System

Abstract

An object of the system according to the embodiment is to simplify the processing when projecting the image onto the spherical surface.SOLUTION: A system according to an embodiment includes a division unit, a complementary unit, and a projection unit. The division part divides the video in a grid shape. The complementing unit complements a gap of the video divided by the dividing unit. The projection unit projects the video complemented by the complementing unit onto the spherical surface.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the processing required to minimize image distortion when projecting an image onto a spherical surface is complex, and there is room for improvement.

[0005] The system according to the embodiment aims to simplify the processing required to project an image onto a spherical surface. [Means for solving the problem]

[0006] The system according to the embodiment includes a division unit, a complementing unit, and a projection unit. The division unit divides an image into a grid pattern. The complementing unit complements gaps in the image divided by the division unit. The projection unit projects the image complemented by the complementing unit onto a spherical surface. [Effects of the Invention]

[0007] The system according to the embodiment can simplify the processing required to project an image onto a spherical surface. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention provides a planetarium movie viewing experience using a 360° screen. This system uses generative AI to create custom images to project onto a spherical surface, creating a sense of being immersed in the film. This creates a spectacular, dynamic visual experience and a sense of immersion. For example, the system divides an existing film into meshes, and then uses generative AI to process the images so that the gaps between them are filled in. The processed images are then projected onto a spherical surface. This process creates a sense of being immersed in the film. This system meets consumer demand for new visual experiences and makes watching movies in planetariums more engaging. Furthermore, the use of generative AI simplifies image processing and reduces costs, making it more likely to be commercially successful.

[0029] A planetarium movie viewing system according to an embodiment includes a division unit, a complement unit, and a projection unit. The division unit divides an image into a grid pattern. For example, the division unit divides an existing movie into a mesh pattern and converts it into an image for spherical projection. The complement unit uses a generation AI to complement gaps in the image divided by the division unit. For example, the complement unit uses the generation AI to naturally complement gaps in the image. The complement unit can also use the generation AI to perform complementation to reduce image distortion. The projection unit projects the image complemented by the complement unit onto a spherical surface. For example, the projection unit accurately projects the complemented image onto the spherical surface. This allows the planetarium movie viewing system according to an embodiment to provide the feeling of being inside the movie.

[0030] The completion unit can use a generation AI to complete gaps in the video. The generation AI, for example, completes gaps in the video naturally. To complete gaps in the video, the generation AI uses a model learned from training data. When completing gaps, the generation AI completes the gaps in a way that maintains the continuity of the video. The generation AI maintains the consistency of the video by using an algorithm for completing gaps. This allows the generation AI to complete gaps in the video naturally.

[0031] The completion unit can use a generating AI to perform completion to reduce distortion in the image. The generating AI performs completion to reduce distortion in the image, for example. The generating AI uses a model learned from training data to reduce distortion in the image. The generating AI maintains the consistency of the image by using an algorithm to reduce distortion. When performing completion to reduce distortion, the generating AI performs completion in a way that maintains the continuity of the image. In this way, by using the generating AI, distortion in the image can be minimized.

[0032] The division unit can divide an existing movie into mesh-like parts. The division unit, for example, divides an existing movie into mesh-like parts. The division unit selects an appropriate mesh size depending on the format and resolution of the movie. When dividing into mesh-like parts, the division unit performs the division so as to maintain the continuity of the image. The division unit maintains the consistency of the image by using an algorithm for dividing into mesh-like parts. In this way, by dividing an existing movie into mesh-like parts, it is possible to convert it into an image for spherical projection.

[0033] The projection unit can accurately project the interpolated image onto a spherical surface. For example, the projection unit accurately projects the interpolated image onto a spherical surface. The projection unit maintains the continuity of the image by adjusting the projection angle and positioning. The projection unit uses a projection algorithm to improve the accuracy of the projection. The projection unit performs projection so as to maintain the consistency of the image during projection. As a result, the continuity of the image can be maintained by accurately projecting the interpolated image onto a spherical surface.

[0034] The completion unit can use a generation AI to naturally complete gaps in the video. The generation AI, for example, naturally completes gaps in the video. The generation AI uses a model learned from training data to complete gaps in the video. When completing gaps, the generation AI performs the completion so as to maintain the continuity of the video. The generation AI maintains the consistency of the video by using an algorithm for completing gaps. This makes it possible to naturally complete gaps in the video by using the generation AI.

[0035] The segmentation unit can apply different mesh sizes based on the content of the video to perform appropriate segmentation. For example, the segmentation unit applies a fine mesh size to scenes with rapid movement in the video to perform detailed segmentation. The segmentation unit applies a larger mesh size to still scenes in the video to perform efficient segmentation. The segmentation unit applies an especially fine mesh size to important scenes in the video to perform detailed segmentation. This makes it possible to improve the quality of the video by performing optimal segmentation according to the content of the video.

[0036] The division unit can dynamically change the mesh division density according to the speed of movement in the video. For example, in scenes where the video is moving fast, the division unit increases the mesh division density to perform detailed division. In scenes where the video is moving slowly, the division unit decreases the mesh division density to perform efficient division. In scenes where the video movement changes, the division unit adjusts the mesh division density in real time according to the speed of the movement. In this way, by changing the division density according to the speed of movement in the video, the quality of the video can be improved.

[0037] The segmentation unit can prioritize segmentation of important scenes in the video and apply detailed meshes. For example, the segmentation unit applies a particularly fine mesh size to a climax scene in the video to perform detailed segmentation. The segmentation unit applies a fine mesh size to an emotional scene in the video to enhance the visual effect. The segmentation unit applies a fine mesh size to an action scene in the video to capture the details of the movement. In this way, by prioritizing segmentation of important scenes, the visual effect can be enhanced.

[0038] The division unit can adjust the mesh division pattern based on the hue and brightness of the image. For example, in scenes with vivid hue in the image, the division unit applies a fine mesh size to perform detailed division. In scenes with high brightness in the image, the division unit applies a larger mesh size to perform efficient division. In scenes where the hue and brightness of the image change, the division unit adjusts the mesh division pattern in real time according to the hue and brightness. In this way, by adjusting the division pattern according to the hue and brightness, it is possible to improve the quality of the image.

[0039] The division unit can analyze audio information of the video and change the mesh division density according to the strength of the audio. For example, in scenes with strong audio, the division unit applies a fine mesh size to perform detailed division. In scenes with weak audio, the division unit applies a larger mesh size to perform efficient division. In scenes where the audio strength changes, the division unit adjusts the mesh division density in real time according to the audio strength. In this way, the quality of the video can be improved by changing the division density according to the audio strength.

[0040] The division unit can adjust the mesh division timing based on a scene change in the video. The division unit divides the mesh, for example, at the timing of a scene change, to maintain the continuity of the video. The division unit divides the mesh before a scene change, to smoothly transition to the next scene. The division unit divides the mesh after a scene change, to maintain the consistency of the video. In this way, by adjusting the division timing based on a scene change, the continuity of the video can be maintained.

[0041] The completion unit can apply different completion algorithms based on the content of the video to perform appropriate completion. For example, in scenes with a lot of movement in the video, the generation AI applies a detailed completion algorithm. In still scenes in the video, the completion unit applies an efficient completion algorithm. In important scenes in the video, the generation AI applies a particularly detailed completion algorithm. This makes it possible to improve the quality of the video by performing optimal completion according to the content of the video.

[0042] The completion unit can dynamically change the accuracy of completion depending on the speed of video movement. For example, in scenes with fast video movement, the generation AI performs highly accurate completion. In scenes with slow video movement, the generation AI performs efficient completion. In scenes with changing video movement, the generation AI adjusts the accuracy of completion in real time depending on the speed of the movement. This makes it possible to improve video quality by changing the accuracy of completion depending on the speed of movement.

[0043] The completion unit can prioritize completion of important scenes in the video and apply detailed completion. For example, in the climax scene of the video, the generation AI performs particularly detailed completion. In the moving scenes of the video, the generation AI performs visually effective completion. In the action scenes of the video, the generation AI performs completion that captures the details of the movements. This makes it possible to enhance the visual effect by prioritizing completion of important scenes.

[0044] The completion unit can adjust the completion algorithm based on the hue and brightness of the image. For example, in scenes with vivid hue, the generation AI of the completion unit applies a detailed completion algorithm. In scenes with high brightness, the generation AI of the completion unit applies an efficient completion algorithm. In scenes where the hue or brightness of the image changes, the generation AI of the completion unit adjusts the completion algorithm in real time according to the hue and brightness. This makes it possible to improve the quality of the image by adjusting the completion algorithm according to the hue and brightness.

[0045] The completion unit analyzes the audio information of the video and can change the accuracy of completion depending on the strength of the audio. For example, in scenes with strong audio, the generation AI performs high-precision completion. In scenes with weak audio, the generation AI performs efficient completion. In scenes where the audio strength changes, the generation AI adjusts the accuracy of completion in real time depending on the audio strength. This makes it possible to improve the quality of the video by changing the accuracy of completion depending on the audio strength.

[0046] The completion unit can adjust the timing of completion based on scene changes in the video. For example, the generation AI performs completion at the timing of a scene change in the completion unit, maintaining the continuity of the video. The generation AI performs completion before a scene change in the completion unit, ensuring a smooth transition to the next scene. The generation AI performs completion after a scene change in the completion unit, maintaining the consistency of the video. In this way, by adjusting the timing of completion based on scene changes, it is possible to maintain the continuity of the video.

[0047] The projection unit can apply different projection algorithms based on the content of the video to perform appropriate projection. For example, the projection unit applies a detailed projection algorithm to scenes with a lot of movement in the video. The projection unit applies an efficient projection algorithm to still scenes in the video. The projection unit applies a particularly detailed projection algorithm to important scenes in the video. This makes it possible to improve the quality of the video by performing optimal projection according to the content of the video.

[0048] The projection unit can dynamically change the projection accuracy according to the speed of movement in the image. For example, in a scene with fast movement in the image, the projection unit performs high-accuracy projection to capture the details of the movement. In a scene with slow movement in the image, the projection unit performs efficient projection to save resources. In a scene with changing movement in the image, the projection unit adjusts the projection accuracy in real time according to the speed of the movement. This makes it possible to improve the quality of the image by changing the projection accuracy according to the speed of the movement.

[0049] The projection unit can prioritize projection of important scenes in the video and apply detailed projection. For example, the projection unit performs particularly detailed projection in the climax scene of the video to enhance the visual effect. The projection unit performs visually effective projection in moving scenes of the video. The projection unit performs projection that captures the details of the movements in action scenes of the video. In this way, by prioritizing projection of important scenes, the visual effect can be enhanced.

[0050] The projection unit can adjust the projection algorithm based on the hue and brightness of the image. For example, the projection unit applies a detailed projection algorithm to a scene with vivid hue of the image. The projection unit applies an efficient projection algorithm to a scene with high brightness of the image. The projection unit adjusts the projection algorithm in real time according to the hue and brightness of the image in a scene where the hue and brightness of the image change. In this way, the quality of the image can be improved by adjusting the projection algorithm according to the hue and brightness.

[0051] The projection unit can analyze audio information of the video and change the precision of projection according to the strength of the audio. For example, in scenes with strong audio, the projection unit performs high-precision projection to improve the link between vision and audio. In scenes with weak audio, the projection unit performs efficient projection to save resources. In scenes where the audio strength changes, the projection unit adjusts the precision of projection in real time according to the strength of the audio. This makes it possible to improve the quality of the video by changing the precision of projection according to the strength of the audio.

[0052] The projection unit can adjust the timing of projection based on a scene change in the video. For example, the projection unit performs projection at the timing of a scene change, thereby maintaining the continuity of the video. The projection unit performs projection before a scene change, thereby smoothly transitioning to the next scene. The projection unit performs projection after a scene change, thereby maintaining the consistency of the video. In this way, by adjusting the timing of projection based on a scene change, the continuity of the video can be maintained.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The division unit can track the user's gaze and adjust the image division method based on the gaze movement. For example, if the user focuses their gaze on a specific area, that area is divided into fine mesh sizes to provide a detailed image. Conversely, if the user moves their gaze, a wider area is divided into larger mesh sizes to provide a more comprehensive image. Furthermore, if the gaze moves quickly, the mesh size can be adjusted in real time to reduce visual strain. This makes it possible to provide a more appropriate video experience by dynamically adjusting the image division method according to the user's gaze.

[0055] The segmentation unit can apply different mesh patterns based on the content of the video to perform appropriate segmentation. For example, in scenes with a lot of movement in the video, a fine mesh pattern is applied to perform detailed segmentation. In still scenes in the video, a larger mesh pattern is applied to perform efficient segmentation. In important scenes in the video, a particularly fine mesh pattern is applied to perform detailed segmentation. This makes it possible to improve the quality of the video by performing optimal segmentation according to the content of the video.

[0056] The interpolation unit can adjust the interpolation algorithm based on the hue and brightness of the image. For example, in scenes with vivid hue, the generation AI applies a detailed interpolation algorithm. In scenes with high brightness, the generation AI applies an efficient interpolation algorithm. In scenes where the hue and brightness of the image change, the generation AI adjusts the interpolation algorithm in real time according to the hue and brightness. This allows for improved image quality by adjusting the interpolation algorithm according to the hue and brightness.

[0057] The division unit can adjust the mesh division timing based on a scene change in the video. For example, the mesh division is performed at the timing of a scene change to maintain the continuity of the video. The mesh division is performed before the scene change to smoothly transition to the next scene. The mesh division is performed after the scene change to maintain the consistency of the video. In this way, by adjusting the division timing based on the scene change, the continuity of the video can be maintained.

[0058] The projection unit can dynamically change the projection accuracy according to the speed of movement in the image. For example, in scenes with fast-moving images, high-accuracy projection is performed to capture the details of the movement. In scenes with slow-moving images, efficient projection is performed to save resources. In scenes with changing image movement, the projection accuracy is adjusted in real time according to the speed of the movement. This makes it possible to improve the quality of the image by changing the projection accuracy according to the speed of the movement.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The division unit divides the image into a grid. For example, the division unit divides an existing movie into a mesh and converts it into an image for spherical projection. Step 2: The completion unit uses the generation AI to complete the gaps in the video split by the division unit. For example, the completion unit uses the generation AI to complete the gaps in the video naturally. The completion unit can also use the generation AI to complete the video to reduce distortion. Step 3: The projection unit projects the image interpolated by the interpolation unit onto a spherical surface. For example, the projection unit accurately projects the interpolated image onto a spherical surface.

[0061] (Example 2) A system according to an embodiment of the present invention provides a planetarium movie viewing experience using a 360° screen. This system uses generative AI to create custom images to project onto a spherical surface, creating a sense of being immersed in the film. This creates a spectacular, dynamic visual experience and a sense of immersion. For example, the system divides an existing film into meshes, and then uses generative AI to process the images so that the gaps between them are filled in. The processed images are then projected onto a spherical surface. This process creates a sense of being immersed in the film. This system meets consumer demand for new visual experiences and makes watching movies in planetariums more engaging. Furthermore, the use of generative AI simplifies image processing and reduces costs, making it more likely to be commercially successful.

[0062] A planetarium movie viewing system according to an embodiment includes a division unit, a complement unit, and a projection unit. The division unit divides an image into a grid pattern. For example, the division unit divides an existing movie into a mesh pattern and converts it into an image for spherical projection. The complement unit uses a generation AI to complement gaps in the image divided by the division unit. For example, the complement unit uses the generation AI to naturally complement gaps in the image. The complement unit can also use the generation AI to perform complementation to reduce image distortion. The projection unit projects the image complemented by the complement unit onto a spherical surface. For example, the projection unit accurately projects the complemented image onto the spherical surface. This allows the planetarium movie viewing system according to an embodiment to provide the feeling of being inside the movie.

[0063] The completion unit can use a generation AI to complete gaps in the video. The generation AI, for example, completes gaps in the video naturally. To complete gaps in the video, the generation AI uses a model learned from training data. When completing gaps, the generation AI completes the gaps in a way that maintains the continuity of the video. The generation AI maintains the consistency of the video by using an algorithm for completing gaps. This allows the generation AI to complete gaps in the video naturally.

[0064] The completion unit can use a generating AI to perform completion to reduce distortion in the image. The generating AI performs completion to reduce distortion in the image, for example. The generating AI uses a model learned from training data to reduce distortion in the image. The generating AI maintains the consistency of the image by using an algorithm to reduce distortion. When performing completion to reduce distortion, the generating AI performs completion in a way that maintains the continuity of the image. In this way, by using the generating AI, distortion in the image can be minimized.

[0065] The division unit can divide an existing movie into mesh-like parts. The division unit, for example, divides an existing movie into mesh-like parts. The division unit selects an appropriate mesh size depending on the format and resolution of the movie. When dividing into mesh-like parts, the division unit performs the division so as to maintain the continuity of the image. The division unit maintains the consistency of the image by using an algorithm for dividing into mesh-like parts. In this way, by dividing an existing movie into mesh-like parts, it is possible to convert it into an image for spherical projection.

[0066] The projection unit can accurately project the interpolated image onto a spherical surface. For example, the projection unit accurately projects the interpolated image onto a spherical surface. The projection unit maintains the continuity of the image by adjusting the projection angle and positioning. The projection unit uses a projection algorithm to improve the accuracy of the projection. The projection unit performs projection so as to maintain the consistency of the image during projection. As a result, the continuity of the image can be maintained by accurately projecting the interpolated image onto a spherical surface.

[0067] The completion unit can use a generation AI to naturally complete gaps in the video. The generation AI, for example, naturally completes gaps in the video. The generation AI uses a model learned from training data to complete gaps in the video. When completing gaps, the generation AI performs the completion so as to maintain the continuity of the video. The generation AI maintains the consistency of the video by using an algorithm for completing gaps. This makes it possible to naturally complete gaps in the video by using the generation AI.

[0068] The segmentation unit can estimate the user's emotion and adjust the video segmentation method based on the estimated user's emotion. For example, when the user is excited, the segmentation unit segments the video using a fine mesh size to provide a more detailed video experience. When the user is relaxed, the segmentation unit segments the video using a larger mesh size to provide a more relaxed video experience. When the user is stressed, the segmentation unit segments the video using a simple mesh size to reduce visual strain. This allows for a more appropriate video experience to be provided by adjusting the video segmentation method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The segmentation unit can apply different mesh sizes based on the content of the video to perform appropriate segmentation. For example, the segmentation unit applies a fine mesh size to scenes with rapid movement in the video to perform detailed segmentation. The segmentation unit applies a larger mesh size to still scenes in the video to perform efficient segmentation. The segmentation unit applies an especially fine mesh size to important scenes in the video to perform detailed segmentation. This makes it possible to improve the quality of the video by performing optimal segmentation according to the content of the video.

[0070] The division unit can dynamically change the mesh division density according to the speed of movement in the video. For example, in scenes where the video is moving fast, the division unit increases the mesh division density to perform detailed division. In scenes where the video is moving slowly, the division unit decreases the mesh division density to perform efficient division. In scenes where the video movement changes, the division unit adjusts the mesh division density in real time according to the speed of the movement. In this way, by changing the division density according to the speed of movement in the video, the quality of the video can be improved.

[0071] The segmentation unit can prioritize segmentation of important scenes in the video and apply detailed meshes. For example, the segmentation unit applies a particularly fine mesh size to a climax scene in the video to perform detailed segmentation. The segmentation unit applies a fine mesh size to an emotional scene in the video to enhance the visual effect. The segmentation unit applies a fine mesh size to an action scene in the video to capture the details of the movement. In this way, by prioritizing segmentation of important scenes, the visual effect can be enhanced.

[0072] The segmentation unit can estimate the user's emotions and determine the priority of videos based on the estimated user emotions. For example, if the user is excited, the segmentation unit prioritizes dividing action scenes. If the user is relaxed, the segmentation unit prioritizes dividing landscape scenes. If the user is moved, the segmentation unit prioritizes dividing emotional scenes. This allows for determining the priority of videos according to the user's emotions, thereby providing a more appropriate video experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0073] The division unit can adjust the mesh division pattern based on the hue and brightness of the image. For example, in scenes with vivid hue in the image, the division unit applies a fine mesh size to perform detailed division. In scenes with high brightness in the image, the division unit applies a larger mesh size to perform efficient division. In scenes where the hue and brightness of the image change, the division unit adjusts the mesh division pattern in real time according to the hue and brightness. In this way, by adjusting the division pattern according to the hue and brightness, it is possible to improve the quality of the image.

[0074] The division unit can analyze audio information of the video and change the mesh division density according to the strength of the audio. For example, in scenes with strong audio, the division unit applies a fine mesh size to perform detailed division. In scenes with weak audio, the division unit applies a larger mesh size to perform efficient division. In scenes where the audio strength changes, the division unit adjusts the mesh division density in real time according to the audio strength. In this way, the quality of the video can be improved by changing the division density according to the audio strength.

[0075] The division unit can adjust the mesh division timing based on a scene change in the video. The division unit divides the mesh, for example, at the timing of a scene change, to maintain the continuity of the video. The division unit divides the mesh before a scene change, to smoothly transition to the next scene. The division unit divides the mesh after a scene change, to maintain the consistency of the video. In this way, by adjusting the division timing based on a scene change, the continuity of the video can be maintained.

[0076] The completion unit can estimate the user's emotions and adjust the completion algorithm based on the estimated user emotions. For example, if the user is relaxed, the generation AI of the completion unit applies a relaxed completion algorithm. If the user is excited, the generation AI of the completion unit applies a visually stimulating completion algorithm. If the user is stressed, the generation AI of the completion unit applies a simple completion algorithm that is less visually burdensome. This allows the completion algorithm to be adjusted according to the user's emotions, thereby providing a more appropriate video experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The completion unit can apply different completion algorithms based on the content of the video to perform appropriate completion. For example, in scenes with a lot of movement in the video, the generation AI applies a detailed completion algorithm. In still scenes in the video, the completion unit applies an efficient completion algorithm. In important scenes in the video, the generation AI applies a particularly detailed completion algorithm. This makes it possible to improve the quality of the video by performing optimal completion according to the content of the video.

[0078] The completion unit can dynamically change the accuracy of completion depending on the speed of video movement. For example, in scenes with fast video movement, the generation AI performs highly accurate completion. In scenes with slow video movement, the generation AI performs efficient completion. In scenes with changing video movement, the generation AI adjusts the accuracy of completion in real time depending on the speed of the movement. This makes it possible to improve video quality by changing the accuracy of completion depending on the speed of movement.

[0079] The completion unit can prioritize completion of important scenes in the video and apply detailed completion. For example, in the climax scene of the video, the generation AI performs particularly detailed completion. In the moving scenes of the video, the generation AI performs visually effective completion. In the action scenes of the video, the generation AI performs completion that captures the details of the movements. This makes it possible to enhance the visual effect by prioritizing completion of important scenes.

[0080] The completion unit can estimate the user's emotions and determine the priority of videos based on the estimated user emotions. For example, if the user is excited, the completion unit prioritizes completion of action scenes. If the user is relaxed, the completion unit prioritizes completion of landscape scenes. If the user is moved, the completion unit prioritizes completion of moving scenes. This allows for a more appropriate video experience by determining the priority of videos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The completion unit can adjust the completion algorithm based on the hue and brightness of the image. For example, in scenes with vivid hue, the generation AI of the completion unit applies a detailed completion algorithm. In scenes with high brightness, the generation AI of the completion unit applies an efficient completion algorithm. In scenes where the hue or brightness of the image changes, the generation AI of the completion unit adjusts the completion algorithm in real time according to the hue and brightness. This makes it possible to improve the quality of the image by adjusting the completion algorithm according to the hue and brightness.

[0082] The completion unit analyzes the audio information of the video and can change the accuracy of completion depending on the strength of the audio. For example, in scenes with strong audio, the generation AI performs high-precision completion. In scenes with weak audio, the generation AI performs efficient completion. In scenes where the audio strength changes, the generation AI adjusts the accuracy of completion in real time depending on the audio strength. This makes it possible to improve the quality of the video by changing the accuracy of completion depending on the audio strength.

[0083] The completion unit can adjust the timing of completion based on scene changes in the video. For example, the generation AI performs completion at the timing of a scene change in the completion unit, maintaining the continuity of the video. The generation AI performs completion before a scene change in the completion unit, ensuring a smooth transition to the next scene. The generation AI performs completion after a scene change in the completion unit, maintaining the consistency of the video. In this way, by adjusting the timing of completion based on scene changes, it is possible to maintain the continuity of the video.

[0084] The projection unit can estimate the user's emotion and adjust the projection method based on the estimated user's emotion. For example, if the user is excited, the projection unit provides a visually stimulating projection method. If the user is relaxed, the projection unit provides a relaxed projection method. If the user is stressed, the projection unit provides a simple, visually less burdensome projection method. This allows the projection method to be adjusted according to the user's emotion, thereby providing a more appropriate video experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] The projection unit can apply different projection algorithms based on the content of the video to perform appropriate projection. For example, the projection unit applies a detailed projection algorithm to scenes with a lot of movement in the video. The projection unit applies an efficient projection algorithm to still scenes in the video. The projection unit applies a particularly detailed projection algorithm to important scenes in the video. This makes it possible to improve the quality of the video by performing optimal projection according to the content of the video.

[0086] The projection unit can dynamically change the projection accuracy according to the speed of movement in the image. For example, in a scene with fast movement in the image, the projection unit performs high-accuracy projection to capture the details of the movement. In a scene with slow movement in the image, the projection unit performs efficient projection to save resources. In a scene with changing movement in the image, the projection unit adjusts the projection accuracy in real time according to the speed of the movement. This makes it possible to improve the quality of the image by changing the projection accuracy according to the speed of the movement.

[0087] The projection unit can prioritize projection of important scenes in the video and apply detailed projection. For example, the projection unit performs particularly detailed projection in the climax scene of the video to enhance the visual effect. The projection unit performs visually effective projection in moving scenes of the video. The projection unit performs projection that captures the details of the movements in action scenes of the video. In this way, by prioritizing projection of important scenes, the visual effect can be enhanced.

[0088] The projection unit can estimate the user's emotions and determine the priority of the images to be projected based on the estimated user's emotions. For example, if the user is excited, the projection unit preferentially projects action scenes. If the user is relaxed, the projection unit preferentially projects landscape scenes. If the user is moved, the projection unit preferentially projects moving scenes. This allows the user to prioritize images according to the user's emotions, thereby providing a more appropriate video experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The projection unit can adjust the projection algorithm based on the hue and brightness of the image. For example, the projection unit applies a detailed projection algorithm to a scene with vivid hue of the image. The projection unit applies an efficient projection algorithm to a scene with high brightness of the image. The projection unit adjusts the projection algorithm in real time according to the hue and brightness of the image in a scene where the hue and brightness of the image change. In this way, the quality of the image can be improved by adjusting the projection algorithm according to the hue and brightness.

[0090] The projection unit can analyze audio information of the video and change the precision of projection according to the strength of the audio. For example, in scenes with strong audio, the projection unit performs high-precision projection to improve the link between vision and audio. In scenes with weak audio, the projection unit performs efficient projection to save resources. In scenes where the audio strength changes, the projection unit adjusts the precision of projection in real time according to the strength of the audio. This makes it possible to improve the quality of the video by changing the precision of projection according to the strength of the audio.

[0091] The projection unit can adjust the timing of projection based on a scene change in the video. For example, the projection unit performs projection at the timing of a scene change, thereby maintaining the continuity of the video. The projection unit performs projection before a scene change, thereby smoothly transitioning to the next scene. The projection unit performs projection after a scene change, thereby maintaining the consistency of the video. In this way, by adjusting the timing of projection based on a scene change, the continuity of the video can be maintained. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned division unit, completion unit, and projection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the division unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the completion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the projection unit is realized by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned division unit, completion unit, and projection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the division unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the completion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the projection unit is realized by the output device 40 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned division unit, completion unit, and projection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the division unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the completion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the projection unit is realized by the output device 40 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned division unit, completion unit, and projection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the division unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the completion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the projection unit is realized by the output device 40 of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The division unit can track the user's gaze and adjust the image division method based on the gaze movement. For example, if the user focuses their gaze on a specific area, that area is divided into fine mesh sizes to provide a detailed image. Conversely, if the user moves their gaze, a wider area is divided into larger mesh sizes to provide a more comprehensive image. Furthermore, if the gaze moves quickly, the mesh size can be adjusted in real time to reduce visual strain. This makes it possible to provide a more appropriate video experience by dynamically adjusting the image division method according to the user's gaze.

[0094] The completion unit analyzes the audio information of the video and can adjust the completion algorithm based on the emotional tone of the audio. For example, if the audio has an emotional tone, the generation AI applies a visually effective completion algorithm. If the audio has a tense tone, the generation AI applies a detailed completion algorithm to increase the visual tension. If the audio has a relaxed tone, the generation AI applies a simple completion algorithm that is less visually burdensome. This allows the system to provide a more appropriate video experience by adjusting the completion algorithm according to the emotional tone of the audio.

[0095] The projection unit can monitor the user's heart rate and adjust the projection method based on heart rate fluctuations. For example, if the user's heart rate is increasing, a visually stimulating projection method is provided. If the user's heart rate is stable, a relaxed projection method is provided. If the user's heart rate is decreasing, a simple, visually less stressful projection method is provided. This allows the projection method to be adjusted according to the user's heart rate, providing a more appropriate visual experience.

[0096] The segmentation unit can apply different mesh patterns based on the content of the video to perform appropriate segmentation. For example, in scenes with a lot of movement in the video, a fine mesh pattern is applied to perform detailed segmentation. In still scenes in the video, a larger mesh pattern is applied to perform efficient segmentation. In important scenes in the video, a particularly fine mesh pattern is applied to perform detailed segmentation. This makes it possible to improve the quality of the video by performing optimal segmentation according to the content of the video.

[0097] The interpolation unit can adjust the interpolation algorithm based on the hue and brightness of the image. For example, in scenes with vivid hue, the generation AI applies a detailed interpolation algorithm. In scenes with high brightness, the generation AI applies an efficient interpolation algorithm. In scenes where the hue and brightness of the image change, the generation AI adjusts the interpolation algorithm in real time according to the hue and brightness. This allows for improved image quality by adjusting the interpolation algorithm according to the hue and brightness.

[0098] The projection unit can estimate the user's emotions and determine the priority of the images to be projected based on the estimated user's emotions. For example, if the user is excited, action scenes are preferentially projected. If the user is relaxed, landscape scenes are preferentially projected. If the user is moved, moving scenes are preferentially projected. This allows for a more appropriate video experience by determining the priority of images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] The division unit can adjust the mesh division timing based on a scene change in the video. For example, the mesh division is performed at the timing of a scene change to maintain the continuity of the video. The mesh division is performed before the scene change to smoothly transition to the next scene. The mesh division is performed after the scene change to maintain the consistency of the video. In this way, by adjusting the division timing based on the scene change, the continuity of the video can be maintained.

[0100] The completion unit can estimate the user's emotions and adjust the completion algorithm based on the estimated user emotions. For example, if the user is relaxed, the generation AI applies a relaxed completion algorithm. If the user is excited, the generation AI applies a visually stimulating completion algorithm. If the user is stressed, the generation AI applies a simple, visually less burdensome completion algorithm. This allows the completion algorithm to be adjusted according to the user's emotions, providing a more appropriate video experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] The projection unit can dynamically change the projection accuracy according to the speed of movement in the image. For example, in scenes with fast-moving images, high-accuracy projection is performed to capture the details of the movement. In scenes with slow-moving images, efficient projection is performed to save resources. In scenes with changing image movement, the projection accuracy is adjusted in real time according to the speed of the movement. This makes it possible to improve the quality of the image by changing the projection accuracy according to the speed of the movement.

[0102] The projection unit can estimate the user's emotions and adjust the projection method based on the estimated user emotions. For example, if the user is excited, a visually stimulating projection method is provided. If the user is relaxed, a relaxed projection method is provided. If the user is stressed, a simple and visually less stressful projection method is provided. This allows the projection method to be adjusted according to the user's emotions, providing a more appropriate video experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The division unit divides the image into a grid. For example, the division unit divides an existing movie into a mesh and converts it into an image for spherical projection. Step 2: The completion unit uses the generation AI to complete the gaps in the video split by the division unit. For example, the completion unit uses the generation AI to complete the gaps in the video naturally. The completion unit can also use the generation AI to complete the video to reduce distortion. Step 3: The projection unit projects the image interpolated by the interpolation unit onto a spherical surface. For example, the projection unit accurately projects the interpolated image onto a spherical surface.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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 AI 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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 AI 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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 AI 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

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

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

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

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

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

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

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

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

[0176] [Explanation of symbols]

[0177] 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 division unit that divides the image into a grid pattern; a completion unit that completes gaps in the video image divided by the division unit; a projection unit that projects the image interpolated by the interpolation unit onto a spherical surface; Equipped with A system characterized by:

2. The complementing unit Filling in gaps in images using generative AI 2. The system of claim 1.

3. The complementing unit Uses generative AI to perform interpolation to reduce image distortion 2. The system of claim 1.

4. The dividing unit Splitting an existing movie into a mesh 2. The system of claim 1.

5. The projection unit Accurately projecting the interpolated image onto a spherical surface 2. The system of claim 1.

6. The complementing unit Using generative AI to naturally fill in gaps in images 2. The system of claim 1.

7. The dividing unit Estimate the user's emotion and change the video segmentation method based on the estimated user emotion.

2. The system of claim 1.

8. The dividing unit Apply different mesh sizes based on the video content for optimal segmentation 2. The system of claim 1.

Images