and instruments on a stage and reflects the reverberation of the sound in the 3D model. The analysis unit also analyzes the audio data and reproduces the reverberation of the sound. For example, echo analysis technology is used to reproduce the reverberation of the sound. The analysis unit also analyzes the audio data and reproduces the reverberation of the sound. For example, reverberation analysis technology is used to reproduce the reverberation of the sound. In this way, the audio data can be analyzed and realistic sound effects can be reproduced.

[0031] The analysis unit analyzes light intensity and color temperature to reproduce realistic lighting effects. For example, the analysis unit analyzes the light intensity in a video and reflects it in a 3D model. For example, it reproduces the intensity of a spotlight on a stage. The analysis unit also analyzes color temperature to reproduce realistic lighting effects. For example, it measures color temperature using a color temperature meter and reproduces lighting effects based on that data. The analysis unit also combines light intensity and color temperature to reproduce realistic lighting effects. For example, it uses physically based rendering technology to perform light source simulation. This allows it to analyze light intensity and color temperature and reproduce realistic lighting effects.

[0032] When loading video, the analysis unit can prioritize analysis of specific scenes and people specified by the user. For example, the analysis unit prioritizes analysis of specific scenes specified by the user. For example, the climax scene of a concert may be prioritized and reflected in the 3D model. The analysis unit also prioritizes analysis of specific people specified by the user. For example, the analysis unit prioritizes analysis of an artist's performance and reflects it in the 3D model. The analysis unit also prioritizes analysis of specific scenes and people specified by the user and generates content based on that data. This allows the analysis of specific scenes and people specified by the user to be prioritized.

[0033] The analysis unit can provide a function for sharing the results of video analysis with other users and for collaboratively editing 3D models. For example, the analysis unit stores the results of video analysis on the cloud and shares them with other users. For example, multiple users can collaboratively edit the video of the same concert. The analysis unit also shares the analysis results in real time and collaboratively edits 3D models. For example, multiple users can perform editing work simultaneously. The analysis unit also provides a version management function and manages the editing history. This allows analysis results to be shared with other users and 3D models to be collaboratively edited.

[0034] The generation unit may provide a function that allows a user to apply custom effects and filters to the generated 3D model. For example, the generation unit allows a user to apply custom effects to the 3D model. For example, the generation unit may add a stage light effect or special effect. The generation unit may also allow a user to apply filters to the 3D model. For example, the generation unit may apply a blur filter or a sharpening filter. The generation unit may also allow a user to save the 3D model to which the custom effects and filters have been applied. This allows a user to apply custom effects and filters to the generated 3D model.

[0035] The generation unit uses the generation AI to automatically suggest the optimal viewing angle, providing the user with a new viewing experience. For example, the generation AI automatically suggests the optimal viewing angle. For example, it suggests the angle from which the artist's performance is best viewed. The generation unit also uses the generation AI to suggest the optimal viewing angle based on the user's viewing history. For example, it analyzes past viewing data and suggests the viewing angle that the user prefers. The generation unit also uses the generation AI to suggest the optimal viewing angle in real time. This allows the generation AI to automatically suggest the optimal viewing angle, providing a new viewing experience.

[0036] The generation unit can provide a function that allows the generated content to be viewed on a VR headset or an AR device. The generation unit, for example, makes the generated 3D content viewable on a VR headset. For example, it makes the content compatible with devices such as Oculus Rift and HTC Vive. The generation unit also makes the generated content viewable on an AR device. For example, it makes the content compatible with devices such as Microsoft HoloLens and Magic Leap. The generation unit also provides an interface for viewing on a VR headset or an AR device. This allows the generated content to be viewed on a VR headset or an AR device.

[0037] The generation unit can provide a function that allows multiple users to simultaneously watch the same content from different angles and exchange opinions in real time. The generation unit, for example, allows multiple users to simultaneously watch the same content from different angles. For example, watching a concert video with friends. The generation unit also provides a function that allows users to exchange opinions in real time. For example, a chat function or a voice call function is provided. The generation unit also provides a video call function that allows users to make a video call while watching. This allows multiple users to simultaneously watch the same content from different angles and exchange opinions in real time.

[0038] The generation unit may provide a function that allows a user to mark a specific scene while watching and easily re-watch the scene later. For example, the generation unit provides a function that allows a user to mark a specific scene while watching. For example, a scene is marked by pressing a button while watching. The generation unit also provides a function that allows a user to easily re-watch the marked scene later. For example, the marked scene is saved in a list and the scene is later selected from the list to re-watch. The generation unit also provides a function that allows a user to add a comment to the marked scene. This allows a user to mark a specific scene while watching and easily re-watch the scene later.

[0039] The generation unit can provide a function that allows the user to change the viewing angle or distance in real time using a voice command. The generation unit provides, for example, a function that allows the user to change the viewing angle using a voice command. For example, the generation unit recognizes commands such as "turn it to the right" or "show me from above." The generation unit also provides a function that allows the user to change the viewing distance using a voice command. For example, the generation unit recognizes commands such as "move closer" or "move further away." The generation unit also provides a function that allows the user to save viewing settings using a voice command. This allows the user to change the viewing angle or viewing distance in real time using a voice command.

[0040] The generation unit can provide a function that allows a user to share a viewing experience with other users and perform collaborative customization. The generation unit provides, for example, a function that allows a user to share a viewing experience with other users. For example, viewing settings and customization contents can be shared. The generation unit also provides a function that allows collaborative customization. For example, multiple users can perform customization work simultaneously. The generation unit also provides a function that allows a shared viewing experience to be saved. This allows a user to share a viewing experience with other users and perform collaborative customization.

[0041] The generation unit may provide a function for saving settings selected by a user when customizing the viewing experience and automatically applying them the next time the user views. The generation unit may provide, for example, a function for saving viewing settings selected by a user. For example, the generation unit may save settings such as viewing angles and effects. The generation unit may also provide a function for automatically applying the saved settings the next time the user views. For example, the generation unit may automatically apply settings based on a user profile. The generation unit may also provide a function for saving multiple settings and applying them according to the situation. This allows the user to save settings selected by the user and automatically apply them the next time the user views.

[0042] The generation unit can provide a function that analyzes video of past concerts or plays and recreates the audience reactions and atmosphere of the time. The generation unit, for example, analyzes video of past concerts or plays and recreates the audience reactions. For example, the sounds of cheers and applause are reflected in the 3D model. The generation unit also recreates the atmosphere within the video. For example, sound effects and lighting effects are recreated. The generation unit also recreates a combination of the audience reactions and the atmosphere. This makes it possible to analyze video of past concerts or plays and recreate the audience reactions and atmosphere of the time.

[0043] The generation unit uses the generation AI to automatically suggest the optimal viewing sequence, thereby providing the user with a new viewing experience. For example, the generation unit uses the generation AI to automatically suggest the optimal viewing sequence. For example, it may suggest a sequence that best shows the artist's performance. The generation unit also uses the generation AI to suggest the optimal viewing sequence based on the user's viewing history. For example, it may analyze past viewing data and suggest a sequence that the user prefers. The generation unit also uses the generation AI to suggest the optimal viewing sequence in real time. This allows the generation AI to automatically suggest the optimal viewing sequence, providing a new viewing experience.

[0044] The generation unit can provide a function that allows multiple users to simultaneously watch a re-created concert or play and exchange opinions in real time. The generation unit, for example, allows multiple users to simultaneously watch a re-created concert or play. For example, watching a past live video together with friends. The generation unit also provides a function that allows for the exchange of opinions in real time. For example, a chat function or a voice call function is provided. The generation unit also provides a video call function that allows video calls to be made while watching. This allows multiple users to simultaneously watch a re-created concert or play and exchange opinions in real time.

[0045] The generation unit can provide a function that allows the reproduced video to be viewed on a VR headset or an AR device. The generation unit, for example, makes the reproduced 3D content viewable on a VR headset. For example, it makes the reproduced content compatible with devices such as Oculus Rift and HTC Vive. The generation unit also makes the reproduced content viewable on an AR device. For example, it makes the reproduced content compatible with devices such as Microsoft HoloLens and Magic Leap. The generation unit also provides an interface for viewing on a VR headset or an AR device. This allows the reproduced video to be viewed on a VR headset or an AR device.

[0046] The generation unit may provide a function that allows a user to apply custom effects or filters to the movements of their favorite idol. For example, the generation unit allows a user to apply custom effects to the movements of their favorite idol. For example, the generation unit may add a light effect to a dance performance. The generation unit may also allow a user to apply filters to the movements of their favorite idol. For example, the generation unit may apply a color correction filter or a special effect filter. The generation unit may also allow a user to save the movements of their favorite idol to which the custom effects or filters have been applied. This allows the user to apply custom effects or filters to the movements of their favorite idol.

[0047] The generation unit can provide a function that allows you to watch your favorite idol moving on a VR headset or AR device. The generation unit, for example, makes it possible to watch your favorite idol moving on a VR headset. For example, it makes it compatible with devices such as Oculus Rift and HTC Vive. The generation unit also makes it possible to watch your favorite idol moving on an AR device. For example, it makes it compatible with devices such as Microsoft HoloLens and Magic Leap. The generation unit also provides an interface for viewing on a VR headset or AR device. This allows you to watch your favorite idol moving on a VR headset or AR device.

[0048] The generation unit can provide a function that allows multiple users to simultaneously watch the same content from different angles and exchange opinions in real time. The generation unit, for example, allows multiple users to simultaneously watch the same content from different angles. For example, watching a performance of a favorite artist together with friends. The generation unit also provides a function that allows for real-time exchange of opinions. For example, a chat function or a voice call function is provided. The generation unit also provides a video call function that allows video calls to be made while watching. This allows multiple users to simultaneously watch the same content from different angles and exchange opinions in real time.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The video viewing system may further include a recommendation unit that analyzes a user's viewing history and recommends content that is optimal for each individual user. For example, the recommendation unit may analyze the genres and artists of videos that the user has previously viewed and recommend similar content. The recommendation unit may also recommend new content that the user may be interested in based on the user's viewing time and viewing frequency. Furthermore, the recommendation unit may compare the user's viewing history with other users and recommend content that users with similar viewing habits prefer. This allows users to easily find new content that matches their preferences.

[0051] The analysis unit can analyze movements in a video and provide a function that allows users to track specific movements. For example, in a sports game, the analysis unit can track the movements of a specific player and highlight those movements. The analysis unit can also analyze the movements of a dancer in a dance performance and allow users to track the movements of that specific dancer. Furthermore, the analysis unit can track the movements of a specific animal in an animal documentary video and highlight those movements. This allows users to easily track movements of interest and improves the viewing experience.

[0052] The analysis unit can analyze text data in a video and provide a function that allows users to search for specific text. For example, the analysis unit can analyze subtitles in a movie to allow users to search for specific lines. The analysis unit can also analyze captions in news videos to allow users to search for specific news items. Furthermore, the analysis unit can analyze text on slides or blackboards in educational videos to allow users to search for specific keywords. This allows users to quickly find the information they need.

[0053] The analysis unit can analyze objects in a video and provide a function that allows users to highlight specific objects. For example, in a movie scene, the analysis unit can highlight specific props or costumes. The analysis unit can also highlight specific buildings or scenery in a documentary video. Furthermore, the analysis unit can highlight specific laboratory equipment or teaching materials in an educational video. This allows users to easily find objects of interest and improves their viewing experience.

[0054] The analyzer can analyze audio data in a video and provide a function that allows users to highlight specific audio. For example, the analyzer can highlight the lines of a specific character in a movie scene. The analyzer can also highlight the sound of a specific instrument in a concert video. Furthermore, the analyzer can highlight a specific narration in a documentary video. This allows users to easily find audio of interest and improve their viewing experience.

[0055] The analyzer can analyze environmental sounds in a video and provide a feature that allows users to highlight specific environmental sounds. For example, the analyzer can highlight specific animal sounds in a nature documentary. The analyzer can also highlight specific traffic sounds in urban footage. Furthermore, the analyzer can highlight specific sound effects in a horror movie. This allows users to easily find environmental sounds that interest them and improve their viewing experience.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The image import unit imports images. For example, it imports video images in digital format. It can also import live images in real time. It can also import 3D images. Step 2: The analysis unit analyzes the video read by the video reading unit. For example, the analysis unit may use image analysis technology to identify the positions of people and objects in the video. The analysis unit may also use audio analysis technology to analyze audio data in the video. The analysis unit may also use data analysis technology to analyze movement in the video. Step 3: The generation unit generates content that can be viewed from any angle based on the data analyzed by the analysis unit. For example, the generation unit generates a 3D model that allows the user to view from any angle. The generation unit can also generate video in real time according to the user's desired viewing angle or distance by specifying the angle or distance from which the user wishes to view the content. The generation unit can also enable a specific scene or performance to be viewed repeatedly.

[0058] (Example 2) A video viewing system according to an embodiment of the present invention is a system that converts video into content that can be viewed from any angle, thereby enabling the video viewing system to infinitely expand the viewing experience.

[0059] A video viewing system according to an embodiment includes a video reading unit, an analysis unit, and a generation unit. The video reading unit reads video. For example, the video reading unit reads video in digital format. It can also read live video in real time. It can also read 3D video. The analysis unit analyzes the video read by the video reading unit. For example, the analysis unit can identify the positions of people and objects in the video using image analysis technology. It can also analyze audio data in the video using audio analysis technology. It can also analyze movement in the video using data analysis technology. The generation unit generates content that can be viewed from any angle based on the data analyzed by the analysis unit. For example, the generation unit generates a 3D model to allow a user to view the content from any angle. The generation unit can also generate video in real time according to a user's desired viewing angle or distance by specifying the angle or distance. The generation unit can also enable a specific scene or performance to be viewed repeatedly. This allows the video viewing system according to an embodiment to convert video into content that can be viewed from any angle. For example, a user can view a concert or play video from various angles. Users can also watch their favorite idols in action from any angle in 360 degrees. Users can also replay and watch past live performance footage from various angles.

[0060] The analysis unit analyzes the audio data and can reflect the position of the sound source and the reverberation of the sound in the 3D model. For example, the analysis unit analyzes audio data in a video and identifies the position of the sound source. For example, the analysis unit identifies the positions of speakers and instruments on a stage and reflects the reverberation of the sound in the 3D model. The analysis unit also analyzes the audio data and reproduces the reverberation of the sound. For example, echo analysis technology is used to reproduce the reverberation of the sound. The analysis unit also analyzes the audio data and reproduces the reverberation of the sound. For example, reverberation analysis technology is used to reproduce the reverberation of the sound. In this way, the audio data can be analyzed and realistic sound effects can be reproduced.

[0061] The analysis unit analyzes light intensity and color temperature to reproduce realistic lighting effects. For example, the analysis unit analyzes the light intensity in a video and reflects it in a 3D model. For example, it reproduces the intensity of a spotlight on a stage. The analysis unit also analyzes color temperature to reproduce realistic lighting effects. For example, it measures color temperature using a color temperature meter and reproduces lighting effects based on that data. The analysis unit also combines light intensity and color temperature to reproduce realistic lighting effects. For example, it uses physically based rendering technology to perform light source simulation. This allows it to analyze light intensity and color temperature and reproduce realistic lighting effects.

[0062] The analysis unit can use an emotion estimation function to analyze a person's emotion and add visual effects according to the emotion. The analysis unit, for example, analyzes the facial expression of a person in the video to estimate the emotion. For example, it uses facial expression recognition technology to analyze the person's facial expression and estimate joy or sadness. The analysis unit can also use voice analysis technology to estimate the emotion. For example, it can analyze the tone and speed of the voice to estimate the emotion. The analysis unit can also use electroencephalogram analysis technology to estimate the emotion. For example, it can analyze electroencephalogram data to estimate the emotion. In this way, the emotion estimation function can be used to add visual effects according to the emotion of a person in the video.

[0063] When loading video, the analysis unit can prioritize analysis of specific scenes and people specified by the user. For example, the analysis unit prioritizes analysis of specific scenes specified by the user. For example, the climax scene of a concert may be prioritized and reflected in the 3D model. The analysis unit also prioritizes analysis of specific people specified by the user. For example, the analysis unit prioritizes analysis of an artist's performance and reflects it in the 3D model. The analysis unit also prioritizes analysis of specific scenes and people specified by the user and generates content based on that data. This allows the analysis of specific scenes and people specified by the user to be prioritized.

[0064] The analysis unit can provide a function for sharing the results of video analysis with other users and for collaboratively editing 3D models. For example, the analysis unit stores the results of video analysis on the cloud and shares them with other users. For example, multiple users can collaboratively edit the video of the same concert. The analysis unit also shares the analysis results in real time and collaboratively edits 3D models. For example, multiple users can perform editing work simultaneously. The analysis unit also provides a version management function and manages the editing history. This allows analysis results to be shared with other users and 3D models to be collaboratively edited.

[0065] The analysis unit can analyze the user's emotions in real time using an emotion estimation function and optimize the viewing experience. The analysis unit, for example, analyzes the user's facial expressions and estimates the emotions during viewing in real time. For example, facial expression recognition technology is used to detect smiling or surprised expressions. The analysis unit also estimates the user's emotions in real time using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice to estimate the emotions. The analysis unit also estimates the user's emotions in real time using electroencephalogram analysis technology. For example, the analysis unit analyzes electroencephalogram data to estimate the emotions. This allows the user's emotions to be analyzed in real time and the viewing experience to be optimized.

[0066] The generation unit may provide a function that allows a user to apply custom effects and filters to the generated 3D model. For example, the generation unit allows a user to apply custom effects to the 3D model. For example, the generation unit may add a stage light effect or special effect. The generation unit may also allow a user to apply filters to the 3D model. For example, the generation unit may apply a blur filter or a sharpening filter. The generation unit may also allow a user to save the 3D model to which the custom effects and filters have been applied. This allows a user to apply custom effects and filters to the generated 3D model.

[0067] The generation unit uses the generation AI to automatically suggest the optimal viewing angle, providing the user with a new viewing experience. For example, the generation AI automatically suggests the optimal viewing angle. For example, it suggests the angle from which the artist's performance is best viewed. The generation unit also uses the generation AI to suggest the optimal viewing angle based on the user's viewing history. For example, it analyzes past viewing data and suggests the viewing angle that the user prefers. The generation unit also uses the generation AI to suggest the optimal viewing angle in real time. This allows the generation AI to automatically suggest the optimal viewing angle, providing a new viewing experience.

[0068] The generation unit can automatically adjust the viewing angle according to the user's emotion using the emotion estimation function. The generation unit, for example, analyzes the user's emotion and automatically adjusts the viewing angle according to the emotion. For example, when the user is excited, a close-up viewing angle is provided. The generation unit also analyzes the user's emotion in real time and adjusts the viewing angle. For example, when the emotion score is high, a specific viewing angle is provided. The generation unit also suggests a viewing angle according to the user's emotion. This makes it possible to automatically adjust the viewing angle according to the user's emotion.

[0069] The generation unit can provide a function that allows the generated content to be viewed on a VR headset or an AR device. The generation unit, for example, makes the generated 3D content viewable on a VR headset. For example, it makes the content compatible with devices such as Oculus Rift and HTC Vive. The generation unit also makes the generated content viewable on an AR device. For example, it makes the content compatible with devices such as Microsoft HoloLens and Magic Leap. The generation unit also provides an interface for viewing on a VR headset or an AR device. This allows the generated content to be viewed on a VR headset or an AR device.

[0070] The generation unit can provide a function that allows multiple users to simultaneously watch the same content from different angles and exchange opinions in real time. The generation unit, for example, allows multiple users to simultaneously watch the same content from different angles. For example, watching a concert video with friends. The generation unit also provides a function that allows users to exchange opinions in real time. For example, a chat function or a voice call function is provided. The generation unit also provides a video call function that allows users to make a video call while watching. This allows multiple users to simultaneously watch the same content from different angles and exchange opinions in real time.

[0071] The generation unit may provide a function that allows a user to mark a specific scene while watching and easily re-watch the scene later. For example, the generation unit provides a function that allows a user to mark a specific scene while watching. For example, a scene is marked by pressing a button while watching. The generation unit also provides a function that allows a user to easily re-watch the marked scene later. For example, the marked scene is saved in a list and the scene is later selected from the list to re-watch. The generation unit also provides a function that allows a user to add a comment to the marked scene. This allows a user to mark a specific scene while watching and easily re-watch the scene later.

[0072] The generation unit can provide a function that allows the user to change the viewing angle or distance in real time using a voice command. The generation unit provides, for example, a function that allows the user to change the viewing angle using a voice command. For example, the generation unit recognizes commands such as "turn it to the right" or "show me from above." The generation unit also provides a function that allows the user to change the viewing distance using a voice command. For example, the generation unit recognizes commands such as "move closer" or "move further away." The generation unit also provides a function that allows the user to save viewing settings using a voice command. This allows the user to change the viewing angle or viewing distance in real time using a voice command.

[0073] The generation unit can automatically suggest customization options according to the user's emotions using the emotion estimation function. For example, the generation unit uses the emotion estimation function to suggest customization options according to the user's emotions. For example, when the user is excited, dynamic visual effects are added. The generation unit also analyzes the user's emotions in real time and suggests customization options. For example, when the emotion score is high, a specific effect is suggested. The generation unit also suggests customization options based on the user's viewing history. This makes it possible to automatically suggest customization options according to the user's emotions.

[0074] The generation unit can provide a function that allows a user to share a viewing experience with other users and perform collaborative customization. The generation unit provides, for example, a function that allows a user to share a viewing experience with other users. For example, viewing settings and customization contents can be shared. The generation unit also provides a function that allows collaborative customization. For example, multiple users can perform customization work simultaneously. The generation unit also provides a function that allows a shared viewing experience to be saved. This allows a user to share a viewing experience with other users and perform collaborative customization.

[0075] The generation unit may provide a function for saving settings selected by a user when customizing the viewing experience and automatically applying them the next time the user views. The generation unit may provide, for example, a function for saving viewing settings selected by a user. For example, the generation unit may save settings such as viewing angles and effects. The generation unit may also provide a function for automatically applying the saved settings the next time the user views. For example, the generation unit may automatically apply settings based on a user profile. The generation unit may also provide a function for saving multiple settings and applying them according to the situation. This allows the user to save settings selected by the user and automatically apply them the next time the user views.

[0076] The generation unit can use the emotion estimation function to identify the viewing setting that the user enjoys most and recommend that setting. For example, the generation unit uses the emotion estimation function to identify the viewing setting that the user enjoys most. For example, the generation unit recommends a setting with a high emotion score. The generation unit also identifies the viewing setting that the user enjoys most based on the user's viewing history. For example, the generation unit analyzes past viewing data and recommends the setting that the user enjoys. The generation unit also analyzes the user's emotions in real time to identify the viewing setting that the user enjoys most. This makes it possible to identify the viewing setting that the user enjoys most and recommend that setting.

[0077] The generation unit can provide a function that analyzes video of past concerts or plays and recreates the audience reactions and atmosphere of the time. The generation unit, for example, analyzes video of past concerts or plays and recreates the audience reactions. For example, the sounds of cheers and applause are reflected in the 3D model. The generation unit also recreates the atmosphere within the video. For example, sound effects and lighting effects are recreated. The generation unit also recreates a combination of the audience reactions and the atmosphere. This makes it possible to analyze video of past concerts or plays and recreate the audience reactions and atmosphere of the time.

[0078] The generation unit uses the generation AI to automatically suggest the optimal viewing sequence, thereby providing the user with a new viewing experience. For example, the generation unit uses the generation AI to automatically suggest the optimal viewing sequence. For example, it may suggest a sequence that best shows the artist's performance. The generation unit also uses the generation AI to suggest the optimal viewing sequence based on the user's viewing history. For example, it may analyze past viewing data and suggest a sequence that the user prefers. The generation unit also uses the generation AI to suggest the optimal viewing sequence in real time. This allows the generation AI to automatically suggest the optimal viewing sequence, providing a new viewing experience.

[0079] The generation unit can automatically select and reproduce scenes according to the user's emotions using the emotion estimation function. The generation unit, for example, automatically selects scenes according to the user's emotions using the emotion estimation function. For example, a specific scene is reproduced when the user is moved. The generation unit also analyzes the user's emotions in real time and selects scenes. For example, a specific scene is reproduced when the emotion score is high. The generation unit also selects scenes based on the user's viewing history. In this way, scenes according to the user's emotions can be automatically selected and reproduced.

[0080] The generation unit can provide a function that allows multiple users to simultaneously watch a re-created concert or play and exchange opinions in real time. The generation unit, for example, allows multiple users to simultaneously watch a re-created concert or play. For example, watching a past live video together with friends. The generation unit also provides a function that allows for the exchange of opinions in real time. For example, a chat function or a voice call function is provided. The generation unit also provides a video call function that allows video calls to be made while watching. This allows multiple users to simultaneously watch a re-created concert or play and exchange opinions in real time.

[0081] The generation unit can provide a function that allows the reproduced video to be viewed on a VR headset or an AR device. The generation unit, for example, makes the reproduced 3D content viewable on a VR headset. For example, it makes the reproduced content compatible with devices such as Oculus Rift and HTC Vive. The generation unit also makes the reproduced content viewable on an AR device. For example, it makes the reproduced content compatible with devices such as Microsoft HoloLens and Magic Leap. The generation unit also provides an interface for viewing on a VR headset or an AR device. This allows the reproduced video to be viewed on a VR headset or an AR device.

[0082] The generation unit can use the emotion estimation function to identify the scene that moves the user the most and recommend that scene. The generation unit, for example, uses the emotion estimation function to identify the scene that moves the user the most. For example, it recommends a scene with a high emotion score. The generation unit also identifies the most moving scene based on the user's viewing history. For example, it analyzes past viewing data and recommends a scene with a high emotion score. The generation unit also analyzes the user's emotions in real time and identifies the most moving scene. This allows the generation unit to identify the scene that moves the user the most and recommend that scene.

[0083] The generation unit may provide a function that allows a user to apply custom effects or filters to the movements of their favorite idol. For example, the generation unit allows a user to apply custom effects to the movements of their favorite idol. For example, the generation unit may add a light effect to a dance performance. The generation unit may also allow a user to apply filters to the movements of their favorite idol. For example, the generation unit may apply a color correction filter or a special effect filter. The generation unit may also allow a user to save the movements of their favorite idol to which the custom effects or filters have been applied. This allows the user to apply custom effects or filters to the movements of their favorite idol.

[0084] The generation unit can automatically adjust the viewing angle according to the user's emotion using the emotion estimation function. The generation unit, for example, analyzes the user's emotion and automatically adjusts the viewing angle according to the emotion. For example, when the user is excited, a close-up viewing angle is provided. The generation unit also analyzes the user's emotion in real time and adjusts the viewing angle. For example, when the emotion score is high, a specific viewing angle is provided. The generation unit also suggests a viewing angle according to the user's emotion. This makes it possible to automatically adjust the viewing angle according to the user's emotion.

[0085] The generation unit can provide a function that allows you to watch your favorite idol moving on a VR headset or AR device. The generation unit, for example, makes it possible to watch your favorite idol moving on a VR headset. For example, it makes it compatible with devices such as Oculus Rift and HTC Vive. The generation unit also makes it possible to watch your favorite idol moving on an AR device. For example, it makes it compatible with devices such as Microsoft HoloLens and Magic Leap. The generation unit also provides an interface for viewing on a VR headset or AR device. This allows you to watch your favorite idol moving on a VR headset or AR device.

[0086] The generation unit can provide a function that allows multiple users to simultaneously watch the same content from different angles and exchange opinions in real time. The generation unit, for example, allows multiple users to simultaneously watch the same content from different angles. For example, watching a performance of a favorite artist together with friends. The generation unit also provides a function that allows for real-time exchange of opinions. For example, a chat function or a voice call function is provided. The generation unit also provides a video call function that allows video calls to be made while watching. This allows multiple users to simultaneously watch the same content from different angles and exchange opinions in real time.

[0087] The generation unit can use the emotion estimation function to identify the viewing angle that most impresses the user and recommend viewing from that angle. The generation unit, for example, uses the emotion estimation function to identify the viewing angle that most impresses the user. For example, the generation unit recommends a viewing angle with a high emotion score. The generation unit also identifies the most impressing viewing angle based on the user's viewing history. For example, the generation unit analyzes past viewing data and recommends a viewing angle with a high emotion score. The generation unit also analyzes the user's emotions in real time and identifies the most impressing viewing angle. This makes it possible to identify the viewing angle that most impresses the user and recommend viewing from that angle.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The video viewing system may further include a recommendation unit that analyzes a user's viewing history and recommends content that is optimal for each individual user. For example, the recommendation unit may analyze the genres and artists of videos that the user has previously viewed and recommend similar content. The recommendation unit may also recommend new content that the user may be interested in based on the user's viewing time and viewing frequency. Furthermore, the recommendation unit may compare the user's viewing history with other users and recommend content that users with similar viewing habits prefer. This allows users to easily find new content that matches their preferences.

[0090] The analysis unit can analyze movements in a video and provide a function that allows users to track specific movements. For example, in a sports game, the analysis unit can track the movements of a specific player and highlight those movements. The analysis unit can also analyze the movements of a dancer in a dance performance and allow users to track the movements of that specific dancer. Furthermore, the analysis unit can track the movements of a specific animal in an animal documentary video and highlight those movements. This allows users to easily track movements of interest and improves the viewing experience.

[0091] The analysis unit can analyze text data in a video and provide a function that allows users to search for specific text. For example, the analysis unit can analyze subtitles in a movie to allow users to search for specific lines. The analysis unit can also analyze captions in news videos to allow users to search for specific news items. Furthermore, the analysis unit can analyze text on slides or blackboards in educational videos to allow users to search for specific keywords. This allows users to quickly find the information they need.

[0092] The analysis unit can analyze the user's emotions using the emotion estimation function and add sound effects according to the emotions. For example, when the user is excited, the analysis unit can increase the volume of the music or add an echo effect. When the user is relaxed, the analysis unit can slow down the tempo of the music or add a reverb effect. When the user is sad, the analysis unit can lower the tone of the music or add quiet environmental sounds. This allows the user to add sound effects according to their emotions and improve their viewing experience.

[0093] The analysis unit can analyze objects in a video and provide a function that allows users to highlight specific objects. For example, in a movie scene, the analysis unit can highlight specific props or costumes. The analysis unit can also highlight specific buildings or scenery in a documentary video. Furthermore, the analysis unit can highlight specific laboratory equipment or teaching materials in an educational video. This allows users to easily find objects of interest and improves their viewing experience.

[0094] The analysis unit can analyze the user's emotions using the emotion estimation function and apply a video filter according to the emotion. For example, when the user is excited, a vivid color filter can be applied to the video. When the user is relaxed, the analysis unit can also apply a soft color filter to the video. Furthermore, when the user is sad, the analysis unit can also apply a monochrome filter to the video. In this way, the viewing experience can be improved by applying a video filter according to the user's emotions.

[0095] The analyzer can analyze audio data in a video and provide a function that allows users to highlight specific audio. For example, the analyzer can highlight the lines of a specific character in a movie scene. The analyzer can also highlight the sound of a specific instrument in a concert video. Furthermore, the analyzer can highlight a specific narration in a documentary video. This allows users to easily find audio of interest and improve their viewing experience.

[0096] The analysis unit can analyze the user's emotions using the emotion estimation function and adjust the subtitle display according to the emotions. For example, when the user is excited, the analysis unit can increase the font size of the subtitles or change the color. The analysis unit can also slow down the display speed of the subtitles when the user is relaxed. Furthermore, the analysis unit can change the background color of the subtitles when the user is sad. This allows the subtitle display to be adjusted according to the user's emotions, improving the viewing experience.

[0097] The analyzer can analyze environmental sounds in a video and provide a feature that allows users to highlight specific environmental sounds. For example, the analyzer can highlight specific animal sounds in a nature documentary. The analyzer can also highlight specific traffic sounds in urban footage. Furthermore, the analyzer can highlight specific sound effects in a horror movie. This allows users to easily find environmental sounds that interest them and improve their viewing experience.

[0098] The analysis unit can analyze the user's emotions using the emotion estimation function and provide a viewing guide that corresponds to the emotions. For example, when the user is excited, action scenes or climax scenes can be recommended. When the user is relaxed, the analysis unit can also recommend calm scenes or scenic scenes. Furthermore, when the user is sad, the analysis unit can also recommend moving scenes or heartwarming scenes. This allows the user to receive a viewing guide that corresponds to their emotions, improving the viewing experience.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The image import unit imports images. For example, it imports video images in digital format. It can also import live images in real time. It can also import 3D images. Step 2: The analysis unit analyzes the video read by the video reading unit. For example, the analysis unit may use image analysis technology to identify the positions of people and objects in the video. The analysis unit may also use audio analysis technology to analyze audio data in the video. The analysis unit may also use data analysis technology to analyze movement in the video. Step 3: The generation unit generates content that can be viewed from any angle based on the data analyzed by the analysis unit. For example, the generation unit generates a 3D model that allows the user to view from any angle. The generation unit can also generate video in real time according to the user's desired viewing angle or distance by specifying the angle or distance from which the user wishes to view the content. The generation unit can also enable a specific scene or performance to be viewed repeatedly.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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 reading unit that reads video; an analysis unit that analyzes the video read by the video reading unit; a generation unit that generates content that can be viewed from any angle based on the data analyzed by the analysis unit. A system characterized by:

2. The analysis unit Analyze the audio data in the video and reflect the location of the sound source and the reverberation of the sound in the 3D model.

2. The system of claim 1.

3. The analysis unit When the video is loaded, specific scenes or people designated by the user are given priority in the analysis.

2. The system of claim 1.

4. The generation unit Providing a function that allows a user to apply custom effects and filters to the generated 3D model 2. The system of claim 1.

5. The generation unit Providing the ability for users to mark specific scenes while watching and easily rewatch those scenes later 2. The system of claim 1.

6. The generation unit Provides a function to analyze the footage of past concerts and plays and recreate the reactions and atmosphere of the audience at the time.

2. The system of claim 1.

7. The analysis unit Using emotion estimation function, the emotions of people in the video are analyzed and visual effects corresponding to the emotions are added.

2. The system of claim 1.

Images