system

Abstract

An object of a system according to an embodiment is to enable a user to experience or visit a place without physically visiting the place.SOLUTION: A system according to an embodiment includes an imaging unit, a generation unit, and a distribution unit. The photographing unit photographs the inside of the center. The generation unit generates a video captured by the imaging unit as VR content. The distribution unit distributes the VR content generated by the generation unit through the Internet.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem that it was not possible to experience or tour a specific location without physically visiting it.

[0005] The system according to the embodiment aims to enable people to experience or visit a specific place without physically visiting it. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera unit, a generator, and a distributor. The camera unit captures images of the interior of the center. The generator generates VR content from the images captured by the camera unit. The distributor distributes the VR content generated by the generator via the Internet. [Effects of the Invention]

[0007] The system according to the embodiment can enable people to experience or visit a specific place without physically visiting it. [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 converts the center, which currently has only one location in Tokyo, into a VR environment, enabling VR experiences and tours from all over the world. This system captures the interior of the center, generates the video as VR content, and distributes it over the Internet. For example, the interior of the center is captured using a high-precision camera and the video is generated as VR content. The generated VR content is then distributed over the Internet so that users can experience it using a VR device. This allows even users who find it difficult to physically visit the center to have a realistic experience. For example, each area of ​​the center is captured in detail to obtain 360-degree video. The captured video is then processed using a generation AI to provide a realistic VR experience. For example, the video is linked to allow users to move freely within the center, providing a seamless experience. The generated VR content is distributed over the Internet. Users can access the VR content via the Internet using a VR device and experience the center. For example, users can tour the center from home using a VR device. This system allows even users who find it difficult to physically visit the center to have a realistic experience. For example, users living overseas or those with limited mobility can tour the center in detail. In addition, the VR experience will help to widely communicate the center's appeal.

[0029] A VR experience system according to an embodiment includes a camera unit, a generator, and a distributor. The camera unit captures images of the interior of a center. Examples of the interior of a center include, but are not limited to, a data center, a research center, and an exhibition center. The camera unit, for example, uses a high-resolution camera to capture precise images of each area within the center. The camera unit can also capture a panoramic view of the center using a 360-degree camera. The camera unit can also use a drone to capture high locations or narrow spaces within the center. For example, the camera unit uses a high-resolution camera to capture detailed images of each area within the center. The 360-degree camera can also capture a panoramic view of the center. The drone can also capture high locations or narrow spaces within the center. The generator uses a generation AI to generate VR content from the images captured by the camera unit. The generation AI processes the images using technologies such as deep learning and GAN (generative artificial network). The generator, for example, uses the generation AI to seamlessly link the captured images to generate VR content in which a user can move freely. The generation unit can also use the generation AI to adjust the color tone and brightness of the image to provide a realistic visual effect. The generation unit can also use the generation AI to add interactive elements to the image, allowing users to freely explore the center. For example, the generation unit can use the generation AI to seamlessly connect captured images to generate VR content that allows users to move freely. The generation AI can also use the generation AI to adjust the color tone and brightness of the image to provide a realistic visual effect. The generation AI can also use the generation AI to add interactive elements to the image to allow users to freely explore the center. The distribution unit distributes the VR content generated by the generation unit via the Internet. The distribution unit can distribute the VR content in real time, for example, using streaming technology. The distribution unit can also use download technology to allow users to download and experience the VR content in advance. The distribution unit can also automatically adjust the image quality of the VR content according to the user's Internet connection speed.For example, the distribution unit can use streaming technology to distribute VR content in real time. Download technology can also be used to allow users to download VR content in advance and experience it. The system has a function to automatically adjust the image quality of the VR content depending on the user's Internet connection speed. As a result, the VR experience system according to the embodiment generates video of the center's interior as VR content and distributes it over the Internet, enabling VR experiences and tours from all over the world. For example, a user can tour the center from home using a VR device.

[0030] The imaging unit can precisely capture each area within the center using a high-resolution camera. Examples of high-resolution cameras include, but are not limited to, 4K cameras and 8K cameras. The imaging unit can precisely capture each area within the center using a high-resolution camera. For example, a 4K camera can be used to precisely capture each area within the center. An 8K camera can also be used to precisely capture each area within the center. Furthermore, the imaging unit can also capture each area within the center using a 360-degree camera. For example, a 360-degree camera can be used to capture a panoramic view of the center. This allows for the use of high-precision cameras to obtain detailed images and provide a realistic VR experience. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without AI. For example, the imaging unit can input video data acquired by a high-resolution camera into a generation AI and have the generation AI process the video data.

[0031] The generation unit can use a generation AI to process the video and provide a realistic VR experience. Examples of generation AI include, but are not limited to, deep learning and GAN (generative artificial network). For example, the generation unit can use a generation AI to seamlessly connect captured video and generate VR content in which a user can move freely. For example, the generation AI can seamlessly connect captured video and generate VR content in which a user can move freely. The generation unit can also use the generation AI to adjust the color tone and brightness of the video to provide realistic visual effects. For example, the generation AI can adjust the color tone and brightness of the video to provide realistic visual effects. Furthermore, the generation unit can use the generation AI to add interactive elements to the video so that a user can freely explore the center. For example, the generation AI can add interactive elements to the video so that a user can freely explore the center. As a result, the use of the generation AI improves the accuracy of the video processing, providing a more realistic VR experience. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input video data to the generation AI and have the generation AI process the video data.

[0032] The distribution unit can enable a user to access VR content via the Internet using a VR device. Examples of VR devices include, but are not limited to, a head-mounted display (HMD) and VR goggles. The distribution unit can distribute VR content in real time using, for example, streaming technology. For example, streaming technology can be used to distribute VR content in real time. The distribution unit can also use download technology to allow a user to download VR content in advance and experience it. For example, download technology can be used to allow a user to download VR content in advance and experience it. Furthermore, the distribution unit has a function to automatically adjust the image quality of the VR content according to the user's Internet connection speed. For example, if the user's Internet connection speed is slow, the image quality can be lowered to ensure smooth distribution. If the user's Internet connection speed is fast, the image quality can be increased to ensure high-quality distribution. If the user's Internet connection speed fluctuates, the image quality can be adjusted in real time to ensure smooth distribution. This allows users to access VR content via the Internet using a VR device, allowing even users who find it difficult to physically visit a center to enjoy a realistic experience. Some or all of the above-described processing by the distribution unit may be performed using, for example, AI, or may be performed without AI. For example, the distribution department can input the user's internet connection speed into the AI ​​and have the AI ​​adjust the image quality.

[0033] The image capture unit can simultaneously acquire environmental data on the temperature and humidity within the center during image capture and reflect the data in the VR experience. Temperature and humidity environmental data includes, but is not limited to, temperature and humidity sensors. For example, the image capture unit can acquire temperature data for each area during image capture and reproduce the temperature during the VR experience. For example, the image capture unit can acquire temperature data for each area during image capture and reproduce the temperature during the VR experience. The image capture unit can also acquire humidity data for each area during image capture and reproduce the humidity during the VR experience. For example, the image capture unit can acquire humidity data for each area during image capture and reproduce the humidity during the VR experience. Furthermore, the image capture unit can acquire wind speed data for each area during image capture and reproduce the wind speed during the VR experience. For example, the image capture unit can acquire wind speed data for each area during image capture and reproduce the wind speed during the VR experience. This allows the acquisition of environmental data and the reflection of the data in the VR experience to provide a more realistic experience. Some or all of the above-described processing in the image capture unit may be performed using, for example, AI, or without AI. For example, the photographing unit can input environmental data acquired by a temperature sensor or humidity sensor into the generation AI and have the generation AI analyze the environmental data.

[0034] The camera unit can record audio data within the center with high accuracy during filming, providing a realistic audio experience. High-accuracy audio data recording techniques include, but are not limited to, high-sensitivity microphones and noise-canceling technology. For example, the camera unit can record background sounds from each area with high accuracy during filming and reproduce those sounds during the VR experience. For example, the camera unit can record background sounds from each area with high accuracy during filming and reproduce those sounds during the VR experience. The camera unit can also record sounds from specific exhibits or experiments with high accuracy during filming and reproduce those sounds during the VR experience. For example, the camera unit can record sounds from specific exhibits or experiments with high accuracy during filming and reproduce those sounds during the VR experience. Furthermore, the camera unit can record the voice of a guide within the center with high accuracy during filming and reproduce that voice during the VR experience. For example, the camera unit can record the voice of a guide within the center with high accuracy during filming and reproduce that voice during the VR experience. This allows for high-accuracy audio data recording to provide a more realistic audio experience. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capturing unit may input audio data captured by a high-sensitivity microphone to the generation AI, and have the generation AI analyze the audio data.

[0035] During filming, the filming unit can film dynamic events within the center in real time and incorporate them into the VR content. Examples of dynamic events include, but are not limited to, live performances and experimental demonstrations. For example, the filming unit can film an experimental demonstration in real time and recreate the demonstration during the VR experience. For example, the filming unit can film an experimental demonstration in real time and recreate the demonstration during the VR experience. The filming unit can also film special events within the center in real time and recreate the event during the VR experience. For example, the filming unit can film a special event within the center in real time and recreate the event during the VR experience. Furthermore, the filming unit can film a guided tour within the center in real time and recreate the tour during the VR experience. For example, the filming unit can film a guided tour within the center in real time and recreate the tour during the VR experience. This allows for a more immersive experience by filming dynamic events in real time and incorporating them into the VR content. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capturing unit may input video data captured in real time to the generation AI, and have the generation AI process the video data.

[0036] During photography, the photography unit can capture multiple versions under different lighting conditions within the center, allowing the user to select from them. Photography under different lighting conditions can include, but is not limited to, different lighting types, brightness, and color temperatures. The photography unit can provide, for example, a version captured under bright lighting conditions and a version captured under dark lighting conditions. For example, a version captured under bright lighting conditions and a version captured under dark lighting conditions can be provided. The photography unit can also provide a version captured under lighting conditions using natural light and a version captured under lighting conditions using artificial lighting. For example, a version captured under lighting conditions using natural light and a version captured under lighting conditions using artificial lighting can be provided. The photography unit can also provide a version captured under lighting conditions that focus on a specific exhibit. For example, a version captured under lighting conditions that focus on a specific exhibit can be provided. This allows multiple versions to be captured under different lighting conditions, allowing the user to select their preferred version. Some or all of the above-described processing by the photography unit can be performed, for example, using AI or without AI. For example, the shooting unit can input video data shot under different lighting conditions into the generation AI and have the generation AI process the video data.

[0037] During filming, the filming unit can film guided tours focusing on specific areas within the center, allowing users to select them. Guided tours focusing on specific areas include, but are not limited to, a science experiment area, a historical exhibit area, and an interactive exhibit area. For example, the filming unit can film a guided tour focusing on the science experiment area. The filming unit can also film a guided tour focusing on the historical exhibit area. For example, the filming unit can film a guided tour focusing on the historical exhibit area. Furthermore, the filming unit can also film a guided tour focusing on the interactive exhibit area. For example, the filming unit can film a guided tour focusing on the interactive exhibit area. By filming a guided tour focusing on a specific area, users can select an area of ​​interest. Some or all of the above-described processing in the filming unit may be performed using, for example, AI, or may be performed without AI. For example, the filming unit can input video data of a guided tour focusing on a specific area into a generation AI and have the generation AI process the video data.

[0038] During filming, the filming unit can add narration explaining the historical background of the center and how it was founded. Examples of historical background and how it was founded include, but are not limited to, the year of establishment, important events, and related people. The filming unit can add narration explaining how the center was founded. For example, the filming unit can add narration explaining how the center was founded. The filming unit can also add narration explaining the historical background of each exhibit. For example, the filming unit can add narration introducing important events and people at the center. Adding narration explaining the historical background and how it was founded helps users understand the center's history and background. Narration can be added using, for example, voice synthesis technology or a professional narrator. Some or all of the above-described processing by the filming unit may be performed using, for example, AI, or without AI. For example, the filming unit can input the content of the narration into a generation AI and have the generation AI perform voice synthesis.

[0039] During generation, the generation unit can add interactive elements based on gaze movement using the user's gaze tracking data. Examples of gaze tracking data include, but are not limited to, gaze tracking sensors and data analysis algorithms. For example, when a user gazes at a specific exhibit, the generation unit can display detailed information about the exhibit. For example, when a user gazes at a specific exhibit, the generation unit can display detailed information about the exhibit. Furthermore, when a user gazes at a specific area, the generation unit can add interactive elements related to the area. For example, when a user gazes at a specific area, the generation unit can add interactive elements related to the area. Furthermore, the generation unit can automatically select the next content to display based on the user's gaze movement. For example, the generation unit can automatically select the next content to display based on the user's gaze movement. Thus, by using the gaze tracking data, interactive elements based on the user's gaze movement can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the gaze tracking data to a generation AI and cause the generation AI to add interactive elements.

[0040] During generation, the generation unit may add a function to display detailed information about specific facilities or exhibits in the center as a pop-up. Examples of detailed information about specific facilities or exhibits include, but are not limited to, technical specifications, historical background, and usage methods. For example, when a user approaches a specific exhibit, the generation unit may display detailed information about the exhibit as a pop-up. For example, when a user approaches a specific exhibit, the generation unit may display detailed information about the exhibit as a pop-up. Furthermore, when a user approaches a specific piece of equipment, the generation unit may display detailed information about the equipment as a pop-up. For example, when a user approaches a specific piece of equipment, the generation unit may display detailed information about the equipment as a pop-up. Furthermore, when a user approaches a specific area, the generation unit may display detailed information about the area as a pop-up. For example, when a user approaches a specific area, the generation unit may display detailed information about the area as a pop-up. This allows the user to easily obtain detailed information by displaying detailed information about specific facilities or exhibits as a pop-up. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information about a particular piece of equipment or exhibit into the generation AI and have the generation AI generate a pop-up display.

[0041] During generation, the generation unit can add interactive elements that allow the user to not only move freely within the center but also perform specific actions. Specific actions include, but are not limited to, object manipulation and movement within the virtual environment. For example, when a user approaches a specific exhibit, the generation unit can display an interactive quiz about the exhibit. For example, when a user approaches a specific exhibit, the generation unit can display an interactive quiz about the exhibit. Furthermore, when a user approaches a specific area, the generation unit can provide an interactive guided tour about the area. For example, when a user approaches a specific area, the generation unit can provide an interactive guided tour about the area. Furthermore, when a user approaches a specific piece of equipment, the generation unit can provide an interactive element that allows the user to operate the equipment. For example, when a user approaches a specific piece of equipment, the generation unit can provide an interactive element that allows the user to operate the equipment. This allows the user to not only move freely within the center but also add interactive elements that allow the user to perform specific actions, thereby providing a more immersive experience. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input data about a particular action into the generation AI and cause the generation AI to generate an interactive element.

[0042] The generation unit can add narration and subtitles in multiple languages ​​during generation to accommodate international users. Narrations and subtitles in multiple languages ​​include, but are not limited to, English, Japanese, and Spanish. The generation unit can provide narration in multiple languages, such as English, Spanish, and Chinese. The generation unit can also automatically display subtitles based on a language selected by a user. For example, the generation unit can automatically display subtitles based on a language selected by a user. Furthermore, the generation unit can provide a language switching function when a user speaks multiple languages. For example, when a user speaks multiple languages, the generation unit can provide a language switching function. By adding narration and subtitles in multiple languages, international users can be accommodated. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the content of the narration and subtitles into a generation AI and have the generation AI perform speech synthesis and subtitle generation.

[0043] During generation, the generation unit can add quizzes or mini-games related to specific areas within the center to enhance the educational element. Quizzes and mini-games include, but are not limited to, question formats, game rules, and interfaces. For example, the generation unit can provide a quiz related to the science experiment area to allow users to enjoy themselves while learning. For example, the generation unit can provide a quiz related to the science experiment area to allow users to enjoy themselves while learning. The generation unit can also provide a mini-game related to the historical exhibit area to allow users to enjoy themselves while learning. For example, the generation unit can provide a mini-game related to the historical exhibit area to allow users to enjoy themselves while learning. Furthermore, the generation unit can provide a quiz or mini-game related to the interactive exhibit area to allow users to enjoy themselves while learning. For example, the generation unit can provide a quiz or mini-game related to the interactive exhibit area to allow users to enjoy themselves while learning. By adding quizzes or mini-games, the educational element can be enhanced and the user can enjoy themselves while learning. Some or all of the above-described processing by the generation unit may be performed using, or without, AI. For example, the generation unit can input the content of the quiz or mini-game into a generation AI and cause the generation AI to generate the quiz or mini-game.

[0044] During generation, the generation unit can add social functions that allow users to communicate with other users in real time. Examples of social functions include, but are not limited to, a chat function, a voice call, and a video call. The generation unit, for example, provides a function that allows users to chat with other users. For example, the generation unit can provide a function that allows users to chat with other users. The generation unit can also provide a function that allows users to make voice calls with other users. For example, the generation unit can provide a function that allows users to make video calls with other users. By adding the social functions, users can communicate with other users in real time. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the social functions into a generation AI and cause the generation AI to generate the social functions.

[0045] The distribution unit may add a function to automatically adjust the image quality of the VR content according to the user's Internet connection speed during distribution. Internet connection speeds include, but are not limited to, Mbps and connection stability. For example, when the user's Internet connection speed is slow, the distribution unit may lower the image quality to provide smooth distribution. For example, when the user's Internet connection speed is slow, the distribution unit may lower the image quality to provide smooth distribution. Furthermore, when the user's Internet connection speed is fast, the distribution unit may increase the image quality to provide high-quality distribution. For example, when the user's Internet connection speed is fast, the distribution unit may increase the image quality to provide high-quality distribution. Furthermore, when the user's Internet connection speed fluctuates, the distribution unit may adjust the image quality in real time to provide smooth distribution. For example, when the user's Internet connection speed fluctuates, the image quality may be adjusted in real time to provide smooth distribution. This automatically adjusting the image quality according to the Internet connection speed enables smooth distribution. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the user's internet connection speed into the generation AI and have the generation AI adjust the image quality.

[0046] The distribution unit may add a function to analyze a user's viewing history and recommend related VR content during distribution. Examples of methods for analyzing viewing history include, but are not limited to, data mining and machine learning algorithms. The distribution unit may recommend related VR content based on content previously viewed by the user. For example, related VR content can be recommended based on content previously viewed by the user. The distribution unit may also recommend new VR content that may be of interest to the user based on the user's viewing history. For example, new VR content that may be of interest to the user based on the user's viewing history can be recommended. Furthermore, the distribution unit may recommend related, highly rated VR content based on ratings of content previously viewed by the user. For example, related, highly rated VR content can be recommended based on ratings of content previously viewed by the user. This allows content that meets the user's interests to be provided by analyzing the viewing history and recommending related VR content. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without AI. For example, the distribution unit may input the user's viewing history data into a generation AI and cause the generation AI to recommend content.

[0047] During distribution, the distribution unit can add an interface that allows the user to provide feedback in real time during the VR experience. Examples of methods for providing feedback include, but are not limited to, a feedback form, voice input, and gesture recognition. The distribution unit, for example, provides an interface that allows the user to input comments in real time during the VR experience. For example, the distribution unit can provide an interface that allows the user to input comments in real time during the VR experience. The distribution unit can also provide an interface that allows the user to make ratings in real time during the VR experience. For example, the distribution unit can provide an interface that allows the user to make ratings in real time during the VR experience. Furthermore, the distribution unit can also provide an interface that allows the user to post questions in real time during the VR experience. For example, the distribution unit can provide an interface that allows the user to post questions in real time during the VR experience. By adding an interface that allows feedback to be provided in real time, the user's opinions can be reflected immediately. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI. For example, the distribution unit may input user feedback data into a generation AI and cause the generation AI to analyze the feedback.

[0048] The distribution unit may add a function that allows a user to record a VR experience and play it back later during distribution. Recording a VR experience may involve, but is not limited to, recording software, storage types, and the like. The distribution unit may provide, for example, a function that allows a user to record a VR experience and play it back later to re-experience it. For example, a function that allows a user to record a VR experience and play it back later to re-experience it. The distribution unit may also provide a function that allows a user to record a specific scene and play it back later to check the details. For example, a function that allows a user to record a specific scene and play it back later to check the details. The distribution unit may also provide a function that allows a user to record a VR experience and share it with other users. For example, a function that allows a user to record a VR experience and share it with other users may be provided. By adding the function that allows a user to record a VR experience and play it back later, the user can look back on the experience. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without AI. For example, the distribution unit may input recording data to a generation AI and have the generation AI analyze the recording.

[0049] The distribution unit may add a social sharing function during distribution that allows a user to share a VR experience with other users. The social sharing function may include, but is not limited to, a sharing platform and a sharing method. For example, the distribution unit may provide a function that allows a user to share a VR experience on social media. For example, the distribution unit may provide a function that allows a user to share a VR experience with friends in real time. For example, the distribution unit may provide a function that allows a user to share a VR experience with friends in real time. Furthermore, the distribution unit may provide a function that allows a user to record a VR experience and share it later with other users. For example, the distribution unit may provide a function that allows a user to record a VR experience and share it later with other users. By adding the social sharing function, the user can share the experience with other users. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input social sharing data into a generation AI and cause the generation AI to generate a sharing function.

[0050] The distribution unit may add a function that allows a user to request detailed information about a specific area or exhibit during the VR experience during distribution. Requesting detailed information may be performed, for example, by voice input, text input, gesture recognition, or the like, but is not limited to these examples. The distribution unit may provide, for example, a function that allows a user to request detailed information about a specific exhibit. The distribution unit may also provide a function that allows a user to request detailed information about a specific area. The distribution unit may also provide a function that allows a user to request detailed information about a specific facility. By adding a function that allows a user to request detailed information about a specific area or exhibit, the user can easily obtain information of interest. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or without AI. For example, the distribution unit may input the user's request data into a generation AI and cause the generation AI to provide detailed information.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The camera unit can use the user's gaze tracking data to add interactive elements in specific areas of the center in response to gaze movements. For example, if a user looks at a specific exhibit, detailed information about that exhibit can be displayed. Also, if a user looks at a specific area, interactive elements related to that area can be added. Furthermore, the next content to be displayed can be automatically selected in response to the user's gaze movements. In this way, by using the gaze tracking data, interactive elements can be provided in response to the user's gaze movements.

[0053] During generation, the generation unit can add a function to pop up detailed information about specific equipment or exhibits in the center. For example, when a user approaches a specific exhibit, detailed information about the exhibit can be pop up. Also, when a user approaches specific equipment, detailed information about the equipment can be pop up. Furthermore, when a user approaches a specific area, detailed information about the area can be pop up. In this way, by popping up detailed information about specific equipment or exhibits, the user can easily obtain detailed information.

[0054] During generation, the generation unit can add interactive elements that not only allow the user to move freely within the center but also allow the user to perform specific actions. For example, when the user approaches a specific exhibit, an interactive quiz about the exhibit can be displayed. Also, when the user approaches a specific area, an interactive guided tour about the area can be provided. Furthermore, when the user approaches specific equipment, an interactive element that allows the user to operate the equipment can be provided. In this way, a more immersive experience can be provided by adding interactive elements that not only allow the user to move freely within the center but also allow the user to perform specific actions.

[0055] The distribution unit can add a function to analyze the user's viewing history and recommend related VR content during distribution. For example, it can recommend related VR content based on content the user has viewed in the past. It can also recommend new VR content that the user may be interested in based on the user's viewing history. Furthermore, it can recommend related, highly rated VR content based on the ratings of content the user has viewed. This makes it possible to provide content that matches the user's interests by analyzing the viewing history and recommending related VR content.

[0056] During distribution, the distribution unit can add an interface that allows the user to provide feedback in real time during the VR experience. For example, an interface can be provided that allows the user to input comments in real time during the VR experience. An interface can also be provided that allows the user to provide ratings in real time during the VR experience. Furthermore, an interface can be provided that allows the user to post questions in real time during the VR experience. By adding an interface that allows feedback to be provided in real time, user opinions can be reflected immediately.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The photography team photographs the interior of the center, which includes a data center, research center, and exhibition center. The photography team uses high-resolution cameras to capture precise images of each area within the center. They can also use 360-degree cameras to capture a panoramic view of the center. They can also use drones to capture high places and narrow spaces within the center. Step 2: The generation unit uses a generation AI to generate VR content from the footage captured by the capture unit. The generation AI processes the footage using technologies such as deep learning and GAN (generative artificial network). The generation unit seamlessly connects the captured footage to generate VR content that users can move freely through. It can also adjust the color tone and brightness of the footage to provide a realistic visual effect. It can also add interactive elements to the footage, allowing users to freely explore the center. Step 3: The distribution unit distributes the VR content generated by the generation unit via the Internet. The distribution unit distributes the VR content in real time using streaming technology. It can also use download technology to allow users to download the VR content in advance and experience it. Furthermore, it has a function to automatically adjust the image quality of the VR content according to the user's Internet connection speed.

[0059] (Example 2) A system according to an embodiment of the present invention converts the center, which currently has only one location in Tokyo, into a VR environment, enabling VR experiences and tours from all over the world. This system captures the interior of the center, generates the video as VR content, and distributes it over the Internet. For example, the interior of the center is captured using a high-precision camera and the video is generated as VR content. The generated VR content is then distributed over the Internet so that users can experience it using a VR device. This allows even users who find it difficult to physically visit the center to have a realistic experience. For example, each area of ​​the center is captured in detail to obtain 360-degree video. The captured video is then processed using a generation AI to provide a realistic VR experience. For example, the video is linked to allow users to move freely within the center, providing a seamless experience. The generated VR content is distributed over the Internet. Users can access the VR content via the Internet using a VR device and experience the center. For example, users can tour the center from home using a VR device. This system allows even users who find it difficult to physically visit the center to have a realistic experience. For example, users living overseas or those with limited mobility can tour the center in detail. In addition, the VR experience will help to widely communicate the center's appeal.

[0060] A VR experience system according to an embodiment includes a camera unit, a generator, and a distributor. The camera unit captures images of the interior of a center. Examples of the interior of a center include, but are not limited to, a data center, a research center, and an exhibition center. The camera unit, for example, uses a high-resolution camera to capture precise images of each area within the center. The camera unit can also capture a panoramic view of the center using a 360-degree camera. The camera unit can also use a drone to capture high locations or narrow spaces within the center. For example, the camera unit uses a high-resolution camera to capture detailed images of each area within the center. The 360-degree camera can also capture a panoramic view of the center. The drone can also capture high locations or narrow spaces within the center. The generator uses a generation AI to generate VR content from the images captured by the camera unit. The generation AI processes the images using technologies such as deep learning and GAN (generative artificial network). The generator, for example, uses the generation AI to seamlessly link the captured images to generate VR content in which a user can move freely. The generation unit can also use the generation AI to adjust the color tone and brightness of the image to provide a realistic visual effect. The generation unit can also use the generation AI to add interactive elements to the image, allowing users to freely explore the center. For example, the generation unit can use the generation AI to seamlessly connect captured images to generate VR content that allows users to move freely. The generation AI can also use the generation AI to adjust the color tone and brightness of the image to provide a realistic visual effect. The generation AI can also use the generation AI to add interactive elements to the image to allow users to freely explore the center. The distribution unit distributes the VR content generated by the generation unit via the Internet. The distribution unit can distribute the VR content in real time, for example, using streaming technology. The distribution unit can also use download technology to allow users to download and experience the VR content in advance. The distribution unit can also automatically adjust the image quality of the VR content according to the user's Internet connection speed.For example, the distribution unit can use streaming technology to distribute VR content in real time. Download technology can also be used to allow users to download VR content in advance and experience it. The system has a function to automatically adjust the image quality of the VR content depending on the user's Internet connection speed. As a result, the VR experience system according to the embodiment generates video of the center's interior as VR content and distributes it over the Internet, enabling VR experiences and tours from all over the world. For example, a user can tour the center from home using a VR device.

[0061] The imaging unit can precisely capture each area within the center using a high-resolution camera. Examples of high-resolution cameras include, but are not limited to, 4K cameras and 8K cameras. The imaging unit can precisely capture each area within the center using a high-resolution camera. For example, a 4K camera can be used to precisely capture each area within the center. An 8K camera can also be used to precisely capture each area within the center. Furthermore, the imaging unit can also capture each area within the center using a 360-degree camera. For example, a 360-degree camera can be used to capture a panoramic view of the center. This allows for the use of high-precision cameras to obtain detailed images and provide a realistic VR experience. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without AI. For example, the imaging unit can input video data acquired by a high-resolution camera into a generation AI and have the generation AI process the video data.

[0062] The generation unit can use a generation AI to process the video and provide a realistic VR experience. Examples of generation AI include, but are not limited to, deep learning and GAN (generative artificial network). For example, the generation unit can use a generation AI to seamlessly connect captured video and generate VR content in which a user can move freely. For example, the generation AI can seamlessly connect captured video and generate VR content in which a user can move freely. The generation unit can also use the generation AI to adjust the color tone and brightness of the video to provide realistic visual effects. For example, the generation AI can adjust the color tone and brightness of the video to provide realistic visual effects. Furthermore, the generation unit can use the generation AI to add interactive elements to the video so that a user can freely explore the center. For example, the generation AI can add interactive elements to the video so that a user can freely explore the center. As a result, the use of the generation AI improves the accuracy of the video processing, providing a more realistic VR experience. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input video data to the generation AI and have the generation AI process the video data.

[0063] The distribution unit can enable a user to access VR content via the Internet using a VR device. Examples of VR devices include, but are not limited to, a head-mounted display (HMD) and VR goggles. The distribution unit can distribute VR content in real time using, for example, streaming technology. For example, streaming technology can be used to distribute VR content in real time. The distribution unit can also use download technology to allow a user to download VR content in advance and experience it. For example, download technology can be used to allow a user to download VR content in advance and experience it. Furthermore, the distribution unit has a function to automatically adjust the image quality of the VR content according to the user's Internet connection speed. For example, if the user's Internet connection speed is slow, the image quality can be lowered to ensure smooth distribution. If the user's Internet connection speed is fast, the image quality can be increased to ensure high-quality distribution. If the user's Internet connection speed fluctuates, the image quality can be adjusted in real time to ensure smooth distribution. This allows users to access VR content via the Internet using a VR device, allowing even users who find it difficult to physically visit a center to enjoy a realistic experience. Some or all of the above-described processing by the distribution unit may be performed using, for example, AI, or may be performed without AI. For example, the distribution department can input the user's internet connection speed into the AI ​​and have the AI ​​adjust the image quality.

[0064] The camera unit can estimate the user's emotions and prioritize the areas to be photographed based on the estimated user's emotions. For example, if the user is excited, the camera unit can prioritize photographing the most popular areas in the center. For example, if the user is excited, the camera unit can prioritize photographing the most popular areas in the center. Furthermore, if the user is relaxed, the camera unit can prioritize photographing quiet or relaxing areas. For example, if the user is relaxed, the camera unit can prioritize photographing quiet or relaxing areas. Furthermore, if the user is curious, the camera unit can prioritize photographing particularly interesting exhibits or experimental areas. For example, if the user is curious, the camera unit can prioritize photographing particularly interesting exhibits or experimental areas. This allows for a more personalized VR experience by prioritizing the areas to be photographed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capture unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0065] The image capture unit can simultaneously acquire environmental data on the temperature and humidity within the center during image capture and reflect the data in the VR experience. Temperature and humidity environmental data includes, but is not limited to, temperature and humidity sensors. For example, the image capture unit can acquire temperature data for each area during image capture and reproduce the temperature during the VR experience. For example, the image capture unit can acquire temperature data for each area during image capture and reproduce the temperature during the VR experience. The image capture unit can also acquire humidity data for each area during image capture and reproduce the humidity during the VR experience. For example, the image capture unit can acquire humidity data for each area during image capture and reproduce the humidity during the VR experience. Furthermore, the image capture unit can acquire wind speed data for each area during image capture and reproduce the wind speed during the VR experience. For example, the image capture unit can acquire wind speed data for each area during image capture and reproduce the wind speed during the VR experience. This allows the acquisition of environmental data and the reflection of the data in the VR experience to provide a more realistic experience. Some or all of the above-described processing in the image capture unit may be performed using, for example, AI, or without AI. For example, the photographing unit can input environmental data acquired by a temperature sensor or humidity sensor into the generation AI and have the generation AI analyze the environmental data.

[0066] The camera unit can record audio data within the center with high accuracy during filming, providing a realistic audio experience. High-accuracy audio data recording techniques include, but are not limited to, high-sensitivity microphones and noise-canceling technology. For example, the camera unit can record background sounds from each area with high accuracy during filming and reproduce those sounds during the VR experience. For example, the camera unit can record background sounds from each area with high accuracy during filming and reproduce those sounds during the VR experience. The camera unit can also record sounds from specific exhibits or experiments with high accuracy during filming and reproduce those sounds during the VR experience. For example, the camera unit can record sounds from specific exhibits or experiments with high accuracy during filming and reproduce those sounds during the VR experience. Furthermore, the camera unit can record the voice of a guide within the center with high accuracy during filming and reproduce that voice during the VR experience. For example, the camera unit can record the voice of a guide within the center with high accuracy during filming and reproduce that voice during the VR experience. This allows for high-accuracy audio data recording to provide a more realistic audio experience. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capturing unit may input audio data captured by a high-sensitivity microphone to the generation AI, and have the generation AI analyze the audio data.

[0067] During filming, the filming unit can film dynamic events within the center in real time and incorporate them into the VR content. Examples of dynamic events include, but are not limited to, live performances and experimental demonstrations. For example, the filming unit can film an experimental demonstration in real time and recreate the demonstration during the VR experience. For example, the filming unit can film an experimental demonstration in real time and recreate the demonstration during the VR experience. The filming unit can also film special events within the center in real time and recreate the event during the VR experience. For example, the filming unit can film a special event within the center in real time and recreate the event during the VR experience. Furthermore, the filming unit can film a guided tour within the center in real time and recreate the tour during the VR experience. For example, the filming unit can film a guided tour within the center in real time and recreate the tour during the VR experience. This allows for a more immersive experience by filming dynamic events in real time and incorporating them into the VR content. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capturing unit may input video data captured in real time to the generation AI, and have the generation AI process the video data.

[0068] The image capture unit can estimate the user's emotions and adjust the time period for capturing images based on the estimated user emotions. For example, if the user is relaxed, the image capture unit can capture images during a quiet time period. For example, if the user is relaxed, the image capture unit can capture images during a quiet time period. Furthermore, if the user is excited, the image capture unit can capture images during a lively time period. For example, if the user is excited, the image capture unit can capture images during a lively time period. Furthermore, if the user is curious, the image capture unit can capture images during a time period when a specific event is taking place. For example, if the user is curious, the image capture unit can capture images during a time period when a specific event is taking place. This allows for adjusting the time period for capturing images based on the user's emotions to provide a more personalized VR experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the image capture unit may be performed using, for example, AI, or without AI. For example, the image capture unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion.

[0069] During photography, the photography unit can capture multiple versions under different lighting conditions within the center, allowing the user to select from them. Photography under different lighting conditions can include, but is not limited to, different lighting types, brightness, and color temperatures. The photography unit can provide, for example, a version captured under bright lighting conditions and a version captured under dark lighting conditions. For example, a version captured under bright lighting conditions and a version captured under dark lighting conditions can be provided. The photography unit can also provide a version captured under lighting conditions using natural light and a version captured under lighting conditions using artificial lighting. For example, a version captured under lighting conditions using natural light and a version captured under lighting conditions using artificial lighting can be provided. The photography unit can also provide a version captured under lighting conditions that focus on a specific exhibit. For example, a version captured under lighting conditions that focus on a specific exhibit can be provided. This allows multiple versions to be captured under different lighting conditions, allowing the user to select their preferred version. Some or all of the above-described processing by the photography unit can be performed, for example, using AI or without AI. For example, the shooting unit can input video data shot under different lighting conditions into the generation AI and have the generation AI process the video data.

[0070] During filming, the filming unit can film guided tours focusing on specific areas within the center, allowing users to select them. Guided tours focusing on specific areas include, but are not limited to, a science experiment area, a historical exhibit area, and an interactive exhibit area. For example, the filming unit can film a guided tour focusing on the science experiment area. The filming unit can also film a guided tour focusing on the historical exhibit area. For example, the filming unit can film a guided tour focusing on the historical exhibit area. Furthermore, the filming unit can also film a guided tour focusing on the interactive exhibit area. For example, the filming unit can film a guided tour focusing on the interactive exhibit area. By filming a guided tour focusing on a specific area, users can select an area of ​​interest. Some or all of the above-described processing in the filming unit may be performed using, for example, AI, or may be performed without AI. For example, the filming unit can input video data of a guided tour focusing on a specific area into a generation AI and have the generation AI process the video data.

[0071] During filming, the filming unit can add narration explaining the historical background of the center and how it was founded. Examples of historical background and how it was founded include, but are not limited to, the year of establishment, important events, and related people. The filming unit can add narration explaining how the center was founded. For example, the filming unit can add narration explaining how the center was founded. The filming unit can also add narration explaining the historical background of each exhibit. For example, the filming unit can add narration introducing important events and people at the center. Adding narration explaining the historical background and how it was founded helps users understand the center's history and background. Narration can be added using, for example, voice synthesis technology or a professional narrator. Some or all of the above-described processing by the filming unit may be performed using, for example, AI, or without AI. For example, the filming unit can input the content of the narration into a generation AI and have the generation AI perform voice synthesis.

[0072] The generation unit can estimate the user's emotions and adjust the difficulty level and level of detail of the VR content based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can provide VR content including detailed explanations. For example, if the user is relaxed, the generation unit can provide VR content including detailed explanations. Furthermore, if the user is excited, the generation unit can provide VR content including many interactive elements. For example, if the user is excited, the generation unit can provide VR content including many interactive elements. Furthermore, if the user is a beginner, the generation unit can provide VR content including simple explanations. For example, if the user is a beginner, the generation unit can provide VR content including simple explanations. This allows for adjusting the difficulty level and level of detail of the VR content based on the user's emotions, thereby providing a more personalized experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0073] During generation, the generation unit can add interactive elements based on gaze movement using the user's gaze tracking data. Examples of gaze tracking data include, but are not limited to, gaze tracking sensors and data analysis algorithms. For example, when a user gazes at a specific exhibit, the generation unit can display detailed information about the exhibit. For example, when a user gazes at a specific exhibit, the generation unit can display detailed information about the exhibit. Furthermore, when a user gazes at a specific area, the generation unit can add interactive elements related to the area. For example, when a user gazes at a specific area, the generation unit can add interactive elements related to the area. Furthermore, the generation unit can automatically select the next content to display based on the user's gaze movement. For example, the generation unit can automatically select the next content to display based on the user's gaze movement. Thus, by using the gaze tracking data, interactive elements based on the user's gaze movement can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the gaze tracking data to a generation AI and cause the generation AI to add interactive elements.

[0074] During generation, the generation unit may add a function to display detailed information about specific facilities or exhibits in the center as a pop-up. Examples of detailed information about specific facilities or exhibits include, but are not limited to, technical specifications, historical background, and usage methods. For example, when a user approaches a specific exhibit, the generation unit may display detailed information about the exhibit as a pop-up. For example, when a user approaches a specific exhibit, the generation unit may display detailed information about the exhibit as a pop-up. Furthermore, when a user approaches a specific piece of equipment, the generation unit may display detailed information about the equipment as a pop-up. For example, when a user approaches a specific piece of equipment, the generation unit may display detailed information about the equipment as a pop-up. Furthermore, when a user approaches a specific area, the generation unit may display detailed information about the area as a pop-up. For example, when a user approaches a specific area, the generation unit may display detailed information about the area as a pop-up. This allows the user to easily obtain detailed information by displaying detailed information about specific facilities or exhibits as a pop-up. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information about a particular piece of equipment or exhibit into the generation AI and have the generation AI generate a pop-up display.

[0075] During generation, the generation unit can add interactive elements that allow the user to not only move freely within the center but also perform specific actions. Specific actions include, but are not limited to, object manipulation and movement within the virtual environment. For example, when a user approaches a specific exhibit, the generation unit can display an interactive quiz about the exhibit. For example, when a user approaches a specific exhibit, the generation unit can display an interactive quiz about the exhibit. Furthermore, when a user approaches a specific area, the generation unit can provide an interactive guided tour about the area. For example, when a user approaches a specific area, the generation unit can provide an interactive guided tour about the area. Furthermore, when a user approaches a specific piece of equipment, the generation unit can provide an interactive element that allows the user to operate the equipment. For example, when a user approaches a specific piece of equipment, the generation unit can provide an interactive element that allows the user to operate the equipment. This allows the user to not only move freely within the center but also add interactive elements that allow the user to perform specific actions, thereby providing a more immersive experience. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input data about a particular action into the generation AI and cause the generation AI to generate an interactive element.

[0076] The generation unit can estimate the user's emotions and adjust the color tone and sound of the VR content based on the estimated user's emotions. The color tone and sound of the VR content can include, but are not limited to, color temperature, volume, and sound quality. For example, if the user is relaxed, the generation unit can provide calm color tones and gentle sound. For example, if the user is relaxed, the generation unit can provide calm color tones and gentle sound. Furthermore, if the user is excited, the generation unit can provide vivid color tones and vibrant sound. For example, if the user is excited, the generation unit can provide vivid color tones and vibrant sound. Furthermore, if the user is nervous, the generation unit can provide color tones and sound that give a sense of security. This allows for a more personalized experience by adjusting the color tone and sound of the VR content based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and have the generation AI adjust the color tone and sound.

[0077] The generation unit can add narration and subtitles in multiple languages ​​during generation to accommodate international users. Narrations and subtitles in multiple languages ​​include, but are not limited to, English, Japanese, and Spanish. The generation unit can provide narration in multiple languages, such as English, Spanish, and Chinese. The generation unit can also automatically display subtitles based on a language selected by a user. For example, the generation unit can automatically display subtitles based on a language selected by a user. Furthermore, the generation unit can provide a language switching function when a user speaks multiple languages. For example, when a user speaks multiple languages, the generation unit can provide a language switching function. By adding narration and subtitles in multiple languages, international users can be accommodated. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the content of the narration and subtitles into a generation AI and have the generation AI perform speech synthesis and subtitle generation.

[0078] During generation, the generation unit can add quizzes or mini-games related to specific areas within the center to enhance the educational element. Quizzes and mini-games include, but are not limited to, question formats, game rules, and interfaces. For example, the generation unit can provide a quiz related to the science experiment area to allow users to enjoy themselves while learning. For example, the generation unit can provide a quiz related to the science experiment area to allow users to enjoy themselves while learning. The generation unit can also provide a mini-game related to the historical exhibit area to allow users to enjoy themselves while learning. For example, the generation unit can provide a mini-game related to the historical exhibit area to allow users to enjoy themselves while learning. Furthermore, the generation unit can provide a quiz or mini-game related to the interactive exhibit area to allow users to enjoy themselves while learning. For example, the generation unit can provide a quiz or mini-game related to the interactive exhibit area to allow users to enjoy themselves while learning. By adding quizzes or mini-games, the educational element can be enhanced and the user can enjoy themselves while learning. Some or all of the above-described processing by the generation unit may be performed using, or without, AI. For example, the generation unit can input the content of the quiz or mini-game into a generation AI and cause the generation AI to generate the quiz or mini-game.

[0079] During generation, the generation unit can add social functions that allow users to communicate with other users in real time. Examples of social functions include, but are not limited to, a chat function, a voice call, and a video call. The generation unit, for example, provides a function that allows users to chat with other users. For example, the generation unit can provide a function that allows users to chat with other users. The generation unit can also provide a function that allows users to make voice calls with other users. For example, the generation unit can provide a function that allows users to make video calls with other users. By adding the social functions, users can communicate with other users in real time. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the social functions into a generation AI and cause the generation AI to generate the social functions.

[0080] The distribution unit can estimate the user's emotions and adjust the order of content to be distributed based on the estimated user emotions. The order of content to be distributed can be based on, for example, the user's level of interest and patterns of emotional change, but is not limited to these examples. For example, if the user is relaxed, the distribution unit can first distribute content from a quiet area. For example, if the user is relaxed, the distribution unit can first distribute content from a quiet area. Furthermore, if the user is excited, the distribution unit can first distribute content from an interactive area. For example, if the user is excited, the distribution unit can first distribute content from an interactive area. Furthermore, if the user is curious, the distribution unit can first distribute content from particularly interesting exhibits. For example, if the user is curious, the distribution unit can first distribute content from particularly interesting exhibits. This allows for adjusting the order of content to be distributed based on the user's emotions, providing a more personalized experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input user emotion data into the generation AI and have the generation AI adjust the order of content.

[0081] The distribution unit may add a function to automatically adjust the image quality of the VR content according to the user's Internet connection speed during distribution. Internet connection speeds include, but are not limited to, Mbps and connection stability. For example, when the user's Internet connection speed is slow, the distribution unit may lower the image quality to provide smooth distribution. For example, when the user's Internet connection speed is slow, the distribution unit may lower the image quality to provide smooth distribution. Furthermore, when the user's Internet connection speed is fast, the distribution unit may increase the image quality to provide high-quality distribution. For example, when the user's Internet connection speed is fast, the distribution unit may increase the image quality to provide high-quality distribution. Furthermore, when the user's Internet connection speed fluctuates, the distribution unit may adjust the image quality in real time to provide smooth distribution. For example, when the user's Internet connection speed fluctuates, the image quality may be adjusted in real time to provide smooth distribution. This automatically adjusting the image quality according to the Internet connection speed enables smooth distribution. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the user's internet connection speed into the generation AI and have the generation AI adjust the image quality.

[0082] The distribution unit may add a function to analyze a user's viewing history and recommend related VR content during distribution. Examples of methods for analyzing viewing history include, but are not limited to, data mining and machine learning algorithms. The distribution unit may recommend related VR content based on content previously viewed by the user. For example, related VR content can be recommended based on content previously viewed by the user. The distribution unit may also recommend new VR content that may be of interest to the user based on the user's viewing history. For example, new VR content that may be of interest to the user based on the user's viewing history can be recommended. Furthermore, the distribution unit may recommend related, highly rated VR content based on ratings of content previously viewed by the user. For example, related, highly rated VR content can be recommended based on ratings of content previously viewed by the user. This allows content that meets the user's interests to be provided by analyzing the viewing history and recommending related VR content. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without AI. For example, the distribution unit may input the user's viewing history data into a generation AI and cause the generation AI to recommend content.

[0083] During distribution, the distribution unit can add an interface that allows the user to provide feedback in real time during the VR experience. Examples of methods for providing feedback include, but are not limited to, a feedback form, voice input, and gesture recognition. The distribution unit, for example, provides an interface that allows the user to input comments in real time during the VR experience. For example, the distribution unit can provide an interface that allows the user to input comments in real time during the VR experience. The distribution unit can also provide an interface that allows the user to make ratings in real time during the VR experience. For example, the distribution unit can provide an interface that allows the user to make ratings in real time during the VR experience. Furthermore, the distribution unit can also provide an interface that allows the user to post questions in real time during the VR experience. For example, the distribution unit can provide an interface that allows the user to post questions in real time during the VR experience. By adding an interface that allows feedback to be provided in real time, the user's opinions can be reflected immediately. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI. For example, the distribution unit may input user feedback data into a generation AI and cause the generation AI to analyze the feedback.

[0084] The delivery unit can estimate the user's emotions and adjust the theme of the content to be delivered based on the estimated user emotions. The theme of the content to be delivered includes, but is not limited to, the user's interests and emotional change patterns, for example. For example, when the user is relaxed, the delivery unit delivers content with a relaxing theme. For example, when the user is relaxed, the delivery unit can deliver content with a relaxing theme. Furthermore, when the user is excited, the delivery unit can deliver content with a stimulating theme. For example, when the user is excited, the delivery unit can deliver content with a stimulating theme. Furthermore, when the user is curious, the delivery unit can deliver content with an educational theme. For example, when the user is curious, the delivery unit can deliver content with an educational theme. By adjusting the theme of the content based on the user's emotions, a more personalized experience can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input user emotion data into the generation AI and have the generation AI adjust the theme of the content.

[0085] The distribution unit may add a function that allows a user to record a VR experience and play it back later during distribution. Recording a VR experience may involve, but is not limited to, recording software, storage types, and the like. The distribution unit may provide, for example, a function that allows a user to record a VR experience and play it back later to re-experience it. For example, a function that allows a user to record a VR experience and play it back later to re-experience it. The distribution unit may also provide a function that allows a user to record a specific scene and play it back later to check the details. For example, a function that allows a user to record a specific scene and play it back later to check the details. The distribution unit may also provide a function that allows a user to record a VR experience and share it with other users. For example, a function that allows a user to record a VR experience and share it with other users may be provided. By adding the function that allows a user to record a VR experience and play it back later, the user can look back on the experience. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without AI. For example, the distribution unit may input recording data to a generation AI and have the generation AI analyze the recording.

[0086] The distribution unit may add a social sharing function during distribution that allows a user to share a VR experience with other users. The social sharing function may include, but is not limited to, a sharing platform and a sharing method. For example, the distribution unit may provide a function that allows a user to share a VR experience on social media. For example, the distribution unit may provide a function that allows a user to share a VR experience with friends in real time. For example, the distribution unit may provide a function that allows a user to share a VR experience with friends in real time. Furthermore, the distribution unit may provide a function that allows a user to record a VR experience and share it later with other users. For example, the distribution unit may provide a function that allows a user to record a VR experience and share it later with other users. By adding the social sharing function, the user can share the experience with other users. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input social sharing data into a generation AI and cause the generation AI to generate a sharing function.

[0087] The distribution unit may add a function that allows a user to request detailed information about a specific area or exhibit during the VR experience during distribution. Requesting detailed information may be performed, for example, by voice input, text input, gesture recognition, or the like, but is not limited to these examples. The distribution unit may provide, for example, a function that allows a user to request detailed information about a specific exhibit. The distribution unit may also provide a function that allows a user to request detailed information about a specific area. The distribution unit may also provide a function that allows a user to request detailed information about a specific facility. By adding a function that allows a user to request detailed information about a specific area or exhibit, the user can easily obtain information of interest. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or without AI. For example, the distribution unit may input the user's request data into a generation AI and cause the generation AI to provide detailed information. === Hard Collateral 1-1 === Each of the multiple elements including the above-described photographing unit, generation unit, and distribution unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit can photograph the interior of the center using the camera 42 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the photographed video as VR content using a generation AI. The distribution unit distributes the generated VR content over the Internet using the communication I / F 26 of the data processing device 12. A user can experience the VR content using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described image capturing unit, generation unit, and distribution unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the image capturing unit can capture images of the interior of the center using the camera 42 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the captured images as VR content using a generation AI. The distribution unit distributes the generated VR content over the Internet using the communication I / F 26 of the data processing device 12. The user can experience the VR content using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned photographing unit, generation unit, and distribution unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the photographing unit can photograph the interior of the center using the camera 42 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the photographed video as VR content using a generation AI. The distribution unit distributes the generated VR content over the Internet using the communication I / F 26 of the data processing device 12. The user can experience the VR content using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned photographing unit, generation unit, and distribution unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit can photograph the inside of the center using the camera 42 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates the photographed video as VR content using a generation AI. The distribution unit distributes the generated VR content via the Internet using the communication I / F 26 of the data processing device 12. The user can experience the VR content using the speaker 240 of the robot 414.

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

[0089] The camera unit can estimate the user's emotions in a specific area of ​​the center and adjust the angle and distance of the shot based on the estimated emotion. For example, if the user is excited, a wide-angle lens can be used to capture a wide area, enhancing the sense of realism. Alternatively, if the user is relaxed, a telephoto lens can be used to focus on a specific exhibit and provide detailed footage. Furthermore, if the user is curious, a macro lens can be used to capture fine details and provide interesting footage. This allows the camera to adjust the angle and distance of the shot based on the user's emotions, providing a more personalized VR experience.

[0090] The generation unit can estimate the user's emotions during generation and adjust navigation of the VR content based on the estimated emotions. For example, if the user is relaxed, navigation can be provided at a slow pace and include detailed explanations. Alternatively, if the user is excited, navigation can be provided at a fast pace and include many interactive elements. Furthermore, if the user is curious, navigation can be provided that allows free exploration and allows easy access to areas of interest. This makes it possible to provide a more personalized experience by adjusting navigation based on the user's emotions.

[0091] The distribution unit can estimate the user's emotions and adjust the volume and sound quality of the content to be distributed based on the estimated emotions. For example, if the user is relaxed, a relaxing environment can be provided by lowering the volume and softening the sound quality. Also, if the user is excited, the volume can be increased and the sound quality can be made clearer, enhancing the sense of realism. Furthermore, if the user is nervous, a sense of security can be imparted by lowering the volume and stabilizing the sound quality. In this way, a more personalized experience can be provided by adjusting the volume and sound quality based on the user's emotions.

[0092] The camera unit can estimate the user's emotions in a specific area within the center and adjust the time period for filming based on the estimated emotions. For example, if the user is relaxed, filming can be performed during a quiet time period to provide relaxing footage. If the user is excited, filming can be performed during a lively time period to provide immersive footage. Furthermore, if the user is curious, filming can be performed during a time period when a specific event is taking place to provide interesting footage. This allows for a more personalized VR experience by adjusting the filming time period based on the user's emotions.

[0093] The generation unit can estimate the user's emotions during generation and adjust the color tones and sounds of the VR content based on the estimated emotions. For example, if the user is relaxed, calm color tones and gentle sounds can be provided. If the user is excited, vivid color tones and vibrant sounds can be provided. Furthermore, if the user is tense, color tones and sounds that give a sense of security can be provided. This allows for a more personalized experience by adjusting the color tones and sounds of the VR content based on the user's emotions.

[0094] The camera unit can use the user's gaze tracking data to add interactive elements in specific areas of the center in response to gaze movements. For example, if a user looks at a specific exhibit, detailed information about that exhibit can be displayed. Also, if a user looks at a specific area, interactive elements related to that area can be added. Furthermore, the next content to be displayed can be automatically selected in response to the user's gaze movements. In this way, by using the gaze tracking data, interactive elements can be provided in response to the user's gaze movements.

[0095] During generation, the generation unit can add a function to pop up detailed information about specific equipment or exhibits in the center. For example, when a user approaches a specific exhibit, detailed information about the exhibit can be pop up. Also, when a user approaches specific equipment, detailed information about the equipment can be pop up. Furthermore, when a user approaches a specific area, detailed information about the area can be pop up. In this way, by popping up detailed information about specific equipment or exhibits, the user can easily obtain detailed information.

[0096] During generation, the generation unit can add interactive elements that not only allow the user to move freely within the center but also allow the user to perform specific actions. For example, when the user approaches a specific exhibit, an interactive quiz about the exhibit can be displayed. Also, when the user approaches a specific area, an interactive guided tour about the area can be provided. Furthermore, when the user approaches specific equipment, an interactive element that allows the user to operate the equipment can be provided. In this way, a more immersive experience can be provided by adding interactive elements that not only allow the user to move freely within the center but also allow the user to perform specific actions.

[0097] The distribution unit can add a function to analyze the user's viewing history and recommend related VR content during distribution. For example, it can recommend related VR content based on content the user has viewed in the past. It can also recommend new VR content that the user may be interested in based on the user's viewing history. Furthermore, it can recommend related, highly rated VR content based on the ratings of content the user has viewed. This makes it possible to provide content that matches the user's interests by analyzing the viewing history and recommending related VR content.

[0098] During distribution, the distribution unit can add an interface that allows the user to provide feedback in real time during the VR experience. For example, an interface can be provided that allows the user to input comments in real time during the VR experience. An interface can also be provided that allows the user to provide ratings in real time during the VR experience. Furthermore, an interface can be provided that allows the user to post questions in real time during the VR experience. By adding an interface that allows feedback to be provided in real time, user opinions can be reflected immediately.

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

[0100] Step 1: The photography team photographs the interior of the center, which includes a data center, research center, and exhibition center. The photography team uses high-resolution cameras to capture precise images of each area within the center. They can also use 360-degree cameras to capture a panoramic view of the center. They can also use drones to capture high places and narrow spaces within the center. Step 2: The generation unit uses a generation AI to generate VR content from the footage captured by the capture unit. The generation AI processes the footage using technologies such as deep learning and GAN (generative artificial network). The generation unit seamlessly connects the captured footage to generate VR content that users can move freely through. It can also adjust the color tone and brightness of the footage to provide a realistic visual effect. It can also add interactive elements to the footage, allowing users to freely explore the center. Step 3: The distribution unit distributes the VR content generated by the generation unit via the Internet. The distribution unit distributes the VR content in real time using streaming technology. It can also use download technology to allow users to download the VR content in advance and experience it. Furthermore, it has a function to automatically adjust the image quality of the VR content according to the user's Internet connection speed.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0115] In the 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.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 filming department takes pictures of the inside of the center, a generation unit that generates VR content from the video captured by the imaging unit; a distribution unit that distributes the VR content generated by the generation unit via the Internet; Equipped with A system characterized by:

2. The imaging unit is High-resolution cameras are used to capture precise images of each area within the center.

2. The system of claim 1.

3. The generation unit Using generative AI to process images and provide a realistic VR experience 2. The system of claim 1.

4. The distribution unit Allows users to access VR content over the internet using a VR device 2. The system of claim 1.

5. The imaging unit is Estimate the user's emotions and prioritize areas to photograph based on the estimated user emotions.

2. The system of claim 1.

6. The imaging unit is During filming, environmental data such as temperature and humidity inside the center is simultaneously acquired and reflected in the VR experience.

2. The system of claim 1.

7. The imaging unit is During filming, audio data from within the center is recorded with high precision to provide a realistic audio experience.

2. The system of claim 1.

8. The imaging unit is During filming, dynamic events within the center are captured in real time and incorporated into the VR content.

2. The system of claim 1.

Images